Keysight Technologies Spectrum Analysis Basics Application Note 150 Ihr Spezialist für

Keysight Technologies Spectrum Analysis Basics Application Note 150 Ihr Spezialist für
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Keysight Technologies
Spectrum Analysis Basics
Application Note 150
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2 | Keysight | Spectrum Analysis Basics – Application Note 150
Keysight Technologies. Inc. dedicates this application note to Blake Peterson.
Blake’s outstanding service in technical support reached customers in all corners of
the world during and after his 45-year career with Hewlett-Packard and Keysight. For
many years, Blake trained new marketing and sales engineers in the “ABCs” of spectrum
analyzer technology, which provided the basis for understanding more advanced
technology. He is warmly regarded as a mentor and technical contributor in spectrum
Blake’s many accomplishments include:
– Authored the original edition of the Spectrum Analysis Basics application note and
contributed to subsequent editions
– Helped launch the 8566/68 spectrum analyzers, marking the beginning of
modern spectrum analysis, and the PSA Series spectrum analyzers that set new
performance benchmarks in the industry when they were introduced
– Inspired the creation of Blake Peterson University––required training for all
engineering hires at Keysight
As a testament to his accomplishments and contributions, Blake was honored with
Microwaves & RF magazine’s first Living Legend Award in 2013.
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3 | Keysight | Spectrum Analysis Basics – Application Note 150
Table of Contents
Chapter 1 – Introduction ..................................................................................................................................5
Frequency domain versus time domain .........................................................................................................5
What is a spectrum? ........................................................................................................................................6
Why measure spectra? ....................................................................................................................................6
Types of signal analyzers .................................................................................................................................8
Chapter 2 – Spectrum Analyzer Fundamentals ............................................................................................9
RF attenuator ...................................................................................................................................................10
Low-pass filter or preselector........................................................................................................................10
Tuning the analyzer ........................................................................................................................................11
IF gain ...............................................................................................................................................................12
Resolving signals.............................................................................................................................................13
Residual FM .....................................................................................................................................................15
Phase noise......................................................................................................................................................16
Sweep time ......................................................................................................................................................18
Envelope detector ..........................................................................................................................................20
Displays ............................................................................................................................................................21
Detector types .................................................................................................................................................22
Sample detection ............................................................................................................................................23
Peak (positive) detection ................................................................................................................................24
Negative peak detection ................................................................................................................................24
Normal detection ............................................................................................................................................24
Average detection ...........................................................................................................................................27
EMI detectors: average and quasi-peak detection .....................................................................................27
Averaging processes.......................................................................................................................................28
Time gating ......................................................................................................................................................31
Chapter 3 – Digital IF Overview ....................................................................................................................36
Digital filters .....................................................................................................................................................36
All-digital IF......................................................................................................................................................37
Custom digital signal processing ..................................................................................................................38
Additional video processing features ...........................................................................................................38
Frequency counting .......................................................................................................................................38
More advantages of all-digital IF...................................................................................................................39
Chapter 4 – Amplitude and Frequency Accuracy........................................................................................40
Relative uncertainty .......................................................................................................................................42
Absolute amplitude accuracy ........................................................................................................................42
Improving overall uncertainty ........................................................................................................................43
Specifications, typical performance and nominal values ...........................................................................43
Digital IF architecture and uncertainties ......................................................................................................43
Amplitude uncertainty examples...................................................................................................................44
Frequency accuracy ........................................................................................................................................44
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4 | Keysight | Spectrum Analysis Basics – Application Note 150
Table of Contents
Chapter 5 – Sensitivity and Noise ................................................................................ 46
Sensitivity....................................................................................................................... 46
Noise floor extension .................................................................................................... 48
Noise figure.................................................................................................................... 49
Preamplifiers .................................................................................................................. 50
Noise as a signal ........................................................................................................... 53
Preamplifier for noise measurements .......................................................................... 54
Chapter 6 – Dynamic Range ........................................................................................ 55
Dynamic range versus internal distortion ................................................................... 55
Attenuator test .............................................................................................................. 56
Noise .............................................................................................................................. 57
Dynamic range versus measurement uncertainty ...................................................... 58
Gain compression.......................................................................................................... 60
Display range and measurement range ...................................................................... 60
Adjacent channel power measurements ..................................................................... 61
Chapter 7 – Extending the Frequency Range ............................................................. 62
Internal harmonic mixing .............................................................................................. 62
Preselection ................................................................................................................... 66
Amplitude calibration.................................................................................................... 68
Phase noise .................................................................................................................. 68
Improved dynamic range .............................................................................................. 69
Pluses and minuses of preselection ............................................................................. 70
External harmonic mixing ............................................................................................. 71
Signal identification ...................................................................................................... 73
Chapter 8 – Modern Signal Analyzers ......................................................................... 76
Application-specific measurements............................................................................. 76
The need for phase information .................................................................................. 77
Digital modulation analysis .......................................................................................... 79
Real-time spectrum analysis ........................................................................................ 80
Chapter 9 – Control and Data Transfer........................................................................ 81
Saving and printing data .............................................................................................. 81
Data transfer and remote instrument control . ........................................................... 81
Firmware updates ......................................................................................................... 82
Calibration, troubleshooting, diagnostics and repair.................................................. 82
Summary ....................................................................................................................... 82
Glossary of Terms .......................................................................................................... 83
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5 | Keysight | Spectrum Analysis Basics – Application Note 150
Chapter 1. Introduction
This application note explains the
fundamentals of swept-tuned,
superheterodyne spectrum analyzers and
discusses the latest advances in spectrum
analyzer capabilities.
At the most basic level, a spectrum
analyzer can be described as a frequencyselective, peak-responding voltmeter
calibrated to display the rms value of a
sine wave. It is important to understand
that the spectrum analyzer is not a power
meter, even though it can be used to
display power directly. As long as we know
some value of a sine wave (for example,
peak or average) and know the resistance
across which we measure this value, we can
calibrate our voltmeter to indicate power.
With the advent of digital technology,
modern spectrum analyzers have been
given many more capabilities. In this note,
we describe the basic spectrum analyzer
as well as additional capabilities made
possible using digital technology and digital
signal processing.
Fourier1 theory tells us any time-domain
electrical phenomenon is made up of
one or more sine waves of appropriate
frequency, amplitude, and phase.
In other words, we can transform a
time-domain signal into its frequencydomain equivalent. Measurements in the
frequency domain tell us how much energy
is present at each particular frequency.
With proper filtering, a waveform such
as the one shown in Figure 1-1 can be
decomposed into separate sinusoidal
waves, or spectral components, which we
can then evaluate independently. Each
sine wave is characterized by its amplitude
and phase. If the signal we wish to analyze
is periodic, as in our case here, Fourier
says that the constituent sine waves are
separated in the frequency domain by 1/T,
where T is the period of the signal 2.
Some measurements require that we
preserve complete information about
the signal frequency, amplitude and
phase. However, another large group
of measurements can be made without
knowing the phase relationships among
the sinusoidal components. This type of
signal analysis is called spectrum analysis.
Because spectrum analysis is simpler
to understand, yet extremely useful, we
begin by looking first at how spectrum
analyzers perform spectrum analysis
measurements, starting in Chapter 2.
Theoretically, to make the transformation
from the time domain to the frequency
domain, the signal must be evaluated over
all time, that is, over infinity. However, in
practice, we always use a finite time period
when making a measurement.
Frequency domain versus
time domain
Before we get into the details of
describing a spectrum analyzer, we
might first ask ourselves: “Just what
is a spectrum and why would we want
to analyze it?” Our normal frame of
reference is time. We note when certain
events occur. This includes electrical
events. We can use an oscilloscope
to view the instantaneous value of a
particular electrical event (or some
other event converted to volts through
an appropriate transducer) as a function
of time. In other words, we use the
oscilloscope to view the waveform of a
signal in the time domain.
Figure 1-1. Complex time-domain signal
Jean Baptiste Joseph Fourier, 1768-1830. A French mathematician and physicist who discovered that periodic functions can be expanded into a series of
sines and cosines.
If the time signal occurs only once, then T is infinite, and the frequency representation is a continuum of sine waves.
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6 | Keysight | Spectrum Analysis Basics – Application Note 150
You also can make Fourier transformations
from the frequency to the time domain.
This case also theoretically requires the
evaluation of all spectral components over
frequencies to infinity. In reality, making
measurements in a finite bandwidth
that captures most of the signal energy
produces acceptable results. When you
perform a Fourier transformation on
frequency domain data, the phase of the
individual components is indeed critical.
For example, a square wave transformed
to the frequency domain and back again
could turn into a sawtooth wave if you do
not preserve phase.
What is a spectrum?
So what is a spectrum in the context of
this discussion? A spectrum is a collection
of sine waves that, when combined
properly, produce the time-domain signal
under examination. Figure 1-1 shows the
waveform of a complex signal. Suppose
that we were hoping to see a sine wave.
Although the waveform certainly shows
us that the signal is not a pure sinusoid, it
does not give us a definitive indication of
the reason why.
Figure 1-2 shows our complex signal in
both the time and frequency domains.
The frequency-domain display plots the
amplitude versus the frequency of each
sine wave in the spectrum. As shown, the
spectrum in this case comprises just two
sine waves. We now know why our original
waveform was not a pure sine wave. It
contained a second sine wave, the second
harmonic in this case. Does this mean we
have no need to perform time-domain
measurements? Not at all. The time
domain is better for many measurements,
and some can be made only in the time
domain. For example, pure time-domain
measurements include pulse rise and fall
times, overshoot and ringing.
People involved in wireless communications
are extremely interested in out-of-band
and spurious emissions. For example,
cellular radio systems must be checked for
harmonics of the carrier signal that might
interfere with other systems operating at
the same frequencies as the harmonics.
Engineers and technicians are also very
concerned about distortion of the message
modulated onto a carrier.
Why measure spectra?
Spectrum monitoring is another important
frequency-domain measurement activity.
Government regulatory agencies allocate
different frequencies for various radio
services, such as broadcast television and
radio, mobile phone systems, police and
emergency communications, and a host of
other applications. It is critical that each
of these services operates at the assigned
frequency and stays within the allocated
channel bandwidth. Transmitters and
other intentional radiators often must
operate at closely spaced adjacent
frequencies. A key performance measure
for the power amplifiers and other
components used in these systems is
the amount of signal energy that spills
over into adjacent channels and causes
The frequency domain also has its
measurement strengths. We have already
seen in Figures 1-1 and 1-2 that the
frequency domain is better for determining
the harmonic content of a signal.
Frequency domain
Time domain
Figure 1-2. Relationship between time and frequency domain
Third-order intermodulation (two tones of
a complex signal modulating each other)
can be particularly troublesome because
the distortion components can fall within
the band of interest, which means they
cannot be filtered away.
Electromagnetic interference (EMI) is
a term applied to unwanted emissions
from both intentional and unintentional
radiators. These unwanted emissions,
either radiated or conducted (through
the power lines or other interconnecting
wires), might impair the operation of
other systems. Almost anyone designing
or manufacturing electrical or electronic
products must test for emission levels
versus frequency according to regulations
set by various government agencies or
industry-standard bodies.
Figure 1-2.
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7 | Keysight | Spectrum Analysis Basics – Application Note 150
Figure 1-3. Harmonic distortion test of a transmitter
Figure 1-4. GSM radio signal and spectral mask showing limits of
unwanted emissions
Figure 1- 5. Two-tone test on an RF power amplifier
Figure 1-6. Radiated emissions plotted against CISPR11 limits as part of an
EMI test
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8 | Keysight | Spectrum Analysis Basics – Application Note 150
Noise is often the signal you want to
measure. Any active circuit or device
will generate excess noise. Tests such
as noise figure and signal-to-noise ratio
(SNR) are important for characterizing
the performance of a device and
its contribution to overall system
Figures 1-3 through 1-6 show some of
these measurements on an X-Series
signal analyzer.
Types of signal analyzers
The first swept-tuned superheterodyne
analyzers measured only amplitude.
However, as technology advanced
and communication systems grew
more complex, phase became a more
important part of the measurement.
Spectrum analyzers, now often labeled
signal analyzers, have kept pace. By
digitizing the signal, after one or more
stages of frequency conversion, phase as
well as amplitude is preserved and can
be included as part of the information
displayed. So today’s signal analyzers
such as the Keysight X-Series combine the
attributes of analog, vector and FFT (fast
Fourier transform) analyzers. To further
improve capabilities, Keysight’s X-Series
signal analyzers incorporate a computer,
complete with a removable disk drive
that allows sensitive data to remain in a
controlled area should the analyzer be
Advanced technology also has allowed
circuits to be miniaturized. As a result,
rugged portable spectrum analyzers such
as the Keysight FieldFox simplify tasks such
as characterizing sites for transmitters
or antenna farms. Zero warm-up time
eliminates delays in situations involving
brief stops for quick measurements. Due
to advanced calibration techniques, field
measurements made with these handheld
analyzers correlate with lab-grade benchtop spectrum analyzers within 10ths of a dB.
More information
For additional information on vector
measurements, see Vector Signal
Analysis Basics–Application Note, literature
number 5989-1121EN. For information
on FFT analyzers that tune to 0 Hz,
see the Web page for the Keysight
In this application note, we concentrate
on swept amplitude measurements,
only briefly touching on measurements
involving phase–see Chapter 8.
Note: When computers became HewlettPackard’s dominant business, it created
and spun off Keysight Technologies in
the late 1990’s to continue the test and
measurement business. Many older
spectrum analyzers carry the HewlettPackard name but are supported by
This application note will give you insight
into your particular spectrum or signal
analyzer and help you use this versatile
instrument to its maximum potential.
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9 | Keysight | Spectrum Analysis Basics – Application Note 150
Chapter 2. Spectrum Analyzer Fundamentals
This chapter focuses on the fundamental
theory of how a spectrum analyzer
works. While today’s technology makes it
possible to replace many analog circuits
with modern digital implementations, it
is useful to understand classic spectrum
analyzer architecture as a starting point in
our discussion.
In later chapters, we will look at the
capabilities and advantages that digital
circuitry brings to spectrum analysis.
Chapter 3 discusses digital architectures
used in spectrum analyzers available
Figure 2-1 is a simplified block diagram
of a superheterodyne spectrum analyzer.
Heterodyne means to mix; that is, to
translate frequency. And super refers to
superaudio frequencies, or frequencies
above the audio range. In the Figure
2-1 block diagram, we see that an input
signal passes through an attenuator,
then through a low-pass filter (later we
will see why the filter is here) to a mixer,
where it mixes with a signal from the local
oscillator (LO).
RF input
Because the mixer is a non-linear device,
its output includes not only the two
original signals, but also their harmonics
and the sums and differences of the
original frequencies and their harmonics.
If any of the mixed signals falls within the
pass band of the intermediate-frequency
(IF) filter, it is further processed (amplified
and perhaps compressed on a logarithmic
scale). It is essentially rectified by the
envelope detector, filtered through
the low-pass filter and displayed. A
ramp generator creates the horizontal
movement across the display from left to
right. The ramp also tunes the LO so its
frequency change is in proportion to the
ramp voltage.
If you are familiar with superheterodyne
AM radios, the type that receive ordinary
AM broadcast signals, you will note a
strong similarity between them and
the block diagram shown in Figure 2-1.
The differences are that the output of a
spectrum analyzer is a display instead of
a speaker, and the local oscillator is tuned
electronically rather than by a front-panel
IF gain
IF filter
The output of a spectrum analyzer is an
X-Y trace on a display, so let’s see what
information we get from it. The display
is mapped on a grid (graticule) with 10
major horizontal divisions and generally
10 major vertical divisions. The horizontal
axis is linearly calibrated in frequency that
increases from left to right. Setting the
frequency is a two-step process. First we
adjust the frequency at the centerline of
the graticule with the center frequency
control. Then we adjust the frequency
range (span) across the full 10 divisions
with the frequency span control. These
controls are independent, so if we change
the center frequency, we do not alter the
frequency span. Alternatively, we can set
the start and stop frequencies instead
of setting center frequency and span.
In either case, we can determine the
absolute frequency of any signal displayed
and the relative frequency difference
between any two signals.
Pre-selector, or
low-pass filter
Figure 2-1. Block diagram of a classic superheterodyne spectrum analyzer
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10 | Keysight | Spectrum Analysis Basics – Application Note 150
The vertical axis is calibrated in amplitude.
You can choose a linear scale calibrated
in volts or a logarithmic scale calibrated
in dB. The log scale is used far more
often than the linear scale because it
has a much wider usable range. The
log scale allows signals as far apart in
amplitude as 70 to 100 dB (voltage ratios
of 3200 to 100,000 and power ratios of
10,000,000 to 10,000,000,000) to be
displayed simultaneously. On the other
hand, the linear scale is usable for signals
differing by no more than 20 to 30 dB
(voltage ratios of 10 to 32). In either case,
we give the top line of the graticule, the
reference level, an absolute value through
calibration techniques1 and use the
scaling per division to assign values to
other locations on the graticule. Therefore,
we can measure either the absolute
value of a signal or the relative amplitude
difference between any two signals.
The blocking capacitor is used to prevent
the analyzer from being damaged by a DC
signal or a DC offset of the signal being
viewed. Unfortunately, it also attenuates
low-frequency signals and increases the
minimum useable start frequency of the
analyzer to 9 kHz, 100 kHz or 10 MHz,
depending on the analyzer.
In some analyzers, an amplitude reference
signal can be connected as shown in
Figure 2-3. It provides a precise frequency
and amplitude signal, used by the analyzer
to periodically self-calibrate.
Low-pass filter or preselector
The low-pass filter blocks high-frequency
signals from reaching the mixer. This
filtering prevents out-of-band signals
from mixing with the local oscillator and
creating unwanted responses on the
display. Microwave spectrum analyzers
replace the low-pass filter with a
preselector, which is a tunable filter that
rejects all frequencies except those we
currently wish to view. In Chapter 7, we go
into more detail about the operation and
purpose of the preselector.
Scale calibration, both frequency and
amplitude, is shown by annotations
written onto the display. Figure 2-2 shows
the display of a typical analyzer.
Now, let’s turn our attention back to the
spectrum analyzer components diagramed
in Figure 2-1.
RF attenuator
The first part of our analyzer is the
RF input attenuator. Its purpose is to
ensure the signal enters the mixer at the
optimum level to prevent overload, gain
compression and distortion. Because
attenuation is a protective circuit for the
analyzer, it is usually set automatically,
based on the reference level. However,
manual selection of attenuation is also
available in steps of 10, 5, 2, or even 1 dB.
The diagram in Figure 2-3 is an example
of an attenuator circuit with a maximum
attenuation of 70 dB in increments of 2 dB.
Figure 2-2. Typical spectrum analyzer display with control settings
0 to 70 dB, 2 dB steps
RF input
Figure 2-3. RF input attenuator circuitry
figure 2-3
1. See Chapter 4, “Amplitude and Frequency Accuracy.”
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11 | Keysight | Spectrum Analysis Basics – Application Note 150
Tuning the analyzer
We need to know how to tune our
spectrum analyzer to the desired
frequency range. Tuning is a function of
the center frequency of the IF filter, the
frequency range of the LO and the range
of frequencies allowed to reach the mixer
from the outside world (allowed to pass
through the low-pass filter). Of all the
mixing products emerging from the mixer,
the two with the greatest amplitudes,
and therefore the most desirable, are
those created from the sum of the LO
and input signal and from the difference
between the LO and input signal. If we can
arrange things so that the signal we wish
to examine is either above or below the LO
frequency by the IF, then only one of the
desired mixing products will fall within the
pass-band of the IF filter and be detected
to create an amplitude response on the
We need to pick an LO frequency and an
IF that will create an analyzer with the
desired tuning range. Let’s assume that
we want a tuning range from 0 to 3.6
GHz. We then need to choose the IF. Let’s
try a 1-GHz IF. Because this frequency
is within our desired tuning range, we
could have an input signal at 1 GHz.
The output of a mixer also includes the
original input signals, so an input signal
at 1 GHz would give us a constant output
from the mixer at the IF. The 1-GHz signal
would thus pass through the system and
give us a constant amplitude response
on the display regardless of the tuning
of the LO. The result would be a hole in
the frequency range at which we could
not properly examine signals because the
amplitude response would be independent
of the LO frequency. Therefore, a 1-GHz IF
will not work.
Instead, we choose an IF that is above
the highest frequency to which we wish
to tune. In the Keysight X-Series signal
analyzers that can tune to 3.6 GHz, the
first LO frequency range is 3.8 to 8.7 GHz,
and the IF chosen is about 5.1 GHz.
Remember that we want to tune from
0 Hz to 3.6 GHz (actually from some low
frequency because we cannot view a 0-Hz
signal with this architecture).
If we start the LO at the IF (LO minus IF
= 0 Hz) and tune it upward from there to
3.6 GHz above the IF, we can cover the
tuning range with the LO minus IF mixing
product. Using this information, we can
generate a tuning equation:
fsig = f LO - f IF
where f sig = signal frequency
f LO = local oscillator frequency, and
f IF = intermediate frequency (IF)
Figure 2-4 illustrates analyzer tuning. In
this figure, f LO is not quite high enough
to cause the f LO – fsig mixing product to
fall in the IF pass band, so there is no
response on the display. If we adjust the
ramp generator to tune the LO higher,
however, this mixing product will fall in
the IF pass band at some point on the
ramp (sweep), and we will see a response
on the display.
The ramp generator controls both the
horizontal position of the trace on the
display and the LO frequency, so we
can now calibrate the horizontal axis of
the display in terms of the input signal
Then we would apply the numbers for the
signal and IF in the tuning equation2:
We are not quite through with the tuning
yet. What happens if the frequency of
the input signal is 9.0 GHz? As the LO
tunes through its 3.8- to 8.7-GHz range,
it reaches a frequency (3.9 GHz) at which
it is the IF away from the 9.0-GHz input
signal. At this frequency we have a mixing
product that is equal to the IF, creating a
response on the display. In other words,
the tuning equation could just as easily
have been:
f LO = 1 kHz + 5.1 GHz = 5.100001 GHz
f sig = f LO + f IF
If we wanted to determine the LO
frequency needed to tune the analyzer to
a low-, mid-, or high-frequency signal
(say, 1 kHz, 1.5 GHz, or 3 GHz), we would
first restate the tuning equation in terms
of f LO:
f LO = fsig + f IF
f LO = 1.5 GHz + 5.1 GHz = 6.6 GHz or
This equation says that the architecture
of Figure 2-1 could also result in a tuning
range from 8.9 to 13.8 GHz, but only if we
allow signals in that range to reach the
f LO = 3 GHz + 5.1 GHz = 8.1 GHz.
Freq range
of analyzer
fLO – f sig
Freq range
of analyzer
fLO + f sig
Freq range of LO
Figure 2-4. The LO must be tuned to f IF + f sig to produce a response on the display
In the text, we round off some of the frequency values for simplicity, although the exact values are shown in the figures.
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12 | Keysight | Spectrum Analysis Basics – Application Note 150
The job of the input low-pass filter in
Figure 2-1 is to prevent these higher
frequencies from getting to the mixer.
We also want to keep signals at the
intermediate frequency itself from
reaching the mixer, as previously
described, so the low-pass filter must do a
good job of attenuating signals at 5.1 GHz
as well as in the range from 8.9 to 13.8
In summary, we can say that for a singleband RF spectrum analyzer, we would
choose an IF above the highest frequency
of the tuning range. We would make the
LO tunable from the IF to the IF plus the
upper limit of the tuning range and include
a low-pass filter in front of the mixer that
cuts off below the IF.
To separate closely spaced signals (see
“Resolving signals” later in this chapter),
some spectrum analyzers have IF
bandwidths as narrow as 1 kHz; others,
10 Hz; still others, 1 Hz. Such narrow
filters are difficult to achieve at a center
frequency of 5.1 GHz, so we must add
additional mixing stages, typically two to
four stages, to down-convert from the first
to the final IF. Figure 2-5 shows a possible
IF chain based on the architecture of a
typical spectrum analyzer.
3.6 GHz
The full tuning equation for this analyzer is:
response on the display, as if it were
an input signal at 0 Hz. This response,
the LO feedthrough, can mask very
low-frequency signals, so not all analyzers
allow the display range to include 0 Hz.
f LO2 + f LO3 + f final IF
IF gain
fsig = f LO1 – (f LO2 + f LO3 + f final IF)
= 4.8 GHz + 300 MHz + 22.5 MHz
= 5.1225 GHz, the first IF.
Simplifying the tuning equation by using
just the first IF leads us to the same
answers. Although only passive filters
are shown in figure 2-5, the actual
implementation includes amplification
in the narrower IF stages. The final IF
section contains additional components,
such as logarithmic amplifiers or analog
-to-digital converters, depending on the
design of the particular analyzer.
Most RF spectrum analyzers allow an LO
frequency as low as, and even below, the
first IF. Because there is finite isolation
between the LO and IF ports of the mixer,
the LO appears at the mixer output. When
the LO equals the IF, the LO signal itself is
processed by the system and appears as a
5.1225 GHz
322.5 MHz
22.5 MHz
Referring back to Figure 2-1, we see the
next component of the block diagram
is a variable gain amplifier. It is used
to adjust the vertical position of signals
on the display without affecting the
signal level at the input mixer. When
the IF gain is changed, the value of the
reference level is changed accordingly to
retain the correct indicated value for the
displayed signals. Generally, we do not
want the reference level to change when
we change the input attenuator, so the
settings of the input attenuator and the IF
gain are coupled together.
A change in input attenuation will
automatically change the IF gain to
offset the effect of the change in input
attenuation, thereby keeping the signal at
a constant position on the display.
3.8 to 8.7 GHz
4.8 GHz
300 MHz
Figure 2-5. Most spectrum analyzers use two to four mixing steps to reach the final IF.
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13 | Keysight | Spectrum Analysis Basics – Application Note 150
Resolving signals
After the IF gain amplifier, we find the IF
section, which consists of the analog or
digital resolution bandwidth (RBW) filters,
or both.
Analog filters
Frequency resolution is the ability of a
spectrum analyzer to separate two input
sinusoids into distinct responses. Fourier
tells us that a sine-wave signal only has
energy at one frequency, so we should
not have any resolution problems. Two
signals, no matter how close in frequency,
should appear as two lines on the display.
But a closer look at our superheterodyne
receiver shows why signal responses have
a definite width on the display.
The output of a mixer includes the sum
and difference products plus the two
original signals (input and LO). A bandpass
filter determines the intermediate
frequency, and this filter selects the
desired mixing product and rejects all
other signals. Because the input signal is
fixed and the local oscillator is swept, the
products from the mixer are also swept. If
a mixing product happens to sweep past
the IF, the characteristic shape of the
bandpass filter is traced on the display.
See Figure 2-6. The narrowest filter in the
chain determines the overall displayed
bandwidth, and in the architecture of
Figure 2-5, this filter is in the 22.5-MHz IF.
Two signals must be far enough apart
or the traces they make will fall on top
of each other and look like only one
response. Fortunately, spectrum analyzers
have selectable resolution (IF) filters, so
it is usually possible to select one narrow
enough to resolve closely spaced signals.
Keysight data sheets describe the ability
to resolve signals by listing the 3-dB
bandwidths of the available IF filters. This
number tells us how close together equalamplitude sinusoids can be and still be
resolved. In this case, there will be about
a 3-dB dip between the two peaks traced
out by these signals. See Figure 2-7. The
signals can be closer together before their
traces merge completely, but the 3-dB
bandwidth is a good rule of thumb for
resolution of equal-amplitude signals 3.
Figure 2-6. As a mixing product sweeps past the IF filter, the filter shape is traced on the display
3. If you experiment with resolution on a spectrum analyzer
using the normal (rosenfell) detector mode (See “Detector
types” later in this chapter) use enough video filtering to
create a smooth trace. Otherwise, you will see smearing as
the two signals interact. While the smeared trace certainly
indicates the presence of more than one signal, it is difficult
to determine the amplitudes of the individual signals. Spectrum analyzers with positive peak as their default detector
mode may not show the smearing effect. You can observe
the smearing by selecting the sample detector mode.
Figure 2-7. Two equal-amplitude sinusoids separated by the 3-dB BW of the selected
IF filter can be resolved.
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14 | Keysight | Spectrum Analysis Basics – Application Note 150
More often than not, we are dealing with
sinusoids that are not equal in amplitude.
The smaller sinusoid can actually be lost
under the skirt of the response traced
out by the larger. This effect is illustrated
in Figure 2-8. The top trace looks like a
single signal, but in fact represents two
signals: one at 300 MHz (0 dBm) and
another at 300.005 MHz (–30 dBm). The
lower trace shows the display after the
300-MHz signal is removed.
Another specification is listed for the
resolution filters: bandwidth selectivity
(or selectivity or shape factor). Bandwidth
selectivity helps determine the resolving
power for unequal sinusoids. For Keysight
analyzers, bandwidth selectivity is
generally specified as the ratio of the
60-dB bandwidth to the 3-dB bandwidth,
as shown in Figure 2-9. The analog filters
in Keysight analyzers are a four-pole,
synchronously tuned design, with a nearly
Gaussian shape4. This type of filter exhibits
a bandwidth selectivity of about 12.7:1.
Figure 2-8. A low-level signal can be lost under the skirt of the response to a larger signal
For example, what resolution bandwidth
must we choose to resolve signals that
differ by 4 kHz and 30 dB, assuming 12.7:1
bandwidth selectivity?
Figure 2-9. Bandwidth selectivity, ratio of 60-dB to 3-dB bandwidths
Some older spectrum analyzer models used five-pole filters for the narrowest resolution bandwidths to provide improved selectivity of about 10:1.
Modern designs achieve even better bandwidth selectivity using digital IF filters.
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15 | Keysight | Spectrum Analysis Basics – Application Note 150
Because we are concerned with rejection
of the larger signal when the analyzer is
tuned to the smaller signal, we need to
consider not the full bandwidth, but the
frequency difference from the filter center
frequency to the skirt. To determine how
far down the filter skirt is at a given offset,
we use the following equation:
H(∆f) = –10(N) log10 [(∆f/f 0)2 + 1]
H(∆f) is the filter skirt rejection in dB,
N is the number of filter poles,
∆f is the frequency offset from the center
in Hz, and
f 0 is given by
2 √ 21/N –1
For our example, N=4 and ∆f = 4000.
Let’s begin by trying the 3-kHz RBW
First, we compute f 0:
f 0 = 2 √ 2¼ –1 = 3448.44
Now we can determine the filter rejection
at a 4-kHz offset:
H(4000) = –10(4) log10 [(4000/3448.44)2 + 1]
= −14.8 dB
This is not enough to allow us to see the
smaller signal. Let’s determine H(∆f) again
using a 1-kHz filter:
f0 =
= 1149.48
2 √ 2¼ –1
This allows us to calculate the filter
H(4000) = –10(4) log10[(4000/1149.48)2 + 1]
= −44.7 dB
Thus, the 1-kHz resolution bandwidth
filter does resolve the smaller signal, as
illustrated in Figure 2-10.
Figure 2-10. The 3-kHz filter (top trace) does not resolve the smaller signal; reducing the resolution bandwidth
to 1 kHz (bottom trace) does
Digital filters
Residual FM
Some spectrum analyzers use digital
techniques to realize their resolution
bandwidth filters. Digital filters can
provide important benefits, such as
dramatically improved bandwidth
selectivity. The Keysight PSA and X-Series
signal analyzers implement all resolution
bandwidths digitally. Other
analyzers, such as the Keysight ESA-E
Series, take a hybrid approach, using
analog filters for the wider bandwidths
and digital filters for bandwidths of 300
Hz and below. Refer to Chapter 3 for more
information on digital filters.
The instability and residual FM of the
LOs in an analyzer, particularly the first
LO, often determine the minimum usable
resolution bandwidth. The unstable YIG
(yttrium iron garnet) oscillator used in
early analyzers typically had a residual FM
of about 1 kHz. Because this instability
was transferred to any mixing product
involving the LO, there was no point in
having resolution bandwidths narrower
than 1 kHz because it was impossible to
determine the cause of any instability on
the display.
However, modern analyzers have
dramatically improved residual FM. For
example, residual FM in Keysight PXA
Series analyzers is nominally 0.25 Hz;
in PSA Series analyzers, 1 to 4 Hz; and
in ESA Series analyzers, 2 to 8 Hz. This
allows bandwidths as low as 1 Hz in many
analyzers, and any instability we see on
a spectrum analyzer today is due to the
incoming signal.
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16 | Keysight | Spectrum Analysis Basics – Application Note 150
Phase noise
No oscillator is perfectly stable. Even
though we may not be able to see the
actual frequency jitter of a spectrum
analyzer LO system, there is still a
manifestation of the LO frequency or
phase instability that can be observed.
This is known as phase noise (sometimes
called sideband noise).
All are frequency or phase modulated
by random noise to some extent. As
previously noted, any instability in the
LO is transferred to any mixing products
resulting from the LO and input signals. So
the LO phase noise modulation sidebands
appear around any spectral component
on the display that is far enough above
the broadband noise floor of the system
(Figure 2-11). The amplitude difference
between a displayed spectral component
and the phase noise is a function of
the stability of the LO. The more stable
the LO, the lower the phase noise. The
amplitude difference is also a function of
the resolution bandwidth. If we reduce
the resolution bandwidth by a factor of
10, the level of the displayed phase noise
decreases by 10 dB5.
The shape of the phase noise spectrum is
a function of analyzer design, in particular,
the sophistication of the phase-lock loops
employed to stabilize the LO. In some
analyzers, the phase noise is a relatively
flat pedestal out to the bandwidth of the
stabilizing loop. In others, the phase noise
may fall away as a function of frequency
offset from the signal. Phase noise is
specified in terms of dBc (dB relative
to a carrier) and normalized to a 1-Hz
noise power bandwidth. It is sometimes
specified at specific frequency offsets. At
other times, a curve is given to show the
phase noise characteristics over a range
of offsets.
Figure 2-11. Phase noise is displayed only when a signal is displayed far enough above the system noise floor
Generally, we can see the inherent phase
noise of a spectrum analyzer only in
the narrower resolution filters, when it
obscures the lower skirts of these filters.
The use of the digital filters previously
described does not change this effect.
For wider filters, the phase noise is hidden
under the filter skirt, just as in the case of
two unequal sinusoids discussed earlier.
Today’s spectrum or signal analyzers,
such as Keysight’s X-Series, allow you to
select different LO stabilization modes
to optimize the phase noise for different
measurement conditions. For example, the
PXA signal analyzer offers three different
– Optimize phase noise for frequency
offsets > 160 kHz from the carrier
This mode optimizes phase noise for
offsets above 160 kHz away from the
– Optimize LO for fast tuning
When this mode is selected, LO
behavior compromises phase noise
at all offsets from the carrier below
approximately 2 MHz. This mode
minimizes measurement time and
allows the maximum measurement
throughput when changing the center
frequency or span.
– Optimize phase noise for frequency
offsets < 140 kHz from the carrier
In this mode, the LO phase noise is
optimized for the area close in to the
carrier at the expense of phase noise
beyond 140-kHz offset.
5. The effect is the same for the broadband noise floor (or any broadband noise signal). See Chapter 5, “Sensitivity and Noise.”
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17 | Keysight | Spectrum Analysis Basics – Application Note 150
Figure 2-12a. Phase noise performance can be optimized for different
measurement conditions
Figure 2-12b. Detail of the 140-kHz carrier offset region
The PXA signal analyzers phase noise
optimization can also be set to auto mode,
which automatically sets the instrument’s
behavior to optimize speed or dynamic
range for various operating conditions.
When the span is > 44.44 MHz or the RBW
is > 1.9 MHz, the PXA selects Fast Tuning
mode. Otherwise, the PXA automatically
chooses Best Close-In Phase Noise
when center frequency < 195 kHz, or
when center frequency ≥ 1 MHz and
span ≤ 1.3 MHz and RBW ≤ 75 kHz. If
these conditions are not met, the PXA
automatically chooses Best Wide-Offset
Phase Noise.
In any case, phase noise becomes the
ultimate limitation in an analyzer’s ability
to resolve signals of unequal amplitude.
As shown in Figure 2-13, we may have
determined that we can resolve two
signals based on the 3-dB bandwidth and
selectivity, only to find that the phase
noise covers up the smaller signal.
Figure 2-13. Phase noise can prevent resolution of unequal signals
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18 | Keysight | Spectrum Analysis Basics – Application Note 150
Sweep time
Analog resolution filters
If resolution were the only criterion on
which we judged a spectrum analyzer,
we might design our analyzer with the
narrowest possible resolution (IF) filter
and let it go at that. But resolution affects
sweep time, and we care very much
about sweep time. Sweep time directly
affects how long it takes to complete a
Resolution comes into play because the IF
filters are band-limited circuits that require
finite times to charge and discharge. If the
mixing products are swept through them
too quickly, there will be a loss of displayed
amplitude, as shown in Figure 2-14. (See
“Envelope detector,” later in this chapter,
for another approach to IF response
time.) If we think about how long a mixing
product stays in the pass band of the IF
filter, that time is directly proportional to
bandwidth and inversely proportional to
the sweep in Hz per unit time, or:
Time in pass band =
= (RBW)(ST)
RBW = resolution bandwidth and
ST = sweep time.
On the other hand, the rise time of
a filter is inversely proportional to its
bandwidth, and if we include a constant
of proportionality, k, then:
Rise time =
If we make the terms equal and solve for
sweep time, we have:
or ST =
Figure 2-14. Sweeping an analyzer too fast causes a drop in displayed amplitude and a shift in indicated
The important message here is that a
change in resolution has a dramatic effect
on sweep time. Older analog analyzers
typically provided values in a 1, 3, 10
sequence or in ratios roughly equaling
the square root of 10. So sweep time
was affected by a factor of about 10 with
each step in resolution. Keysight X-Series
s ign a l analyzers offer bandwidth steps of
just 10% for an even better compromise
among span, resolution and sweep time.
Spectrum analyzers automatically couple
sweep time to the span and resolution
bandwidth settings. Sweep time is
adjusted to maintain a calibrated display.
If the need arises, we can override the
automatic setting and set sweep time
manually. If you set a sweep time shorter
than the maximum available, the analyzer
indicates that the display is uncalibrated
with a “Meas Uncal” message in the
upper-right part of the graticule.
k (Span)
For the synchronously-tuned, nearGaussian filters used in many analog
analyzers, the value of k is in the 2 to 3
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19 | Keysight | Spectrum Analysis Basics – Application Note 150
Digital resolution filters
The digital resolution filters used in
Keysight spectrum analyzers have an
effect on sweep time that is different from
the effects we’ve just discussed for analog
filters. For swept analysis, the speed of
digitally implemented filters, with no
further processing, can show a two to four
times improvement.
However, the X-Series signal analyzers
with Option FS1 are programmed to
correct for the effect of sweeping too fast
for resolution bandwidths between about
3 kHz and 100 kHz. As a result, sweep
times that would otherwise be many
seconds may be reduced to milliseconds,
depending upon the particular settings.
See Figure 2-14a. The sweep time without
the correction would be 79.8 seconds.
Figure 2-14b shows a sweep time of 1.506
s with Option FS1 installed. For the widest
resolution bandwidths, sweep times are
already very short. For example, using
the formula with k = 2 on a span of 1 GHz
and a RBW of 1 MHz, the sweep time
calculates to just 2 msec.
For narrower resolution bandwidths,
analyzers such as the Keysight X-Series
use fast Fourier transforms (FFTs) to
process the data, also producing shorter
sweep times than the formula predicts.
The difference occurs because the signal
being analyzed is processed in frequency
blocks, depending upon the particular
analyzer. For example, if the frequency
block was 1 kHz, then when we select a
10-Hz resolution bandwidth, the analyzer
is in effect simultaneously processing the
data in each 1-kHz block through 100
contiguous 10-Hz filters. If the digital
processing were instantaneous, we would
expect sweep time to be reduced by a
factor of 100. In practice, the reduction
factor is less, but is still significant. For
more information on the advantages of
digital processing, refer to Chapter 3.
Figure 2-14a. Full span sweep speed, RBW of 20 kHz, without Option FS1
Figure 2-14b. Full span sweep speed, RBW of 20 kHz, with Option FS1
More information
A more detailed discussion about fast sweep measurements can be found in Using
Fast-Sweep Techniques to Accelerate Spur Searches – Application Note, literature number
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20 | Keysight | Spectrum Analysis Basics – Application Note 150
Envelope detector6
Older analyzers typically converted the IF
signal to video with an envelope detector7.
In its simplest form, an envelope detector
consists of a diode, resistive load and
low-pass filter, as shown in Figure 2-15.
The output of the IF chain in this example,
an amplitude modulated sine wave, is
applied to the detector. The response
of the detector follows the changes in
the envelope of the IF signal, but not the
instantaneous value of the IF sine wave
For most measurements, we choose a
resolution bandwidth narrow enough to
resolve the individual spectral components
of the input signal. If we fix the frequency
of the LO so that our analyzer is tuned to
one of the spectral components of the
signal, the output of the IF is a steady
sine wave with a constant peak value. The
output of the envelope detector will then
be a constant (DC) voltage, and there is no
variation for the detector to follow.
However, there are times when we
deliberately choose a resolution
bandwidth wide enough to include two
or more spectral components. At other
times, we have no choice. The spectral
components are closer in frequency than
our narrowest bandwidth. Assuming only
two spectral components within the pass
band, we have two sine waves interacting
to create a beat note, and the envelope
of the IF signal varies, as shown in Figure
2-16, as the phase between the two sine
waves varies.
More information
Additional information on envelope
detectors can be found in Spectrum
and Signal Analyzer Measurements and
Noise–Application Note, literature
number 5966-4008E.
IF signal
Figure 2-15. Envelope detector
Figure 2-16. Output of the envelope detector follows the peaks of the IF signal
The width of the resolution (IF) filter
determines the maximum rate at which
the envelope of the IF signal can change.
This bandwidth determines how far apart
two input sinusoids can be so that after
the mixing process they will both be
within the filter at the same time. Let’s
assume a 22.5-MHz final IF and a 100-kHz
bandwidth. Two input signals separated by
100 kHz would produce mixing products
of 22.45 and 22.55 MHz and would meet
the criterion. See Figure 2-16. The detector
must be able to follow the changes in the
envelope created by these two signals but
not the 22.5-MHz IF signal itself.
The envelope detector is what makes
the spectrum analyzer a voltmeter. Let’s
duplicate the situation above and have two
equal-amplitude signals in the pass band
of the IF at the same time. A power meter
would indicate a power level 3 dB above
either signal, that is, the total power of the
two. Assume that the two signals are close
enough so that, with the analyzer tuned
half-way between them, there is negligible
attenuation due to the roll-off of the filter 8.
The analyzer display will vary between a
value that is twice the voltage of either
(6 dB greater) and zero (minus infinity on
the log scale). We must remember that
the two signals are sine waves (vectors)
at different frequencies, and so they
continually change in phase with respect to
each other. At some time they add exactly
in phase; at another, exactly out of phase.
So the envelope detector follows the
changing amplitude values of the peaks
of the signal from the IF chain but not
the instantaneous values, resulting in the
loss of phase information. This gives the
analyzer its voltmeter characteristics.
Digitally implemented resolution
bandwidths do not have an analog
envelope detector. Instead, the digital
processing computes the root sum of
the squares of the I and Q data, which is
mathematically equivalent to an envelope
detector. For more information on digital
architecture, refer to Chapter 3.
6. The envelope detector should not be confused with the display detectors. See “Detector types” later in this chapter.
7. A signal whose frequency range extends from zero (DC) to some upper frequency determined by the circuit elements. Historically, spectrum
analyzers with analog displays used this signal to drive the vertical deflection plates of the CRT directly. Hence it was known as the video signal.
8. For this discussion, we assume the filter is perfectly rectangular.
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21 | Keysight | Spectrum Analysis Basics – Application Note 150
Up until the mid-1970s, spectrum
analyzers were purely analog. The
displayed trace presented a continuous
indication of the signal envelope, and no
information was lost. However, analog
displays had drawbacks. The major
problem was in handling the long sweep
times required for narrow resolution
bandwidths. In the extreme case, the
display became a spot that moved slowly
across the cathode ray tube (CRT),
with no real trace on the display. So a
meaningful display was not possible with
the longer sweep times.
Keysight (part of Hewlett-Packard at the
time) pioneered a variable-persistence
storage CRT in which we could adjust the
fade rate of the display. When properly
adjusted, the old trace would just fade
out at the point where the new trace was
updating the display. This display was
continuous, had no flicker and avoided
confusing overwrites. It worked quite well,
but the intensity and the fade rate had to
be readjusted for each new measurement
When digital circuitry became affordable
in the mid-1970s, it was quickly put to
use in spectrum analyzers. Once a trace
had been digitized and put into memory,
it was permanently available for display.
It became an easy matter to update
the display at a flicker-free rate without
blooming or fading. The data in memory
was updated at the sweep rate, and since
the contents of memory were written to
the display at a flicker-free rate, we could
follow the updating as the analyzer swept
through its selected frequency span just
as we could with analog systems.
Figure 2-17. When digitizing an analog signal, what value should be displayed at each point?
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22 | Keysight | Spectrum Analysis Basics – Application Note 150
Detector types
With digital displays, we had to decide
what value should be displayed for each
display data point. No matter how many
data points we use across the display,
each point must represent what has
occurred over some frequency range and
time interval (although we usually do not
think in terms of time when dealing with a
spectrum analyzer).
It is as if the data for each interval is
thrown into a bucket and we apply
whatever math is necessary to extract the
desired bit of information from our input
signal. This datum is put into memory
and written to the display. This process
provides great flexibility.
Here we will discuss six different
detector types.
In Figure 2-18, each bucket contains
data from a span and timeframe that is
determined by these equations:
bucket width = span/(trace points – 1)
bucket width = sweep time/(trace points – 1)
The sampling rates are different for
various instruments, but greater accuracy
is obtained from decreasing the span
or increasing the sweep time because
the number of samples per bucket will
increase in either case. Even in analyzers
with digital IFs, sample rates and
interpolation behaviors are designed to
be the equivalent of continuous-time
The “bucket” concept is important, as it
will help us differentiate the six detector
– Sample
– Positive peak (also simply called peak)
– Negative peak
– Normal
– Average
– Quasipeak
Figure 2-18. Each of the 1001 trace points (buckets) covers a 100-kHz frequency span and a 0.01-millisecond
time span
One bucket
Positive peak
Negative peak
Figure 2-19. The trace point saved in memory is based on the detector type algorithm
The first three detectors, sample, peak,
and negative peak are easy to understand
and are visually represented in Figure
2-19. Normal, average, and quasipeak are
more complex and will be discussed later.
Let’s return to the question of how to
display an analog system as faithfully
as possible using digital techniques.
Let’s imagine the situation illustrated
in Figure 2-17. We have a display that
contains only noise and a single CW
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23 | Keysight | Spectrum Analysis Basics – Application Note 150
Sample detection
As a first method, let us simply select
the data point as the instantaneous level
at the center of each bucket (see Figure
2-19). This is the sample detection mode.
To give the trace a continuous look,
we design a system that draws vectors
between the points. Comparing Figure
2-17 with 2-20, it appears that we get a
fairly reasonable display. Of course, the
more points there are in the trace, the
better the replication of the analog signal
will be. The number of available display
points can vary for different analyzers. On
X-Series signal analyzers, the number of
display points for frequency domain traces
can be set from a minimum of 1 point to
a maximum of 40,001 points. As shown in
Figure 2-21, more points do indeed get us
closer to the analog signal.
While the sample detection mode does a
good job of indicating the randomness of
noise, it is not a good mode for analyzing
sinusoidal signals. If we were to look at
a 100-MHz comb on a Keysight PXA, we
might set it to span from 0 to 26.5 GHz.
Even with 1,001 display points, each
display point represents a span (bucket)
of 26.5 MHz. This is far wider than the
maximum 8-MHz resolution bandwidth.
Figure 2-20. Sample display mode using 10 points
to display the signal shown in Figure 2-17
Figure 2-21. More points produce a display closer
to an analog display
Figure 2-22a. A 10-MHz span of a 250-kHz comb in the sample display mode
As a result, the true amplitude of a comb
tooth is shown only if its mixing product
happens to fall at the center of the IF when
the sample is taken. Figure 2-22a shows
a 10-MHz span with a 750-Hz bandwidth
using sample detection. The comb teeth
should be relatively equal in amplitude,
as shown in Figure 2-22b (using peak
detection). Therefore, sample detection
does not catch all the signals, nor does it
necessarily reflect the true peak values
of the displayed signals. When resolution
bandwidth is more narrow than the sample
interval (the bucket width), sample mode
can give erroneous results.
Figure 2-22b. The actual comb over a 10-MHz span using peak (positive) detection
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24 | Keysight | Spectrum Analysis Basics – Application Note 150
Peak (positive) detection
Negative peak detection
Normal detection
One way to insure that all sinusoids are
reported at their true amplitudes is to
display the maximum value encountered
in each bucket. This is the positive peak
detection mode, or peak. This mode is
illustrated in Figure 2-22b. Peak is the
default mode offered on many spectrum
analyzers because it ensures that no
sinusoid is missed, regardless of the ratio
between resolution bandwidth and bucket
width. However, unlike sample mode,
peak does not give a good representation
of random noise because it only displays
the maximum value in each bucket and
ignores the true randomness of the noise.
So spectrum analyzers that use peak
detection as their primary mode generally
also offer sample mode as an alternative.
Negative peak detection displays the
minimum value encountered in each
bucket. It is generally available in most
spectrum analyzers, though it is not used
as often as other types of detection.
Differentiating CW from impulsive signals
in EMC testing is one application where
negative peak detection is valuable. Later
in this application note, we will see how
negative peak detection is also used in
signal identification routines when you
use external mixers for high-frequency
To provide a better visual display of
random noise than offered by peak mode
and yet avoid the missed-signal problem
of the sample mode, the normal detection
mode (informally known as Rosenfell 9
mode) is offered on many spectrum
analyzers. Should the signal both rise
and fall, as determined by the positive
peak and negative peak detectors, the
algorithm classifies the signal as noise.
Figure 2-23a. Normal mode
In that case, an odd-numbered data point
displays the maximum value encountered
during its bucket. And an even-numbered
data point displays the minimum value
encountered during its bucket. See Figure
2-25. Normal and sample modes are
compared in Figures 2-23a and 2-23b.10
Figure 2-23b. Sample mode
Rosenfell is not a person’s name but rather a description of the algorithm that tests to see if the signal rose and fell within the bucket represented
by a given data point. It is also sometimes written as “rose’n’fell.”
10. Because of its usefulness in measuring noise, the sample detector is usually used in “noise marker” applications. Similarly, the measurement of
channel power and adjacent-channel power requires a detector type that gives results unbiased by peak detection. For analyzers without
averaging detectors, sample detection is the best choice.
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25 | Keysight | Spectrum Analysis Basics – Application Note 150
What happens when a sinusoidal signal
is encountered? We know that as a mixing
product is swept past the IF filter, an
analyzer traces out the shape of the filter
on the display. If the filter shape is spread
over many display points, we encounter a
situation in which the displayed signal only
rises as the mixing product approaches
the center frequency of the filter and
only falls as the mixing product moves
away from the filter center frequency. In
either of these cases, the positive-peak
and negative-peak detectors sense an
amplitude change in only one direction,
and, according to the normal detection
algorithm, the maximum value in each
bucket is displayed. See Figure 2-24.
What happens when the resolution
bandwidth is narrow, relative to a bucket?
The signal will both rise and fall during
the bucket. If the bucket happens to be
an odd-numbered one, all is well. The
maximum value encountered in the bucket
is simply plotted as the next data point.
However, if the bucket is even-numbered,
then the minimum value in the bucket
is plotted. Depending on the ratio of
resolution bandwidth to bucket width, the
minimum value can differ from the true
peak value (the one we want displayed) by
a little or a lot. In the extreme, when the
bucket is much wider than the resolution
bandwidth, the difference between the
maximum and minimum values encountered
in the bucket is the full difference
between the peak signal value and the
noise. This is true for the example in
Figure 2-25. See bucket 6. The peak
value of the previous bucket is always
compared to that of the current bucket.
The greater of the two values is displayed
if the bucket number is odd, as depicted
in bucket 7. The signal peak actually
occurs in bucket 6 but is not displayed
until bucket 7.
Figure 2-24. Normal detection displays maximum values in buckets where the signal only rises or only falls
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26 | Keysight | Spectrum Analysis Basics – Application Note 150
The normal detection algorithm:
If the signal rises and falls within a bucket:
Even-numbered buckets display the
minimum (negative peak) value in the
bucket. The maximum is remembered.
Odd-numbered buckets display the
maxi(positive peak) value determined by
comparing the current bucket peak with
the previous (remembered) bucket peak.
If the signal only rises or only falls within a
bucket, the peak is displayed. See Figure
This process may cause a maximum value
to be displayed one data point too far to
the right, but the offset is usually only
a small percentage of the span. Some
spectrum analyzers, such as the Keysight
PXA signal analyzer, compensate for this
potential effect by moving the LO start
and stop frequencies.
Figure 2-25. Trace points selected by the normal detection algorithm
Another type of error occurs when two
peaks are displayed when only one
actually exists. Figure 2-26 shows this
error. The outline of the two peaks is
displayed using peak detection with a
wider RBW.
So peak detection is best for locating CW
signals well out of the noise. Sample is
best for looking at noise, and normal is
best for viewing signals and noise.
Figure 2-26. Normal detection can show two peaks when only one peak actually exists
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27 | Keysight | Spectrum Analysis Basics – Application Note 150
Average detection
Although modern digital modulation
schemes have noise-like characteristics,
sample detection does not always provide
us with the information we need. For
instance, when taking a channel power
measurement on a W-CDMA signal,
integration of the rms values is required.
This measurement involves summing
power across a range of analyzer
frequency buckets. Sample detection does
not provide this capability.
While spectrum analyzers typically collect
amplitude data many times in each bucket,
sample detection keeps only one of those
values and throws away the rest. On the
other hand, an averaging detector uses all
the data values collected within the time
(and frequency) interval of a bucket. Once
we have digitized the data, and knowing
the circumstances under which they were
digitized, we can manipulate the data in
a variety of ways to achieve the desired
Some spectrum analyzers refer to the
averaging detector as an rms detector
when it averages power (based on the root
mean square of voltage). Keysight X-Series
signal analyzers have an average detector
that can average the power, voltage or
log of the signal by including a separate
control to select the averaging type:
Power (rms) averaging computes rms
levels, by taking the square root of the
average of the squares of the voltage data
measured during the bucket interval. This
computed voltage is squared and divided
by the characteristic input impedance
of the spectrum analyzer, normally 50
ohms. Power averaging calculates the true
average power, and is best for measuring
the power of complex signals.
Voltage averaging averages the linear
voltage data of the envelope signal
measured during the bucket interval. It is
often used in EMI testing for measuring
narrowband signals (this topic will be
discussed further in the next section).
Voltage averaging is also useful for
observing rise and fall behavior of AM or
pulse-modulated signals such as radar
and TDMA transmitters.
In both swept and FFT cases, the
integration captures all the power
information available, rather than just
that which is sampled by the sample
detector. As a result, the average detector
has a lower variance result for the same
measurement time. In swept analysis, it
also allows the convenience of reducing
variance simply by extending the sweep
Log-power (video) averaging averages
the logarithmic amplitude values (dB)
of the envelope signal measured during
the bucket interval. Log power averaging
is best for observing sinusoidal signals,
especially those near noise.11
EMI detectors: average and
quasipeak detection
Thus, using the average detector with
the averaging type set to power provides
true average power based upon rms
voltage, while the average detector with
the averaging type set to voltage acts as
a general-purpose average detector. The
average detector with the averaging type
set to log has no other equivalent.
Average detection is an improvement
over using sample detection for the
determination of power. Sample detection
requires multiple sweeps to collect enough
data points to give us accurate average
power information. Average detection
changes channel power measurements
from being a summation over a range
of buckets into integration over the
time interval representing a range of
frequencies in a swept analyzer. In a
fast Fourier transfer (FFT) analyzer 12,
the summation used for channel power
measurements changes from being a
summation over display buckets to being a
summation over FFT bins.
11. See Chapter 5, “Sensitivity and Noise.”
12. Refer to Chapter 3 for more information on the FFT analyzers. They perform math computations on
many buckets simultaneously, which improves measurement speed.
13. CISPR, the International Special Committee on Radio Interference, was established in 1934 by a
group of international organizations to address radio interference. CISPR is a non-governmental
group composed of National Committees of the International Electrotechnical Commission (IEC),
as well as numerous international organizations. CISPR’s recommended standards generally form
the basis for statutory EMC requirements adopted by governmental regulatory agencies around the
An important application of average
detection is for characterizing devices
for electromagnetic interference (EMI).
In this case, voltage averaging, as
described in the previous section, is used
for measuring narrowband signals that
might be masked by the presence of
broadband impulsive noise. The average
detection used in EMI instruments
takes an envelope-detected signal and
passes it through a low-pass filter with
a bandwidth much less than the RBW.
The filter integrates (averages) the
higher-frequency components such as
noise. To perform this type of detection in
an older spectrum analyzer that doesn’t
have a built-in voltage averaging detector
function, set the analyzer in linear mode
and select a video filter with a cut-off
frequency below the lowest PRF of the
measured signal.
Quasipeak detectors (QPD) are also
used in EMI testing. QPD is a weighted
form of peak detection. The measured
value of the QPD drops as the repetition
rate of the measured signal decreases.
Thus, an impulsive signal with a given
peak amplitude and a 10-Hz pulse
repetition rate will have a lower quasipeak
value than a signal with the same
peak amplitude but having a 1-kHz
repetition rate. This signal weighting is
accomplished by circuitry with specific
charge, discharge and display time
constants defined by CISPR13.
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28 | Keysight | Spectrum Analysis Basics – Application Note 150
QPD is a way of measuring and quantifying
the “annoyance factor” of a signal. Imagine
listening to a radio station suffering from
interference. If you hear an occasional “pop”
caused by noise once every few seconds,
you can still listen to the program without
too much trouble. However, if that same
amplitude pop occurs 60 times per second,
it becomes extremely annoying, making the
radio program intolerable to listen to.
Averaging processes
There are several processes in a spectrum
analyzer that smooth the variations
in envelope-detected amplitude. The
first method, average detection, was
discussed previously. Two other methods,
video filtering and trace averaging, are
discussed next.14
Figure 2-27. Spectrum analyzers display signal plus noise
Video filtering
Discerning signals close to the noise is
not just a problem when performing EMC
tests. Spectrum analyzers display signals
plus their own internal noise, as shown in
Figure 2-27. To reduce the effect of noise
on the displayed signal amplitude, we often
smooth or average the display, as shown in
Figure 2-28. Spectrum analyzers include
a variable video filter for this purpose. The
video filter is a low-pass filter that comes
after the envelope detector and determines
the bandwidth of the video signal that will
later be digitized to yield amplitude data.
The cutoff frequency of the video filter can
be reduced to the point where it becomes
smaller than the bandwidth of the selected
resolution bandwidth (IF) filter. When this
occurs, the video system can no longer
follow the more rapid variations of the
envelope of the signal(s) passing through
the IF chain.
Figure 2-28. Display of Figure 2-27 after full smoothing
More information
A more detailed discussion about
noise markers can be found in Spectrum and Signal Analyzer Measurements
and Noise – Application Note, literature
number 5966-4008E
A fourth method, called a noise marker,
is discussed in Chapter 5, “Sensitivity and
Figure 2-29. Smoothing effect of VBW-to-RBW ratios of 3:1, 1:10, and 1:100
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29 | Keysight | Spectrum Analysis Basics – Application Note 150
The result is an averaging or smoothing of
the displayed signal.
The effect is most noticeable in measuring noise, particularly when you use a
wide-resolution bandwidth. As we reduce
the video bandwidth, the peak-to-peak
variations of the noise are reduced. As
Figure 2-29 shows, the degree of reduction (degree of averaging or smoothing)
is a function of the ratio of the video to
resolution bandwidths. At ratios of 0.01 or
less, the smoothing is very good. At higher
ratios, the smoothing is not as good. The
video filter does not affect any part of the
trace that is already smooth (for example,
a sinusoid displayed well out of the noise).
If we set the analyzer to positive peak
detection mode, we notice two things:
First, if VBW > RBW, then changing the
resolution bandwidth does not make
much difference in the peak-to-peak
fluctuations of the noise. Second, if VBW
< RBW, changing the video bandwidth
seems to affect the noise level. The
fluctuations do not change much
because the analyzer is displaying only
the peak values of the noise. However,
the noise level appears to change with
video bandwidth because the averaging
(smoothing) changes, thereby changing
the peak values of the smoothed noise
envelope. See Figure 2-30a. When we
select average detection, we see the
average noise level remains constant. See
Figure 2-30b.
Figure 2-30a. Positive peak detection mode: reducing video bandwidth lowers peak noise but not
average noise
Because the video filter has its own
response time, the sweep time increases
approximately inversely with video
bandwidth when the VBW is less than the
resolution bandwidth. The sweep time
(ST) can therefore be described by this
The analyzer sets the sweep time
automatically to account for video
bandwidth as well as span and resolution
Figure 2-30b. Average detection mode: noise level remains constant, regardless of VBW-to-RBW ratios
(3:1, 1:10 and 1:100)
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30 | Keysight | Spectrum Analysis Basics – Application Note 150
Trace averaging
Digital displays offer another choice for
smoothing the display: trace averaging.
Trace averaging uses a completely
different process from the smoothing
performed using the average detector. In
this case, averaging is accomplished over
two or more sweeps on a point-by-point
basis. At each display point, the new
value is averaged in with the previously
averaged data:
n– 1
A avg = n
A prior avg +
n An
( )
( )
A avg = new average value
A prior avg = average from prior sweep
A n= measured value on current sweep
n = number of current sweep
Thus, the display gradually converges to an
average over a number of sweeps. As with
video filtering, we can select the degree
of averaging or smoothing. We do this by
setting the number of sweeps over which
the averaging occurs. Figure 2-31 shows
trace averaging for different numbers of
sweeps. While trace averaging has no effect
on sweep time, the time to reach a given
degree of averaging is about the same as
with video filtering because of the number
of sweeps required.
In many cases, it does not matter which
form of display smoothing we pick. If the
signal is noise or a low-level sinusoid
very close to the noise, we get the same
results with either video filtering or trace
Figure 2-32a. Video filtering
Figure 2-31. Trace averaging for 1, 5, 20 and 100 sweeps, top to bottom (trace position offset for each
set of sweeps
However, there is a distinct difference
between the two. Video filtering performs
averaging in real time. That is, we see the
full effect of the averaging or smoothing
at each point on the display as the sweep
progresses. Each point is averaged only
once, for a time of about 1/VBW on each
sweep. Trace averaging, on the other hand,
requires multiple sweeps to achieve the
full degree of averaging, and the averaging
at each point takes place over the full time
period needed to complete the multiple
As a result, we can get significantly
different results from the two averaging
methods on certain signals. For example,
a signal with a spectrum that changes
with time can yield a different average on
each sweep when we use video filtering.
However, if we choose trace averaging
over many sweeps, we will get a value
much closer to the true average. See
Figures 2-32a and 2-32b.
Figures 2-32a and 2-32b show how video
filtering and trace averaging yield different
results on an FM broadcast signal.
Figure 2-32b. Trace averaging
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31 | Keysight | Spectrum Analysis Basics – Application Note 150
Time gating
Measuring time division
duplex signals
Time-gated spectrum analysis allows
you to obtain spectral information about
signals occupying the same part of the
frequency spectrum that are separated
in the time domain. Using an external
trigger signal to coordinate the separation
of these signals, you can perform the
following operations:
– Measure any one of several signals
separated in time (For example, you
can separate the spectra of two radios
time-sharing a single frequency.)
– Measure the spectrum of a signal in
one time slot of a TDMA system
– Exclude the spectrum of interfering
signals, such as periodic pulse edge
transients that exist for only a limited
Why time gating is needed
Traditional frequency-domain spectrum
analysis provides only limited information
for certain difficult-to-analyze signals.
Examples include the following signal
Unfortunately, a traditional spectrum
analyzer cannot do that. It simply shows
the combined spectrum, as seen in Figure
2-33b. Using the time-gating capability
and an external trigger signal, you can see
the spectrum of just radio #1 (or radio #2
if you wish) and identify it as the source
of the spurious signal shown, as in Figure
– An externally supplied gate trigger
– The gate control or trigger mode
(edge or level) (The X-Series signal
analyzers can be set to gate-trigger
holdoff to ignore potential false
– The gate delay setting, which
determines how long after the trigger
signal the gate actually becomes
active and the signal is observed
– The gate length setting, which
determines how long the gate is on
and the signal is observed
Pulsed RF
Time multiplexed
Time domain multiple access (TDMA)
Interleaved or intermittent
Burst modulated
In some cases, time-gating capability
enables you to perform measurements
that would otherwise be very difficult, if
not impossible to make.
Figure 2-33b. Frequency spectrum of combined
signals. Which radio produces the spurious
To illustrate using time-gating capability
to perform difficult measurements,
consider Figure 2-33a, which shows a
simplified digital mobile-radio signal in
which two radios, #1 and #2, are timesharing a single frequency channel. Each
radio transmits a single 1-ms burst, then
shuts off while the other radio transmits
for 1 ms. The challenge is to measure
the unique frequency spectrum of each
Time gating can be achieved using
three different methods we will discuss
below. However, there are certain basic
concepts of time gating that apply to any
implementation. In particular, you must
have, or be able to set, the following four
Figure 2-33a. Simplified digital mobile-radio signal in the time domain
Figure 2-33c. The time-gated spectrum
of signal #1 identifies it as the source of
spurious emission
Figure 2-33d. The time-gated spectrum
of signal #2 shows it is free of spurious
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32 | Keysight | Spectrum Analysis Basics – Application Note 150
Controlling these parameters will allow
us to look at the spectrum of the signal
during a desired portion of the time. If
you are fortunate enough to have a gating
signal that is only true during the period
of interest, you can use level gating, as
shown in Figure 2-34. However, in many
cases the gating signal will not perfectly
coincide with the time we want to measure
the spectrum. Therefore, a more flexible
approach is to use edge triggering in
conjunction with a specified gate delay
and gate length to precisely define the
time period in which to measure the signal.
Consider the GSM signal with eight time
slots in Figure 2-35. Each burst is 0.577
ms and the full frame is 4.615 ms. We
may be interested in the spectrum of the
signal during a specific time slot. For the
purposes of this example, let’s assume we
are using only two of the eight available
time slots (time slots 1 and 3), as shown in
Figure 2-36. When we look at this signal
in the frequency domain in Figure 2-37,
we observe an unwanted spurious signal
present in the spectrum. In order to
troubleshoot the problem and find the
source of this interfering signal, we need
to determine the time slot in which it is
occurring. If we wish to look at time slot 3,
we set up the gate to trigger on the rising
edge of the burst in time slot 3, and, then
specify a gate delay of 1.4577 ms and
a gate length of 461.60 µs, as shown in
Figure 2-38. The gate delay assures that
we only measure the spectrum of time
slot 3 while the burst is fully on. Note that
the gate start and stop value is carefully
selected to avoid the rising and falling
edge of the burst, as we want to allow time
for the RBW filtered signal to settle out
before we make a measurement. Figure
2-39 shows the spectrum of time slot 3,
which reveals that the spurious signal is
not caused by this burst.
Figure 2-34. Level triggering: the spectrum analyzer only measures the frequency spectrum when the
gate trigger signal is above a certain level
Figure 2-35. A TDMA format signal (in this case, GSM) with 8 time slots, time slot zero is “off”.
Three methods are commonly used to
perform time gating:
– Gated FFT
– Gated LO
– Gated video
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33 | Keysight | Spectrum Analysis Basics – Application Note 150
Figure 2-36. A zero span (time domain) view of the GSM signal with only time
slots 1 and 3 “on”.
Figure 2-37. Frequency domain view of the GSM signal with 2 time slots “on”
showing an unwanted spurious signal present in the spectrum.
Figure 2-38. Time gating is used to look at the spectrum of the GSM time
slot 3.
Figure 2-39. Spectrum of time slot 3 reveals that the spurious signal is not
caused by this burst.
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34 | Keysight | Spectrum Analysis Basics – Application Note 150
IF resolution
bandwidth IF log
(IF to video)
Display logic
Scan generator
Figure 2-40. In gated LO mode, the LO sweeps only during gate interval
Gated FFT
Gated LO
The Keysight X-Series signal analyzers
have built-in FFT capabilities. In this
mode, the data is acquired for an FFT
starting at a chosen delay following
a trigger. The IF signal is digitized and
captured for a time period of 1.83 divided
by resolution bandwidth. An FFT is
computed based on this data acquisition
and the results are displayed as the
spectrum. Thus, the spectrum is that
which existed at a particular time of
known duration. This is the fastest gating
technique when the span is not wider
than the FFT maximum width.
LO gating, sometimes referred to as
gated sweep, is another technique for
performing time gating. With this method,
we control the voltage ramp produced by
the scan generator to sweep the LO, as
shown in Figure 2-40. When the gate is
active, the LO ramps up in frequency like
any spectrum analyzer. When the gate
is blocked, the voltage out of the scan
generator is frozen, and the LO stops
rising in frequency. This technique can be
much faster than gated video because
multiple buckets can be measured during
each burst. As an example, let’s use the
same GSM signal described earlier in this
To get the maximum possible frequency
resolution, choose the narrowest available
RBW with a capture time that fits within
the time period of interest. You may
not always need that much resolution,
however, and you could choose a wider
RBW with a corresponding narrower
gate length. The minimum usable RBW
in gated FFT applications is always lower
than the minimum usable RBW in other
gating techniques, because the IF must
fully settle during the burst in other
techniques, which takes longer than 1.83
divided by RBW.
Using an X-Series signal analyzer, a
standard, non-gated, spectrum sweep
over a 1-MHz span takes 14.6 ms, as
shown in Figure 2-41. With a gate length
of 0.3 ms, the spectrum analyzer sweep
must be built up in 49 gate intervals
(14.6 divided by 0.3). Or, if the full frame
of the GSM signal is 4.615 ms, the total
measurement time is 49 intervals times
4.615 ms = 226 ms. This represents
a significant improvement in speed
compared to the gated video technique,
which will be described in the following
section. LO gating is available on X-Series
signal analyzers and PSA Series spectrum
Figure 2-41. Spectrum of the GSM signal
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35 | Keysight | Spectrum Analysis Basics – Application Note 150
Gated video
Gated video is the analysis technique
used in a number of spectrum analyzers,
including the Keysight 8560, 8590 and
ESA Series. In this case, the video voltage
is switched off, or to “negative infinity
decibels,” during the time the gate is
supposed to be in its “blocked” mode.
The detector is set to peak detection. The
sweep time must be set so that the gates
occur at least once per display point,
or bucket, so the peak detector is able
to see real data during that time interval.
Otherwise, there will be trace points
with no data, resulting in an incomplete
spectrum. Therefore, the minimum sweep
time is N display buckets times burst cycle
time. For example, in GSM measurements,
the full frame lasts 4.615 ms. For an ESA
spectrum analyzer set to its default value
of 401 display points, the minimum sweep
time for GSM gated video measurements
would be 401 times 4.615 ms or 1.85 s.
IF resolution
bandwidth IF log
(IF to video)
– ∞ dB
Some TDMA formats have cycle times as
large as 90 ms, resulting in long sweep
times using the gated video technique.
Now that you’ve seen how a classic
analog spectrum analyzer works and how
to use some of the important features
and capabilities, let’s take a look at how
replacing some analog circuits with digital
technology improves spectrum analyzer
Gate control
Display logic
Scan generator
Figure 2-42. Block diagram of a spectrum analyzer with gated video
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36 | Keysight | Spectrum Analysis Basics – Application Note 150
Chapter 3. Digital IF Overview
Since the 1980s, one of the most profound
changes in spectrum analysis has been
the application of digital technology to
replace portions of spectrum analyzers
that had been implemented previously
as analog circuits. With the availability
of high-performance analog-to-digital
converters, the latest spectrum analyzers
digitize incoming signals much earlier in
the signal path compared to spectrum
analyzer designs of just a few years ago.
The change has been most dramatic in the
IF section of the spectrum analyzer. Digital
IFs1 have had a great impact on spectrum
analyzer performance, with significant
improvements in speed, accuracy and the
ability to measure complex signals using
advanced DSP techniques.
Digital filters
You will find a partial implementation of
digital IF circuitry in the Keysight ESA-E
Series spectrum analyzers. While the
1-kHz and wider RBWs are implemented
with traditional analog LC and crystal
filters, the narrowest bandwidths (1 Hz
to 300 Hz) are realized using digital
21.4 MHz
As shown in Figure 3-1, the linear analog
signal is mixed down to an 8.5-kHz IF and
passed through a bandpass filter only
1 kHz wide. This IF signal is amplified, then
sampled at an 11.3-kHz rate and digitized.
Once in digital form, the signal is
put through a fast Fourier transform
algorithm. To transform the appropriate
signal, the analyzer must be fixed-tuned
(not sweeping). That is, the transform
must be done on a time-domain signal.
Thus the ESA-E Series analyzers step in
900-Hz increments, instead of sweeping
continuously, when we select one of
the digital resolution bandwidths. This
stepped tuning can be seen on the display,
which is updated in 900-Hz increments as
the digital processing is completed.
As you will see in a moment, other
spectrum and signal analyzers, such as
the Keysight X-Series analyzers, use an
all-digital IF, implementing all resolution
bandwidth filters digitally.
A key benefit of the digital processing
done in these analyzers is a bandwidth
selectivity of about 4:1. This selectivity
is available on the narrowest filters, the
ones we would choose to separate the
most closely spaced signals.
In Chapter 2, we did a filter skirt
selectivity calculation for two signals
spaced 4 kHz apart, using a 3-kHz
analog filter. Let’s repeat that calculation
using digital filters. A good model of the
selectivity of digital filters is a nearGaussian model:
H(∆f) = –3.01 dB x
[ RBW/2
where H(∆f) is the filter skirt rejection in dB.
∆f is the frequency offset from the center
in Hz, and α is a parameter that controls
selectivity. α = 2 for an ideal Gaussian
filter. The swept RBW filters used in
Keysight spectrum analyzers are based
on a near-Gaussian model with an α value
equal to 2.12, resulting in a selectivity
ratio of 4.1:1.
Sample and hold
at 11.3 kHz
3rd LO
8.5 kHz CF
1 kHz BW
Figure 3-1. Digital implementation of 1-, 3-, 10-, 30-, 100- and 300-Hz resolution filters in ESA-E Series spectrum analyzers
Strictly speaking, once a signal has been digitized, it is no longer at an intermediate frequency, or IF. At that point, the signal is represented by digital
data values. However, we use the term “digital IF” to describe the digital processing that replaces the analog IF processing found in traditional spectrum
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37 | Keysight | Spectrum Analysis Basics – Application Note 150
Entering the values from our example into
the equation, we get:
4000 2.12
H(4 kHz) = –3.01 dB x
= –24.1 dB
At an offset of 4 kHz, the 3-kHz digital
filter is down –24.1 dB compared to the
analog filter which was only down –14.8
dB. Because of its superior selectivity,
the digital filter can resolve more closely
spaced signals.
All-digital IF
Analyzers such as the Keysight X-Series
combine several digital techniques to
achieve the all-digital IF. The all-digital IF
offers users a wealth of advantages. The
combination of FFT analysis for narrow
spans and swept analysis for wider spans
optimizes sweeps for the fastest possible
measurements. Architecturally, the ADC
is moved closer to the input port, a move
made possible by improvements to the
A-to-D converters and other digital
Let’s begin by taking a look at the block
diagram of the all-digital IF in the X-Series
signal analyzer, as shown in Figure 3-2.
Even a very fast-rising RF burst,
downconverted to the IF frequency, will
experience a delay of more than three
cycles of the ADC clock (30 MHz) through
the anti-alias filter. The delay allows
time for an impending large signal to
be recognized before it overloads the
ADC. The logic circuitry controlling the
autorange detector will decrease the gain
in front of the ADC before a large signal
reaches it, thus preventing clipping. If the
signal envelope remains small for a long
time, the autoranging circuit increases
the gain, reducing the effective noise
at the input. The digital gain after the
ADC is also changed to compensate for
the analog gain in front of it. The result
is a “floating point” ADC with very wide
dynamic range when autoranging is
enabled in swept mode.
In this case, all 160 resolution bandwidths
are digitally implemented. However, there
is some analog circuitry prior to the ADC,
starting with several stages of down
conversion, followed by a pair of singlepole prefilters (one an LC filter, the other
crystal-based). A prefilter helps prevent
succeeding stages from contributing
third-order distortion in the same way a
prefilter would in an analog IF. In addition,
it enables dynamic range extension via
autoranging. The output of the singlepole prefilter is routed to the autorange
detector and the anti-alias filter.
As with any FFT-based IF architecture,
the anti-alias filter is required to prevent
aliasing (the folding of out-of-band signals
into the ADC sampled data). This filter has
many poles and thus has substantial group
Custom IC
I, Q
log (r)
Autoranging ADC system
Figure 3-2. Block diagram of the all-digital IF in the Keysight X-Series signal analyzers
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38 | Keysight | Spectrum Analysis Basics – Application Note 150
Figure 3-3 illustrates the sweeping
behavior of the X-Series analyzers. The
single-pole prefilter allows the gain to
be turned up high when the analyzer is
tuned far from the carrier. As the carrier
gets closer, the gain falls and the ADC
quantization noise rises. The noise level
will depend on the signal level frequency
separation from the carrier, so it looks
like a step-shaped phase noise. However,
phase noise is different from this
autoranging noise. Phase noise cannot be
avoided in a spectrum analyzer. However,
reducing the prefilter width can reduce
autoranging noise at most frequency
offsets from the carrier. Since the prefilter
width is approximately 2.5 times the
RBW, reducing the RBW reduces the
autoranging noise.
Custom digital signal
Turning back to the block diagram of
the digital IF (Figure 3-2), after the ADC
gain has been set with analog gain and
corrected with digital gain, a custom IC
begins processing the samples. First, it
splits the 30-MHz IF samples into I and
Q pairs at half the rate (15 Mpairs/s). The
I and Q pairs are given a high-frequency
boost with a single-stage digital filter
that has gain and phase approximately
opposite to that of the single-pole analog
prefilter. Next, I and Q signals are low-pass
filtered with a linear-phase filter with
nearly ideal Gaussian response. Gaussian
filters have always been used for swept
spectrum analysis, because of their
optimum compromise between frequency
domain performance (shape factor) and
time-domain performance (response to
rapid sweeps). With the signal bandwidth
now reduced, the I and Q pairs may be
decimated and sent to the processor
for FFT processing or demodulation.
Although FFTs can be performed to cover
a segment of frequency span up to the
10-MHz bandwidth of the anti-alias filter,
even a narrower FFT span, such as 1 kHz,
with a narrow RBW, such as 1 Hz, would
require FFTs with 20 million data points.
Using decimation for narrower spans, the
number of data points needed to compute
the FFT is greatly reduced, speeding up
clipping threshold
Prefilter gain
analog IF
Digital IF RBW response
Noise floor after autoranging
Typical LO phase noise
Frequency or time
Figure 3-3. Autoranging keeps ADC noise close to the carrier and lower than LO noise or RBW filter response
For swept analysis, the filtered I and Q
pairs are converted to magnitude and
phase pairs. For traditional swept analysis,
the magnitude signal is video-bandwidth
(VBW) filtered and samples are taken
through the display detector circuit.
The log/linear display selection and dB/
division scaling occur in the processor,
so a trace can be displayed on any scale
without remeasuring.
Additional video processing
The VBW filter normally smoothes the
log of the magnitude of the signal, but
it has many additional features. It can
convert the log magnitude to a voltage
envelope before filtering and convert it
back for consistent behavior before display
Filtering the magnitude on a linear
voltage scale is desirable for observing
pulsed-RF envelope shapes in zero
span. The log-magnitude signal also
can be converted to a power (magnitude
squared) signal before filtering, and then
it can be converted back. Filtering the
power allows the analyzer to give the
same average response to signals with
noise-like characteristics, such as digital
communications signals, as to CW signals
with the same rms voltage. An increasingly
common measurement need is total power
in a channel or across a frequency range.
In a measurement such as this, the display
points might represent the average
power during the time the LO sweeps
through that point. The VBW filter can
be reconfigured into an accumulator to
perform averaging on either a log, voltage
or power scale.
Frequency counting
Swept spectrum analyzers usually have
a frequency counter. This counter counts
the zero crossings in the IF signal and
offsets that count by the known frequency
offsets from LOs in the rest of the
conversion chain. If the count is allowed
to run for a second, you can achieve a
resolution of 1 Hz.
Because of its digitally synthesized LOs
and all-digital RBWs, the native frequency
accuracy of the X-Series signal analyzer
is very good (0.1% of span). In addition,
the X-Series signal analyzer includes
a frequency counter that observes not
just zero crossings, but also the change
in phase. Thus, it can resolve frequency
to the tens-of-millihertz level in 0.1
second. With this design, the ability to
resolve frequency changes is not limited
by the spectrum analyzer, but rather is
determined by the noisiness of the signal
being counted.
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39 | Keysight | Spectrum Analysis Basics – Application Note 150
More advantages of all-digital IF
We have already discussed a number
of advantages of signal analyzers with
all-digital IF: power/voltage/log video
filtering, high-resolution frequency
counting, log/linear switching of stored
traces, excellent shape factors, an
average-across-the display-point detector
mode, 160 RBWs, and of course, FFT or
swept processing. In spectrum analysis,
the filtering action of RBW filters causes
errors in frequency and amplitude
measurements that are a function of the
sweep rate. For a fixed level of these
errors, the all-digital IF’s linear phase RBW
filters allow faster sweep rates than analog
filters permit. The digital implementation
also allows well-known compensations
to frequency and amplitude readout,
permitting sweep rates typically twice
as fast as older analyzers and excellent
performance at even four times the sweep
speed. Keysight X-Series signal analyzers
can achieve over 50 times faster sweep
speeds (see Chapter 2 - Digital resolution
Digitally implemented logarithmic
amplification is very accurate. Typical
errors of the entire analyzer are much
smaller than the measurement uncertainty
with which the manufacturer proves the
log fidelity. The log fidelity on all digital IF
implementations is specified at ± 0.07 dB
for any level up to –20 dBm at the input
mixer of the analyzer. The range of the
log amp does not limit the log fidelity at
low levels, as it would be in an analog IF;
the range is only limited by noise around
–155 dBm at the input mixer. Because
of single-tone compression in upstream
circuits at higher powers, the fidelity
specification degrades to ± 0.13 dB for
signal levels down to –10 dBm at the input
mixer. By comparison, analog log amps are
usually specified with tolerances in the ± 1
dB region.
Other IF-related accuracies are improved
as well. The IF prefilter is analog and must
be aligned like an analog filter, so it is
subject to alignment errors, but it is much
better than most analog filters. With only
one stage to manufacture, that stage
can be made much more stable than the
4- and 5-stage filters of analog IF-based
spectrum analyzers. As a result, the gain
variations between RBW filters is held to
a specification of ± 0.03 dB for general
digital IF implementations, which is ten
times better than all-analog designs.
The accuracy of the IF bandwidth is
determined by settability limitations
in the digital part of the filtering and
calibration uncertainties in the analog
prefilter. Again, the prefilter is highly
stable and contributes only 20 percent
of the error that would exist with an RBW
made of five such stages. As a result, most
RBWs are within 2 percent of their stated
bandwidth, compared to 10 to 20 percent
specifications in analog-IF analyzers.
Bandwidth accuracy is important for
minimizing the inaccuracy of channel
power measurements and similar
measurements. The noise bandwidth
of the RBW filters is known to much
better specifications than the 2 percent
setting tolerance, and noise markers
and channel-power measurements are
corrected to a tolerance of ± 0.5 percent.
Therefore, bandwidth uncertainties
contribute only ± 0.022 dB to the
amplitude error of noise density and
channel-power measurements.
Finally, with no analog reference-leveldependent gain stages, there is no “IF
gain” error at all. The sum of all these
improvements means that the all-digital
IF makes a quantum improvement in
spectrum analyzer accuracy. It also allows
you to change analyzer settings without
significantly impacting measurement
uncertainty. We will cover this topic in
more detail in the next chapter.
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40 | Keysight | Spectrum Analysis Basics – Application Note 150
Chapter 4. Amplitude and Frequency Accuracy
Now let’s look at amplitude accuracy, or
perhaps better, amplitude uncertainty.
Most spectrum analyzers are specified
in terms of both absolute and relative
accuracy. However, relative performance
affects both, so let’s look first at
factors affecting relative measurement
Before we discuss these uncertainties,
let’s look again at the block diagram
of an analog swept-tuned spectrum
analyzer, shown in Figure 4-1, and see
which components contribute to the
uncertainties. Later in this chapter, we
will see how a digital IF and various
correction and calibration techniques
can substantially reduce measurement
Components that contribute to
– Input connector (mismatch)
– RF input attenuator
– Mixer and input filter (flatness)
– IF gain/attenuation (reference level)
– RBW filters
– Display scale fidelity
– Calibrator (not shown)
RF input
Impedance mismatch is an important
factor in measurement uncertainty that
is often overlooked. Analyzers do not
have perfect input impedances, and
signal sources do not have ideal output
impedances. When a mismatch exists,
the incident and reflected signal vectors
may add constructively or destructively.
Thus the signal received by the analyzer
can be larger or smaller than the original
signal. In most cases, uncertainty due to
mismatch is relatively small. However, as
spectrum analyzer amplitude accuracy
has improved dramatically in recent years,
mismatch uncertainty now constitutes a
more significant part of total measurement
uncertainty. In any case, improving the
match of either the source or analyzer
reduces uncertainty.
The general expression used to calculate
the maximum mismatch error in dB is:
Error (dB) = –20 log[1 ± |(ρanalyzer)(ρsource)|]
Spectrum analyzer data sheets typically
specify the input voltage standing wave
ratio (VSWR). Knowing the VSWR, we can
calculate ρ with the following equation:
As an example, consider a spectrum
analyzer with an input VSWR of 1.2 and
a device under test (DUT) with a VSWR
of 1.4 at its output port. The resulting
mismatch error would be ±0.13 dB.
More information
For more information about how
improving the match of either
the source or analyzer reduces
uncertainty, see the Keysight PSA
Performance Spectrum Analyzer Series
Amplitude Accuracy – Technical Overview
literature number 5980-3080EN.
where ρ is the reflection coefficient.
IF gain
IF filter
Pre-selector, or
low-pass filter
Figure 4-1. Spectrum analyzer block diagram
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41 | Keysight | Spectrum Analysis Basics – Application Note 150
Since the analyzer’s worst-case match
occurs when its input attenuator is set
to 0 dB, we should avoid the 0 dB setting
if we can. Alternatively, we can attach
a well-matched pad (attenuator) to
the analyzer input and greatly reduce
mismatch as a factor. Adding attenuation
is a technique that works well to reduce
measurement uncertainty when the
signal we wish to measure is well above
the noise. However, in cases where the
signal-to-noise ratio is small (typically ≤
7 dB), adding attenuation will increase
measurement error because the noise
power adds to the signal power, resulting
in an erroneously high reading.
Let’s turn our attention to the input
attenuator. Some relative measurements
are made with different attenuator
settings. In these cases, we must
consider the input attenuation switching
uncertainty. Because an RF input
attenuator must operate over the entire
frequency range of the analyzer, its step
accuracy varies with frequency. The
attenuator also contributes to the overall
frequency response. At 1 GHz, we expect
the attenuator performance to be quite
good; at 26 GHz, not as good.
The next component in the signal path
is the input filter. Spectrum analyzers
use a fixed low-pass filter in the low
band and a tunable bandpass filter
called a preselector (we will discuss the
preselector in more detail in Chapter 7) in
the higher frequency bands. The low-pass
filter has a better frequency response than
the preselector and adds a small amount
of uncertainty to the frequency response
error. A preselector, usually a YIG-tuned
filter, has a larger frequency response
variation, ranging from 1.5 dB to 3 dB at
millimeter-wave frequencies.
Following the input filter are the mixer and
the local oscillator, both of which add to the
frequency response uncertainty.
Figure 4-2 illustrates what the frequency
response might look like in one frequency
band. Frequency response is usually
specified as ± x dB relative to the midpoint
between the extremes. The frequency
response of a spectrum analyzer represents
the overall system performance resulting
from the flatness characteristics and
interactions of individual components in
the signal path up to and including the first
mixer. Microwave spectrum analyzers use
more than one frequency band to go above
3.6 GHz. This is done by using a higher
harmonic of the local oscillator, which will
be discussed in detail in Chapter 7. When
making relative measurements between
signals in different frequency bands, you
must add the frequency response of each
band to determine the overall frequency
response uncertainty. In addition, some
spectrum analyzers have a band switching
uncertainty which must be added to the
overall measurement uncertainty.
After the input signal is converted to an
IF, it passes through the IF gain amplifier
and IF attenuator, which are adjusted
to compensate for changes in the RF
attenuator setting and mixer conversion
loss. Input signal amplitudes are thus
referenced to the top line of the graticule
on the display, known as the reference
The IF amplifier and attenuator work
only at one frequency and, therefore, do
not contribute to frequency response.
However, some amplitude uncertainty is
always introduced and it depends on how
accurately the IF amplifier and attenuator
can be set to a desired value. This
uncertainty is known as reference level
Another parameter we might change
during the course of a measurement is
resolution bandwidth. Different filters
have different insertion losses. Generally,
we see the greatest difference when
switching between LC filters (typically
used for the wider resolution bandwidths)
and crystal filters (used for narrow
bandwidths). This results in resolution
bandwidth switching uncertainty.
The most common way to display
signals on a spectrum analyzer is to use
a logarithmic amplitude scale, such as
10 dB per div or 1 dB per div. Therefore,
the IF signal usually passes through a
log amplifier. The gain characteristic
of the log amplifier approximates a
logarithmic curve. So any deviation from
a perfect logarithmic response adds to
the amplitude uncertainty. Similarly, when
the spectrum analyzer is in linear mode,
the linear amplifiers do not have a perfect
linear response. This type of uncertainty is
called display scale fidelity.
Frequency response
Signals in the same harmonic band
+0.5 dB
- 0.5 dB
Specification: 0.5 dB
Figure 4-2. Relative frequency response in a single band
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42 | Keysight | Spectrum Analysis Basics – Application Note 150
Relative uncertainty
Absolute amplitude accuracy
When we make relative measurements on
an incoming signal, we use either some
part of the same signal or a different
signal as a reference. For example, when
we make second harmonic distortion
measurements, we use the fundamental
of the signal as our reference. Absolute
values do not come into play; we are
interested only in how the second
harmonic differs in amplitude from the
Almost all spectrum analyzers have a
built-in calibration source that provides
a known reference signal of specified
amplitude and frequency. We rely on
the relative accuracy of the analyzer
to translate the absolute calibration of
the reference to other frequencies and
amplitudes. Spectrum analyzers often
have an absolute frequency response
specification, where the zero point on
the flatness curve is referenced to this
calibration signal. Many Keysight spectrum
analyzers use a 50-MHz reference signal.
At this frequency, the specified absolute
amplitude accuracy is extremely good:
± 0.24 dB for the X-Series PXA signal
In a worst-case relative measurement
scenario, the fundamental of the signal
may occur at a point where the frequency
response is highest, while the harmonic
we wish to measure occurs at the point
where the frequency response is the
lowest. The opposite scenario is equally
likely. Therefore, if our relative frequency
response specification is ± 0.5 dB, as
shown in Figure 4-2, then the total
uncertainty would be twice that value, or
± 1.0 dB.
Perhaps the two signals under test are in
different frequency bands of the spectrum
analyzer. In that case, a rigorous analysis
of the overall uncertainty must include the
sum of the flatness uncertainties of the
two frequency bands.
It is best to consider all known
uncertainties and then determine which
ones can be ignored when making a
certain type of measurement. The range
of values shown in Table 4-1 represents
the specifications of a variety of spectrum
Some of the specifications, such as
frequency response, are frequency-range
dependent. A 3-GHz RF analyzer might
have a frequency response of ± 0.38 dB,
while a microwave spectrum analyzer
tuning in the 26-GHz range could
have a frequency response of ± 2.5
dB or higher. On the other hand, other
sources of uncertainty, such as changing
resolution bandwidths, apply equally to all
Table 4-1. Representative values of amplitude uncertainty for common spectrum analyzers
Amplitude uncertainties (± dB)
RF attenuator switching uncertainty
0.18 to 0.7
Frequency response
0.38 to 2.5
Reference level accuracy (IF attenuator/gain change)
0.0 to 0.7
Resolution bandwidth switching uncertainty
0.03 to 1.0
Display scale fidelity
0.07 to 1.15
Calibrator accuracy
0.24 to 0.34
Other uncertainties might be irrelevant in a
relative measurement, like RBW switching
uncertainty or reference level accuracy,
which apply to both signals at the same
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43 | Keysight | Spectrum Analysis Basics – Application Note 150
Improving overall uncertainty
When we look at total measurement
uncertainty for the first time, we may well
be concerned as we add up the uncertainty
figures. The worst-case view assumes each
source of uncertainty for your spectrum
analyzer is at the maximum specified value,
and all are biased in the same direction at
the same time. The sources of uncertainty
can be considered independent variables,
so it is likely that some errors will be
positive while others will be negative.
Therefore, a common practice is to
calculate the root sum of squares (RSS)
Regardless of whether we calculate the
worst-case or RSS error, we can take
steps to improve the situation. First of
all, we should know the specifications for
our particular spectrum analyzer. These
specifications may be good enough over
the range in which we are making our
measurement. If not, Table 4-1 suggests
some opportunities to improve accuracy.
Before taking any data, we can step
through a measurement to see if any
controls can be left unchanged. We
might find that the measurement can be
made without changing the RF attenuator
setting, resolution bandwidth or reference
level. If so, all uncertainties associated
with changing these controls drop out.
We may be able to trade off reference
level accuracy against display fidelity,
using whichever is more accurate and
eliminating the other as an uncertainty
factor. We can even get around frequency
response if we are willing to go to the
trouble of characterizing our particular
analyzer 2. You can accomplish this by
using a power meter and comparing the
reading of the spectrum analyzer at the
desired frequencies with the reading of the
power meter.
The same applies to the calibrator. If we
have a more accurate calibrator, or one
closer to the frequency of interest, we
may wish to use that in lieu of the built-in
Finally, many analyzers available today
have self-calibration routines. These
routines generate error coefficients (for
example, amplitude changes versus
resolution bandwidth) that the analyzer
later uses to correct measured data. As a
result, these self-calibration routines allow
us to make good amplitude measurements
with a spectrum analyzer and give us more
freedom to change controls during the
course of a measurement.
Specifications, typical
performance and nominal values
When evaluating spectrum analyzer
accuracy, it is important to have a clear
understanding of the many different values
found on an analyzer data sheet. Keysight
defines three classes of instrument
performance data:
Typical performance does not include
measurement uncertainty. During
manufacture, all instruments are tested
for typical performance parameters.
Nominal values indicate expected
performance or describe product
performance that is useful in the
application of the product, but is not
covered by the product warranty. Nominal
parameters generally are not tested
during the manufacturing process.
Digital IF architecture and
As described in the previous chapter,
a digital IF architecture eliminates or
minimizes many of the uncertainties
experienced in analog spectrum
analyzers. These include:
Specifications describe the performance
of parameters covered by the product
warranty over a temperature range of
0 to 55 °C (unless otherwise noted). Each
instrument is tested to verify it meets
the specification and takes into account
the measurement uncertainty of the
equipment used to test the instrument.
All of the units tested will meet the
Reference level accuracy (IF
gain uncertainty)
Some test equipment manufacturers use
a “2 sigma” or 95% confidence value for
certain instrument specifications. When
evaluating data sheet specifications for
instruments from different manufacturers,
it is important to make sure you are
comparing like numbers in order to make
an accurate comparison.
A digital IF architecture does not include
a log amplifier. Instead, the log function
is performed mathematically, and
traditional log fidelity uncertainty does
not exist. However, other factors, such
as RF compression (especially for input
signals above –20 dBm), ADC range gain
alignment accuracy and ADC linearity (or
quantization error) contribute to display
scale uncertainty. The quantization
error can be improved by the addition
of noise, which smoothes the average of
the ADC transfer function. This added
noise is called dither. While the dither
improves linearity, it does slightly degrade
the displayed average noise level. In the
X-Series signal analyzers, we generally
recommend you use dither when the
measured signal has a signal-to-noise
ratio of greater than or equal to 10 dB.
Typical performance describes additional
product performance information that
is not covered by the product warranty.
It is performance beyond specification
that 80% of the units exhibit with a 95%
confidence level over the temperature
range 20 to 30 °C.
Spectrum analyzers with an all-digital
IF, such as the Keysight X-Series, do not
have IF gain that changes with reference
level. Therefore, there is no IF gain
Display scale fidelity
Should we do so, then mismatch may become a more significant error.
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44 | Keysight | Spectrum Analysis Basics – Application Note 150
When the signal-to-noise ratio is less
than 10 dB, the degradations to accuracy
of any single measurement (in other
words, without averaging) that come from
a higher noise floor are worse than the
linearity problems solved by adding dither,
so dither is best turned off.
RBW switching uncertainty
The digital IF in the X-Series signal
analyzers includes an analog prefilter
set to 2.5 times the desired resolution
bandwidth. This prefilter has some
uncertainty in bandwidth, gain and center
frequency as a function of the RBW
setting. The rest of the RBW filtering is
done digitally in an ASIC in the digital IF
section. Though the digital filters are not
perfect, they are very repeatable, and
some compensation is applied to minimize
the error. This results in a tremendous
overall improvement to the RBW
switching uncertainty compared to analog
Amplitude uncertainty examples
Let’s look at some amplitude uncertainty
examples for various measurements.
Suppose we want to measure a 1-GHz
RF signal with an amplitude of –20 dBm.
If we use a Keysight PXA X-Series signal
analyzer with Atten = 10 dB, RBW = 1 kHz,
VBW = 1 kHz, Span = 20 kHz, Ref level =
–20 dBm, log scale, and coupled sweep
time, and an ambient temperature of 20
to 30 °C, the specifications tell us that
the absolute uncertainty equals ± 0.24 dB
plus the absolute frequency response. The
MXA X-Series signal analyzer measuring
the same signal using the same settings
would have a specified uncertainty
of ± 0.33 plus the absolute frequency
response. These values are summarized in
Table 4-2.
At higher frequencies, the uncertainties
get larger. In this example, we want
to measure a 10-GHz signal with an
amplitude of –10 dBm. In addition, we also
want to measure its second harmonic at
20 GHz.
Table 4-2. Amplitude uncertainties when measuring a 1-GHz signal
Source of uncertainty
Absolute uncertainty of 1-GHz, –20-dBm signal
N9030A PXA
N9020A MXA
N9010A EXA
Absolute amplitude accuracy
± 0.24 dB
± 0.33 dB
± 0.40 dB
Frequency response
± 0.35 dB
± 0.45 dB
± 0.60 dB
Total worst-case uncertainty
± 0.59 dB
± 0.78 dB
± 1.00 dB
Total RSS uncertainty
± 0.42 dB
± 0.56 dB
± 0.72 dB
Table 4-3. Absolute and relative amplitude accuracy comparison (8563EC and N9030A PXA)
Source of uncertainty
Measurement of a 10-GHz signal at –10 dBm
Absolute uncertainty of
fundamental at 10 GHz
Relative uncertainty of second
harmonic at 20 GHz
N9030A PXA
N9030A PXA
± 0.3 dB
Absolute amplitude
± 0.24 dB
Frequency response
± 2.9 dB
± 2.0 dB
± (2.2 + 2.5) dB
± (2.0 + 2.0) dB
Band switching
± 1.0 dB
IF gain
RBW switching
± 0.03 dB
Display scale fidelity
± 0.07 dB
± 0.85 dB
± 0.07 dB
Total worst-case
± 3.20 dB
± 2.34 dB
± 6.55 dB
± 4.07 dB
Total RSS uncertainty
± 2.91 dB
± 2.02 dB
± 3.17 dB
± 2.83 dB
Assume the following measurement
conditions: 0 to 55 °C, RBW = 300 kHz,
Atten = 10 dB, Ref level = –10 dBm. In
Table 4-3, we compare the absolute and
relative amplitude uncertainty of two
different Keysight spectrum and signal
analyzers, an 8563EC (with analog IF) and
N9030A PXA (with digital IF).
Frequency accuracy
So far, we have focused almost exclusively
on amplitude measurements. What about
frequency measurements? Again, we can
classify two broad categories, absolute
and relative frequency measurements.
Absolute measurements are used to
measure the frequencies of specific
signals. For example, we might want to
measure a radio broadcast signal to verify
it is operating at its assigned frequency.
Absolute measurements are also used
to analyze undesired signals, such as
when you search for spurs. Relative
measurements, on the other hand,
are useful for discovering the distance
between spectral components or the
modulation frequency.
Up until the late 1970s, absolute
frequency uncertainty was measured in
megahertz because the first LO was a
high-frequency oscillator operating above
the RF range of the analyzer, and there
was no attempt to tie the LO to a more
accurate reference oscillator. Today’s
LOs are synthesized to provide better
accuracy. Absolute frequency uncertainty
is often described under the frequency
readout accuracy specification and
refers to center frequency, start, stop and
marker frequencies.
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45 | Keysight | Spectrum Analysis Basics – Application Note 150
With the introduction of the Keysight
8568A in 1977, counter-like frequency
accuracy became available in a generalpurpose spectrum analyzer, and ovenized
oscillators were used to reduce drift. Over
the years, crystal reference oscillators with
various forms of indirect synthesis have
been added to analyzers in all cost ranges.
The broadest definition of indirect synthesis
is that the frequency of the oscillator
in question is in some way determined
by a reference oscillator. This includes
techniques such as phase lock, frequency
discrimination and counter lock.
We also need to consider the temperature
stability, as it can be worse than the
drift rate. In short, there are a number
of factors to consider before we can
determine frequency uncertainty.
What we care about is the effect these
changes have had on frequency accuracy
(and drift). A typical readout accuracy
might be stated:
When you make relative measurements,
span accuracy comes into play. For
Keysight analyzers, span accuracy
generally means the uncertainty in the
indicated separation of any two spectral
components on the display. For example,
suppose span accuracy is 0.5% of span
and we have two signals separated by
two divisions in a 1-MHz span (100 kHz
per division). The uncertainty of the signal
separation would be 5 kHz. The uncertainty
would be the same if we used delta markers
and the delta reading was 200 kHz. So we
would measure 200 kHz ± 5 kHz.
± [(freq readout x freq ref error) + A% of
span + B% of RBW + C Hz]
Note that we cannot determine an exact
frequency error unless we know something
about the frequency reference. In most
cases, we are given an annual aging
rate, such as ± 1 x 10 –7 per year, though
sometimes aging is given over a shorter
period (for example, ± 5 x 10 –10 per day).
In addition, we need to know when the
oscillator was last adjusted and how
close it was set to its nominal frequency
(usually 10 MHz). Other factors that we
often overlook when we think about
frequency accuracy include how long the
reference oscillator has been operating.
Many oscillators take 24 to 72 hours to
reach their specified drift rate. To minimize
this effect, some spectrum analyzers
continue to provide power to the reference
oscillator as long as the instrument is
plugged into the AC power line. In this
case, the instrument is not really turned
“off.” It is more accurate to say it is on
In a factory setting, there is often an
in-house frequency standard available
that is traceable to a national standard.
Most analyzers with internal reference
oscillators allow you to use an external
reference. The frequency reference error
in the foregoing expression then becomes
the error of the in-house standard.
When making measurements in the field,
we typically want to turn our analyzer on,
complete our task, and move on as quickly
as possible. It is helpful to know how the
reference in our analyzer behaves under
short warm-up conditions. For example,
the Keysight ESA-E Series portable
spectrum analyzers will meet published
specifications after a 5-minute warm up.
Most analyzers offer markers you can put
on a signal to see amplitude and absolute
However, the indicated frequency of the
marker is a function of the frequency
calibration of the display, the location of
the marker on the display and the number
of display points selected. Also, to get
the best frequency accuracy, we must
be careful to place the marker exactly at
the peak of the response to a spectral
component. If we place the marker at
some other point on the response, we will
get a different frequency reading. For the
best accuracy, we may narrow the span
and resolution bandwidth to minimize
their effects and to make it easier to place
the marker at the peak of the response.
Many analyzers have marker modes
that include internal counter schemes
to eliminate the effects of span and
resolution bandwidth on frequency
accuracy. The counter does not count the
input signal directly, but instead counts
the IF signal and perhaps one or more
of the LOs, and the processor computes
the frequency of the input signal. A
minimum signal-to-noise ratio is required
to eliminate noise as a factor in the
count. Counting the signal in the IF also
eliminates the need to place the marker
at the exact peak of the signal response
on the display. If you are using this marker
counter function, placement anywhere
near the peak of the signal sufficiently
out of the noise will do. Marker count
accuracy might be stated as:
± [(marker freq x freq ref error)
+ counter resolution]
We must still deal with the frequency
reference error, as we previously
discussed. Counter resolution refers to
the least-significant digit in the counter
readout, a factor here just as with any
simple digital counter. Some analyzers
allow you to use the counter mode with
delta markers. In that case, the effects of
counter resolution and the fixed frequency
would be doubled.
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46 | Keysight | Spectrum Analysis Basics – Application Note 150
Chapter 5. Sensitivity and Noise
One of the primary ways engineers use
spectrum analyzers is for searching out
and measuring low-level signals. The
limitation in these measurements is the
noise generated within the spectrum
analyzer itself. This noise, generated by
the random electron motion in various
circuit elements, is amplified by multiple
gain stages in the analyzer and appears
on the display as a noise signal. On a
spectrum analyzer, this noise is commonly
referred to as the displayed average noise
level, or DANL1. The noise power observed
in the DANL is a combination of thermal
noise and the noise figure of the spectrum
analyzer. While there are techniques to
measure signals slightly below the DANL,
this noise power ultimately limits our
ability to make measurements of low-level
Let’s assume a 50-ohm termination
is attached to the spectrum analyzer
input to prevent any unwanted signals
from entering the analyzer. This passive
termination generates a small amount of
noise energy equal to kTB, where:
k = Boltzmann’s constant
(1.38 x 10 –23 joule/K)
T = temperature, in Kelvin
B = bandwidth in which the noise is
measured, in Hertz
The total noise power is a function of
measurement bandwidth, so the value is
typically normalized to a 1-Hz bandwidth.
Therefore, at room temperature, the noise
power density is –174 dBm/Hz. When this
noise reaches the first gain stage in the
analyzer, the amplifier boosts the noise,
plus adds some of its own.
As the noise signal passes on through
the system, it is typically high enough
in amplitude that the noise generated in
subsequent gain stages adds only a small
amount to the total noise power. The
input attenuator and one or more mixers
may be between the input connector of
a spectrum analyzer and the first stage
of gain, and all of these components
generate noise. However, the noise
they generate is at or near the absolute
minimum of –174 dBm/Hz, so they do not
significantly affect the noise level input to
the first gain stage, and its amplification is
typically insignificant.
While the input attenuator, mixer and
other circuit elements between the input
connector and first gain stage have little
effect on the actual system noise, they
do have a marked effect on the ability of
an analyzer to display low-level signals
because they attenuate the input signal.
That is, they reduce the signal-to-noise
ratio and so degrade sensitivity.
We can determine the DANL simply by
noting the noise level indicated on the
display when the spectrum analyzer input
is terminated with a 50-ohm load. This
level is the spectrum analyzer’s own noise
floor. Signals below this level are masked
by the noise and cannot be seen. However,
the DANL is not the actual noise level at
the input, but rather the effective noise
level. An analyzer display is calibrated
to reflect the level of a signal at the
analyzer input, so the displayed noise floor
represents a fictitious or effective noise
floor at the input.
The actual noise level at the input is a
function of the input signal. Indeed, noise
is sometimes the signal of interest. Like
any discrete signal, a noise signal is much
easier to measure when it is well above
the effective (displayed) noise floor. The
effective input noise floor includes the
losses caused by the input attenuator,
mixer conversion loss, and other circuit
elements prior to the first gain stage. We
cannot do anything about the conversion
loss of the mixers, but we can change
the RF input attenuator. This enables us
to control the input signal power to the
first mixer and thus change the displayed
signal-to-noise floor ratio. Clearly, we get
the lowest DANL by selecting minimum
(zero) RF attenuation.
Because the input attenuator has no
effect on the actual noise generated in the
system, some early spectrum analyzers
simply left the displayed noise at the
same position on the display regardless of
the input attenuator setting. That is, the
IF gain remained constant. In this case,
the input attenuator affected the location
of a true input signal on the display. As
input attenuation was increased, further
attenuating the input signal, the location
of the signal on the display went down
while the noise remained stationary.
1. Displayed average noise level is sometimes confused with the term “sensitivity.” While related, these terms have different meanings. Sensitivity is a
measure of the minimum signal level that yields a defined signal-to-noise ratio (SNR) or bit error rate (BER). It is a common metric of radio receiver
performance. Spectrum analyzer specifications are always given in terms of the DANL.
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47 | Keysight | Spectrum Analysis Basics – Application Note 150
Beginning in the late 1970s, spectrum
analyzer designers took a different
approach. In newer analyzers, an internal
microprocessor changes the IF gain to
offset changes in the input attenuator.
Thus, signals present at the analyzer’s
input remain stationary on the display
as we change the input attenuator, while
the displayed noise moves up and down.
In this case, the reference level remains
unchanged, as shown in Figure 5-1. As
the attenuation increases from 5 to 15
to 25 dB, the displayed noise rises while
the –30-dBm signal remains constant.
In either case, we get the best signal-tonoise ratio by selecting minimum input
Resolution bandwidth also affects
signal-to-noise ratio, or sensitivity. The
noise generated in the analyzer is random
and has a constant amplitude over a wide
frequency range. Since the resolution, or
IF, bandwidth filters come after the first
gain stage, the total noise power that
passes through the filters is determined
by the width of the filters. This noise signal
is detected and ultimately reaches the
display. The random nature of the noise
signal causes the displayed level to vary as:
Figure 5-1. In modern signal analyzers, reference levels remain constant when you change input attenuation
10 log (BW2 /BW1)
BW1 = starting resolution bandwidth
BW = ending resolution bandwidth
So if we change the resolution bandwidth
by a factor of 10, the displayed noise level
changes by 10 dB, as shown in Figure
5-2. For continuous wave (CW) signals,
we get best signal-to-noise ratio, or best
sensitivity, using the minimum resolution
bandwidth available in our spectrum
analyzer 2.
Figure 5-2. Displayed noise level changes as 10 log (BW2 /BW1 )
Broadband, pulsed signals can exhibit the opposite behavior, where the SNR increases as the bandwidth gets larger.
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48 | Keysight | Spectrum Analysis Basics – Application Note 150
A spectrum analyzer displays signal plus
noise, and a low signal-to-noise ratio
makes the signal difficult to distinguish.
We noted previously that the video filter
can be used to reduce the amplitude
fluctuations of noisy signals without
affecting constant signals. Figure 5-3
shows how the video filter can improve
our ability to discern low-level signals.
The video filter does not affect the
average noise level and so does not, by
this definition, affect the sensitivity of an
In summary, we get best sensitivity for
narrowband signals by selecting the
minimum resolution bandwidth and
minimum input attenuation. These settings
give us the best signal-to-noise ratio. We
can also select minimum video bandwidth
to help us see a signal at or close to the
noise level 3. Of course, selecting narrow
resolution and video bandwidths does
lengthen the sweep time.
Noise floor extension
While lowering an analyzer’s inherent
noise floor through hardware design
and component choices is obviously
beneficial for dynamic range, there are
practical limits, and another approach
offers significant improvement. With
sufficient processing and other technical
innovations, the noise power in a signal
analyzer can be modeled and subtracted
from measurement results to reduce the
effective noise level. In the Keysight PXA
signal analyzer this operation is called
noise floor extension (NFE).
Generally, if you can accurately identify
the noise power contribution of an
analyzer, you can subtract this power from
various kinds of spectrum measurements.
Examples include signal power or band
power, ACPR, spurious, phase noise,
harmonic and intermodulation distortion.
Noise subtraction techniques do not
improve the performance of vector analysis
operations such as demodulation or
time-domain displays of signals.
Figure 5-3. Video filtering makes low-level signals more discernible
Keysight has been demonstrating noise
subtraction capability for some time, using
trace math in vector signal analyzers to
remove analyzer noise from spectrum and
band power measurements. (Similar trace
math is available in the Keysight X-Series
signal analyzers.)
3. For the effect of noise on accuracy, see “Dynamic range versus measurement uncertainty” in
Chapter 6.
This capability is effective, though
somewhat inconvenient. It involves
disconnecting the signal from the analyzer,
measuring analyzer noise level with a
large amount of averaging, reconnecting
the signal and using trace math to display
a corrected result. It is necessary to remeasure the analyzer noise power every
time the analyzer configuration (frequency
center/span, attenuator/input range,
resolution bandwidth) changed.
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49 | Keysight | Spectrum Analysis Basics – Application Note 150
The Keysight PXA analyzers dramatically
improve this measurement technique for
many measurement situations. Critical
parameters that determine the analyzer’s
noise floor are measured when it is
calibrated, and these parameters are used
(with current measurement information
such as analyzer temperature) to fully
model the analyzer’s noise floor, including
changes in analyzer configuration and
operating conditions. The analyzer’s noise
power contribution is then automatically
subtracted from spectrum and power
measurements. This process in the PXA is
called noise floor extension and is enabled
with a keystroke in the Mode Setup menu.
An example is shown in Figure 5-4.
The effectiveness of NFE can be expressed
in several ways. Average noise power in
the display (DANL) is usually reduced by
10 to 12 dB in the analyzer’s low band
(below 3.6 GHz) and about 8 dB in its high
band (above 3.6 GHz). While the apparent
noise level will be reduced, only the
analyzer’s noise power is being subtracted.
Therefore, the apparent power of signals in
the display will be reduced if the analyzer’s
noise power is a significant part of their
power, and not otherwise.
Thus measurements of both discrete
signals and the noise floor of signal
sources connected to the PXA are
more accurately measured with NFE
enabled. NFE works with all spectrum
measurements regardless of RBW or VBW,
and it also works with any type of detector
or averaging.
More information
For more information on using noise
floor extension, please refer to, Using
Noise Floor Extension in the PXA Signal
Analyzer – Application Note, literature
number 5990-5340EN.
Figure 5-4. Noise floor extension view of harmonics
Noise figure
Many receiver manufacturers specify the
performance of their receivers in terms of
noise figure, rather than sensitivity. We
will show you how the two can be equated.
A spectrum analyzer is a receiver, and we
will examine noise figure on the basis of a
sinusoidal input.
Noise figure can be defined as the
degradation of signal-to-noise ratio
as a signal passes through a device, a
spectrum analyzer in our case. We can
express noise figure as:
F =
Si /Ni
F = noise figure as power ratio (also known
as noise factor)
Si = input signal power
Ni = true input noise power
So = output signal power
No = output noise power
We can simplify this expression for our
spectrum analyzer. First of all, the output
signal is the input signal times the gain
of the analyzer. Second, the gain of our
analyzer is unity because the signal level
at the output (indicated on the display)
is the same as the level at the input
(input connector). So our expression,
after substitution, cancellation and
rearrangement, becomes:
F = No/Ni
This expression tells us that all we need
to do to determine the noise figure is
compare the noise level as read on the
display to the true (not the effective) noise
level at the input connector. Noise figure
is usually expressed in terms of dB, or:
NF = 10 log(F) = 10 log(No) – 10 log(Ni).
We use the true noise level at the input,
rather than the effective noise level,
because our input signal-to-noise ratio
was based on the true noise. As we saw
earlier, when the input is terminated in
50 ohms, the kTB noise level at room
temperature in a 1-Hz bandwidth is
–174 dBm.
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50 | Keysight | Spectrum Analysis Basics – Application Note 150
We know the displayed level of noise on
the analyzer changes with bandwidth.
So all we need to do to determine the
noise figure of our spectrum analyzer
is to measure the noise power in some
bandwidth, calculate the noise power
that we would have measured in a 1-Hz
bandwidth using 10 log(BW2 /BW1), and
compare that to –174 dBm.
For example, if we measured –110 dBm in
a 10-kHz resolution bandwidth, we would
NF = [measured noise in dBm] –
10 log(RBW/1) – kTBB=1 Hz
–110 dBm –10 log(10,000/1) – (–174 dBm)
–110 – 40 + 174
= 24 dB
Noise figure is independent of bandwidth4.
Had we selected a different resolution
bandwidth, our results would have been
exactly the same. For example, had we
chosen a 1-kHz resolution bandwidth,
the measured noise would have been
–120 dBm and 10 log(RBW/1) would have
been 30. Combining all terms would have
given –120 – 30 + 174 = 24 dB, the same
noise figure as above.
The 24- dB noise figure in our example
tells us that a sinusoidal signal must
be 24 dB above kTB to be equal to the
displayed average noise level on this
particular analyzer. Thus we can use
noise figure to determine the DANL for a
given bandwidth or to compare DANLs
of different analyzers with the same
One reason for introducing noise figure
is that it helps us determine how much
benefit we can derive from the use of a
preamplifier. A 24-dB noise figure, while
good for a spectrum analyzer, is not so
good for a dedicated receiver.
However, by placing an appropriate
preamplifier in front of the spectrum
analyzer, we can obtain a system
(preamplifier/spectrum analyzer) noise
figure lower than that of the spectrum
analyzer alone. To the extent that we
lower the noise figure, we also improve the
system sensitivity.
When we introduced noise figure in the
previous discussion, we did so on the
basis of a sinusoidal input signal. We can
examine the benefits of a preamplifier on
the same basis. However, a preamplifier
also amplifies noise, and this output noise
can be higher than the effective input noise
of the analyzer. In the “Noise as a signal”
section later in this chapter, you will see
how a spectrum analyzer using log power
averaging displays a random noise signal
2.5 dB below its actual value. As we explore
preamplifiers, we shall account for this
2.5 dB factor where appropriate.
Rather than develop a lot of formulas
to see what benefit we get from a
preamplifier, let us look at two extreme
cases and see when each might apply.
First, if the noise power out of the
preamplifier (in a bandwidth equal to that
of the spectrum analyzer) is at least 15 dB
higher than the DANL of the spectrum
analyzer, then the sensitivity of the system
is approximately that of the preamplifier,
less 2.5 dB. How can we tell if this is the
case? Simply connect the preamplifier to
the analyzer and note what happens to the
noise on the display. If it goes up 15 dB or
more, we have fulfilled this requirement.
On the other hand, if the noise power
out of the preamplifier (again, in the
same bandwidth as that of the spectrum
analyzer) is 10 dB or more lower than
the displayed average noise level on the
analyzer, the noise figure of the system is
that of the spectrum analyzer less the gain
of the preamplifier. Again we can test by
inspection. Connect the preamplifier to the
analyzer; if the displayed noise does not
change, we have fulfilled the requirement.
Testing by experiment means we must
have the equipment at hand. We do not
need to worry about numbers. We simply
connect the preamplifier to the analyzer,
note the average displayed noise level and
subtract the gain of the preamplifier. Then
we have the sensitivity of the system.
However, we really want to know ahead of
time what a preamplifier will do for us. We
can state the two cases above as follows:
If NFpre + Gpre ≥ NFSA + 15 dB,
Then NFsys = NFpre – 2.5 dB
If NFpre + Gpre ≤ NFSA – 10 dB,
Then NFsys
= NFSA – Gpre
Using these expressions, we’ll see how
a preamplifier affects our sensitivity.
Assume that our spectrum analyzer
has a noise figure of 24 dB and the
preamplifier has a gain of 36 dB and a
noise figure of 8 dB. All we need to do is
to compare the gain plus noise figure of
the preamplifier to the noise figure of the
spectrum analyzer. The gain plus noise
figure of the preamplifier is 44 dB, more
than 15 dB higher than the noise figure of
the spectrum analyzer, so the sensitivity
of the preamplifier/spectrum-analyzer
combination is that of the preamplifier,
less 2.5 dB. In a 10 kHz resolution
bandwidth, our preamplifier/analyzer
system has a sensitivity (displayed
average noise level, DANL) of:
kTBB=1 + 10log(NBW/1Hz) + NFSYS +
In this expression, kTB = −174 dBm/Hz, so
kTBB=1 is −174 dBm. The noise bandwidth
(NBW) for typical digital RBW’s is 0.2 dB
wider than the RBW, thus 40.2 dB. The
noise figure of the system is 8 dB. The
LogCorrectionFactor is −2.5 dB. So the
sensitivity is −128.3 dBm.
4. This may not always be precisely true for a given analyzer because of the way resolution bandwidth filter sections and gain are distributed in the IF chain.
5. The noise figure computed in this manner cannot be directly compared to that of a receiver because the “measured noise” term in the equation understates
the actual noise by 2.5 dB. See the section titled “Noise as a signal” later in this chapter.
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51 | Keysight | Spectrum Analysis Basics – Application Note 150
This is an improvement of 18.3 dB
over the –110 dBm noise floor without
the preamplifier.
However, there might be a drawback to
using this preamplifier, depending upon
our ultimate measurement objective. If
we want the best sensitivity but no loss of
measurement range, this preamplifier is
not the right choice. Figure 5-5 illustrates
this point. A spectrum analyzer with a
24-dB noise figure will have an average
displayed noise level of –110 dBm in a
10-kHz resolution bandwidth. If the 1-dB
compression point 6 for that analyzer is
0 dBm, the measurement range is 110 dB.
When we connect the preamplifier, we
must reduce the maximum input to the
system by the gain of the preamplifier to
–36 dBm. However, when we connect the
preamplifier, the displayed average noise
level will rise by about 17.5 dB because
the noise power out of the preamplifier is
that much higher than the analyzer’s own
noise floor, even after accounting for the
2.5 dB factor. It is from this higher noise
level that we now subtract the gain of
the preamplifier. With the preamplifier in
place, our measurement range is 92.5 dB,
17.5 dB less than without the preamplifier.
The loss in measurement range equals the
change in the displayed noise when the
preamplifier is connected.
Finding a preamplifier that will give us
better sensitivity without costing us
measurement range dictates that we must
meet the second of the above criteria;
that is, the sum of its gain and noise figure
must be at least 10 dB less than the noise
figure of the spectrum analyzer. In this
case, the displayed noise floor will not
change noticeably when we connect the
preamplifier, so although we shift the
whole measurement range down by the
gain of the preamplifier, we end up with
Interestingly enough, we can use the
input attenuator of the spectrum analyzer
to effectively degrade the noise figure (or
reduce the gain of the preamplifier, if you
prefer). For example, if we need slightly
better sensitivity but cannot afford to
give up any measurement range, we can
use the above preamplifier with 30 dB
of RF input attenuation on the spectrum
Spectrum analyzer and preamplifier
Spectrum analyzer
0 dBm
the same overall range we started with.
To choose the correct preamplifier, we
must look at our measurement needs.
If we want absolutely the best
sensitivity and are not concerned about
measurement range, we would choose a
high-gain, low-noise-figure preamplifier
so that our system would take on the noise
figure of the preamplifier, less 2.5 dB. If we
want better sensitivity but cannot afford to
give up any measurement range, we must
choose a lower-gain preamplifier.
1 dB compression
System 1 dB compression
–36 dBm
110 dB spectrum
analyzer range
92.5 dB
–110 dBm
System sensitivity
–92.5 dBm
–128.5 dBm
Figure 5-5. If displayed noise goes up when a preamplifier is connected, measurement range is diminished by the amount the noise changes
6. See the section titled “Mixer compression” in Chapter 6.
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52 | Keysight | Spectrum Analysis Basics – Application Note 150
This attenuation increases the noise
figure of the analyzer from 24 to 54 dB.
Now the gain plus noise figure of the
preamplifier (36 + 8) is 10 dB less than
the noise figure of the analyzer, and we
have met the conditions of the second
criterion above.
The noise figure of the system is now:
NFSA – Gpre + 3 dB
NFpre + 3 dB
NFSA – Gpre + 2 dB
NFpre + 2 dB
System noise
NFSA – Gpre + 1 dB
Figure (dB)
NFpre + 1 dB
NFSA – Gpre
= 54 dB – 36 dB
= 18 dB
This represents a 6-dB improvement over
the noise figure of the analyzer alone with
0 dB of input attenuation. So we have
improved sensitivity by 6 dB and given up
virtually no measurement range.
Of course, there are preamplifiers that
fall in between the extremes. Figure 5-6
enables us to determine system noise
figure from a knowledge of the noise
figures of the spectrum analyzer and
preamplifier and the gain of the amplifier.
We enter the graph of Figure 5-6 by
determining NFPRE + GPRE – NFSA . If
the value is less than zero, we find the
corresponding point on the dashed curve
and read system noise figure as the left
ordinate in terms of dB above NFSA – GPRE.
If NFPRE + GPRE – NFSA is a positive value,
we find the corresponding point on the
solid curve and read system noise figure
as the right ordinate in terms of dB above
Let’s first test the two previous extreme
As NFPRE + GPRE – NFSA becomes less
than –10 dB, we find that system noise
figure asymptotically approaches
NFSA – GPRE. As the value becomes
greater than +15 dB, system noise figure
asymptotically approaches NFPRE less
2.5 dB.
NFpre – 1 dB
NFpre – 2 dB
+5 +10
NFpre + Gpre – NFSA (dB)
NFpre – 2.5 dB
Figure 5-6. System noise figure for sinusoidal signals
Next, let’s try two numerical examples.
Above, we determined that the noise
figure of our analyzer is 24 dB. What would
the system noise figure be if we add a
Keysight 8447D amplifier, a preamplifier
with a noise figure of about 8 dB and a
gain of 26 dB? First, NFPRE + GPRE – NFSA
is +10 dB. From the graph of Figure 5-6
we find a system noise figure of about
NFPRE – 1.8 dB, or about 8 – 1.8 = 6.2 dB.
The graph accounts for the 2.5-dB factor.
On the other hand, if the gain of the
preamplifier is just 10 dB, then NFPRE +
GPRE – NFSA is –6 dB. This time the graph
indicates a system noise figure of NFSA –
GPRE + 0.6 dB, or 24 – 10 + 0.6 = 14.6 dB.
(We did not introduce the 2.5-dB factor
previously when we determined the noise
figure of the analyzer alone because we
read the measured noise directly from the
display. The displayed noise included the
2.5-dB factor.)
Many modern spectrum analyzers have
optional built-in preamplifiers available.
Compared to external preamplifiers, builtin preamplifiers simplify measurement
setups and eliminate the need for
additional cabling.
Measuring signal amplitude is much
more convenient with a built-in
preamplifier, because the preamplifier/
spectrum analyzer combination is
calibrated as a system, and amplitude
values displayed on screen are already
corrected for proper readout. With an
external preamplifier, you must correct
the spectrum analyzer reading with a
reference level offset equal to the preamp
gain. Most modern spectrum analyzers
allow you to enter the gain value of the
external preamplifier from the front
panel. The analyzer then applies this gain
offset to the displayed reference level
value, so you can directly view corrected
measurements on the display.
More information
For more details on noise
figure, see Fundamentals of
RF and Microwave Noise Figure Measurements – Application Note, literature
number 5952-8255E.
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53 | Keysight | Spectrum Analysis Basics – Application Note 150
Figure 5-7.
Random noise
has a Gaussian
Noise as a signal
So far, we have focused on the noise
generated within the measurement system
(analyzer or analyzer/preamplifier). We
described how the measurement system’s
displayed average noise level limits the
overall sensitivity. However, random
noise is sometimes the signal we want to
measure. Because of the nature of noise,
the superheterodyne spectrum analyzer
indicates a value that is lower than the
actual value of the noise. Let’s see why
this is so and how we can correct for it.
By random noise, we mean a signal
whose instantaneous amplitude has a
Gaussian distribution versus time, as
shown in Figure 5-7. For example, thermal
or Johnson noise has this characteristic.
Such a signal has no discrete spectral
components, so we cannot select some
particular component and measure it to
get an indication of signal strength. In
fact, we must define what we mean by
signal strength. If we sample the signal at
an arbitrary instant, we could theoretically
get any amplitude value. We need some
measure that expresses the noise level
averaged over time. Power, which is of
course proportionate to rms voltage,
satisfies that requirement.
We have already seen that both video
filtering and video averaging reduce the
peak-to-peak fluctuations of a signal and
can give us a steady value. We must equate
this value to either power or rms voltage.
The rms value of a Gaussian distribution
equals its standard deviation, σ.
Let’s start with our analyzer in the linear
display mode. The Gaussian noise at the
input is band limited as it passes through
the IF chain, and its envelope takes on
a Rayleigh distribution (Figure 5-8). The
noise we see on our analyzer display, the
output of the envelope detector, is the
Rayleigh-distributed envelope of the input
noise signal. To get a steady value, the mean
value, we use video filtering or averaging.
The mean value of a Rayleigh distribution is
1.253 σ.
Figure 5-8. The
envelope of
Gaussian noise
has a Rayleigh
However, our analyzer is a peakresponding voltmeter calibrated to
indicate the rms value of a sine wave. To
convert from peak to rms, our analyzer
scales its readout by 0.707 (–3 dB). The
mean value of the Rayleigh-distributed
noise is scaled by the same factor, giving
us a reading of 0.886 σ (l.05 dB below σ).
To equate the mean value displayed by the
analyzer to the rms voltage of the input
noise signal, we must account for the error
in the displayed value. Note, however,
that the error is not an ambiguity; it is a
constant error that we can correct for by
adding 1.05 dB to the displayed value.
In most spectrum analyzers, the display
scale (log or linear in voltage) controls
the scale on which the noise distribution
is averaged with either the VBW filter or
with trace averaging. Normally, we use
our analyzer in the log display mode, and
this mode adds to the error in our noise
The gain of a log amplifier is a function
of signal amplitude, so the higher noise
values are not amplified as much as the
lower values. As a result, the output of the
envelope detector is a skewed Rayleigh
distribution, and the mean value that we
get from video filtering or averaging is
another 1.45 dB lower. In the log mode,
then, the mean or average noise is
displayed 2.5 dB too low. Again, this error
is not an ambiguity, and we can correct
for it 7.
This is the 2.5-dB factor we accounted for
in the previous preamplifier discussion,
when the noise power out of the
preamplifier was approximately equal to or
greater than the analyzer’s own noise.
Another factor that affects noise
measurements is the bandwidth in
which the measurement is made. We
have seen how changing resolution
bandwidth affects the displayed level
of the analyzer’s internally generated
noise. Bandwidth affects external noise
signals in the same way. To compare
measurements made on different
analyzers, we must know the bandwidths
used in each case.
7. In X-Series analyzers, the averaging can be set to video, voltage or power (rms), independent of display scale. When using power averaging, no correction is
needed, since the average rms level is determined by the square of the magnitude of the signal, not by the log or envelope of the voltage.
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54 | Keysight | Spectrum Analysis Basics – Application Note 150
Not only does the 3-dB (or 6-dB)
bandwidth of the analyzer affect the
measured noise level, the shape of the
resolution filter also plays a role. To
make comparisons possible, we define
a standard noise-power bandwidth: the
width of a rectangular filter that passes
the same noise power as our analyzer’s
filter. For the near-Gaussian filters
in Keysight analyzers, the equivalent
noise-power bandwidth is about 1.05 to
1.13 times the 3-dB bandwidth, depending
on bandwidth selectivity. For example, a
10-kHz resolution bandwidth filter has a
noise-power bandwidth in the range of
10.5 to 11.3 kHz.
If we use 10 log(BW2 /BW1) to adjust
the displayed noise level to what we
would have measured in a noise-power
bandwidth of the same numeric value
as our 3-dB bandwidth, we find that the
adjustment varies from:
10 log(10,000/10,500) = –0.21 dB
10 log(10,000/11,300) = –0.53 dB
In other words, if we subtract something
between 0.21 and 0.53 dB from the
indicated noise level, we have the noise
level in a noise-power bandwidth that
is convenient for computations. For the
following examples, we will use 0.5 dB as a
reasonable compromise for the bandwidth
Let’s consider the various correction
factors to calculate the total correction for
each averaging mode:
Linear (voltage) averaging:
Rayleigh distribution (linear mode): 1.05 dB
3-dB/noise power bandwidths: –0.50 dB
Total correction:
0.55 dB
Log averaging:
Logged Rayleigh distribution: 2.50 dB
3-dB/noise power bandwidths: –0.50 dB
Total correction:
2.00 dB
NF SA – G pre + 3 dB
NF pre + 3 dB
System noise
NF SA – G pre + 2 dB
Figure (dB)
NF pre + 2 dB
NF SA – G pre + 1 dB
NF pre + 1 dB
NF SA – G pre
NF pre + G pre – NF SA (dB)
NF pre
Figure 5-9. System noise figure for noise signals
Power (rms voltage) averaging:
Power distribution:
0.00 dB
3-dB/noise power bandwidths: –0.50 dB
Total correction:
–0.50 dB
Many of today’s microprocessorcontrolled analyzers allow us to activate
a noise marker. When we do so, the
microprocessor switches the analyzer into
the power (rms) averaging mode, computes
the mean value of a number of display
points about the marker 9, normalizes and
corrects the value to a 1-Hz noise-power
bandwidth and displays the normalized
The analyzer does the hard part. It is
easy to convert the noise-marker value
to other bandwidths. For example, if
we want to know the total noise in a
4-MHz communication channel, we add
10 log(4,000,000/1), or 66 dB to the
noise-marker value10.
Preamplifier for noise
Noise signals are typically low-level
signals, so we often need a preamplifier to
have sufficient sensitivity to measure them.
However, we must recalculate sensitivity
of our analyzer first. We previously defined
sensitivity as the level of a sinusoidal signal
that is equal to the displayed average noise
floor. Since the analyzer is calibrated to
show the proper amplitude of a sinusoid,
no correction for the signal was needed.
But noise is displayed 2.5 dB too low, so
an input noise signal must be 2.5 dB above
the analyzer’s displayed noise floor to be
at the same level by the time it reaches
the display. The input and internal noise
signals add to raise the displayed noise by
3 dB, a factor of two in power. So we can
define the noise figure of our analyzer for a
noise signal as:
NFSA(N) = (noise floor)dBm/RBW –
10 log(RBW/1) – kTBB=1 + 2.5 dB
If we use the same noise floor we
used previously, –110 dBm in a 10-kHz
resolution bandwidth, we get:
NFSA(N) = –110 dBm – 10 log(10,000/1) –
(–174 dBm) + 2.5 dB = 26.5 dB
As was the case for a sinusoidal signal,
NFSA(N) is independent of resolution
bandwidth and tells us how far above kTB
a noise signal must be to be equal to the
noise floor of our analyzer.
When we add a preamplifier to our
analyzer, the system noise figure and
sensitivity improve. However, we have
accounted for the 2.5-dB factor in our
definition of NFSA(N), so the graph of
system noise figure becomes that of Figure
5-9. We determine system noise figure for
noise the same way that we did previously
for a sinusoidal signal.
8. The X-Series analyzers specify noise power bandwidth accuracy to within 0.5% (± 0.022 dB).
9. For example, the X-Series analyzers compute the mean over half a division, regardless of the number of display points.
10. Most modern spectrum analyzers make this calculation even easier with the channel power function. You enter the integration bandwidth of the
channel and center the signal on the analyzer display. The channel power function then calculates the total signal power in the channel.
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55 | Keysight | Spectrum Analysis Basics – Application Note 150
Chapter 6. Dynamic Range
Dynamic range is generally thought of
as the ability of an analyzer to measure
harmonically related signals and the
interaction of two or more signals,
for example, to measure second- or
third-harmonic distortion or third-order
intermodulation. In dealing with such
measurements, remember that the input
mixer of a spectrum analyzer is a nonlinear
device, so it always generates distortion
of its own. The mixer is nonlinear for a
reason. It must be nonlinear to translate
an input signal to the desired IF. But the
unwanted distortion products generated
in the mixer fall at the same frequencies
as the distortion products we wish to
measure on the input signal.
So we might define dynamic range in
this way: it is the ratio, expressed in dB,
of the largest to the smallest signals
simultaneously present at the input of
the spectrum analyzer that allows
measurement of the smaller signal to a
given degree of uncertainty.
Notice that accuracy of the measurement
is part of the definition. In the following
examples, you will see how both internally
generated noise and distortion affect
Dynamic range versus
internal distortion
To determine dynamic range versus
distortion, we must first determine just
how our input mixer behaves. Most
analyzers, particularly those using
harmonic mixing to extend their tuning
range1, use diode mixers. (Other types
of mixers would behave similarly.) The
current through an ideal diode can be
expressed as:
i = Is(eqv/kT–1)
where IS = the diode’s saturation current
q = electron charge (1.60 x 10–19 C)
v = instantaneous voltage
k = Boltzmann’s constant
(1.38 x 10 –23 joule/K)
T= temperature in Kelvin
We can expand this expression into a
power series:
i = IS(k1v + k 2 v 2 + k 3 v 3 +...)
where k1 = q/kT
k 2 = k12 /2!
k 3 = k13 /3!, etc.
Let’s now apply two signals to the mixer.
One will be the input signal we wish to
analyze; the other, the local oscillator
signal necessary to create the IF:
v = VLO sin(ωLO t) + V1 sin(ω1t)
If we go through the mathematics, we
arrive at the desired mixing product that,
with the correct LO frequency, equals the
k2VLOV1 cos[(ωLO – ω1)t]
A k 2VLOV1 cos[(ωLO + ω1)t] term is also
generated, but in our discussion of the
tuning equation, we found that we want
the LO to be above the IF, so (ωLO + ω1) is
also always above the IF.
With a constant LO level, the mixer output
is linearly related to the input signal level.
For all practical purposes, this is true as
long as the input signal is more than 15 to
20 dB below the level of the LO. There are
also terms involving harmonics of the input
(3k3/4)VLOV12 sin(ωLO – 2 ω1)t,
(k4/8)VLOV13 sin(ωLO – 3ω1)t, etc.
These terms tell us that dynamic range due
to internal distortion is a function of the
input signal level at the input mixer. Let’s see
how this works, using as our definition of
dynamic range, the difference in dB between
the fundamental tone and the internally
generated distortion.
The argument of the sine in the first term
includes 2ω1, so it represents the second
harmonic of the input signal.
The level of this second harmonic is a
function of the square of the voltage of
the fundamental, V12. This fact tells us
that for every 1 dB we drop the level of
the fundamental at the input mixer, the
internally generated second harmonic
drops by 2 dB. See Figure 6-1. The second
term includes 3ω1, the third harmonic,
and the cube of the input-signal voltage,
V13. So a 1-dB change in the fundamental
at the input mixer changes the internally
generated third harmonic by 3 dB.
Distortion is often described by its order.
The order can be determined by noting
the coefficient associated with the signal
frequency or the exponent associated
with the signal amplitude. Thus secondharmonic distortion is second order and
third harmonic distortion is third order.
The order also indicates the change in
internally generated distortion relative to
the change in the fundamental tone that
created it.
Now let us add a second input signal:
v = VLO sin(ωLO t) + V1 sin(ω1t) + V2 sin(ω2t)
This time, when we go through the math
to find internally generated distortion, in
addition to harmonic distortion, we get:
(k4/8)VLOV12V2cos[ωLO – (2ω1 – ω2)]t,
(k4/8)VLOV1V22 cos[ωLO – (2ω2 – ω1)]t, etc.
These equations represent
intermodulation distortion, the interaction
of the two input signals with each other.
The lower distortion product, 2ω1 – ω2,
falls below ω1 by a frequency equal to the
difference between the two fundamental
tones, ω2 – ω1. The higher distortion
product, 2ω2 – ω1, falls above ω2 by the
same frequency. See Figure 6-1.
Once again, dynamic range is a function
of the level at the input mixer.
1. See Chapter 7, “Extending the Frequency Range.”
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56 | Keysight | Spectrum Analysis Basics – Application Note 150
The internally generated distortion changes
as the product of V12 and V2 in the first case,
of V1 and V22 in the second. If V1 and V2
have the same amplitude, the usual case
when testing for distortion, we can treat
their products as cubed terms (V13 or V23).
Thus, for every dB that we simultaneously
change the level of the two input signals,
there is a 3-dB change in the distortion
components, as shown in Figure 6-1.
This is the same degree of change that we
see for third harmonic distortion in Figure 6-1.
And in fact, this too, is third-order distortion.
In this case, we can determine the degree of
distortion by summing the coefficients of ω1
and ω2 (e.g., 2ω1 – 1ω2 yields 2 + 1 = 3) or the
exponents of V1 and V2.
All this says that dynamic range depends
upon the signal level at the mixer. How do
we know what level we need at the mixer for
a particular measurement? Most analyzer
data sheets include graphs to tell us how
dynamic range varies. However, if no graph
is provided, we can draw our own2.
We do need a starting point, and this we
must get from the data sheet. Let’s look at
second-order distortion first. Let’s assume
the data sheet says second-harmonic
distortion is 75 dB down for a signal
–40 dBm at the mixer. Because distortion
is a relative measurement, and, at least for
the moment, we are calling our dynamic
range the difference in dB between
fundamental tone or tones and the
internally generated distortion, we have
our starting point. Internally generated
second-order distortion is 75 dB down, so
we can measure distortion down 75 dB.
We plot that point on a graph whose axes
are labeled distortion (dBc) versus level
at the mixer (level at the input connector
minus the input-attenuator setting). See
Figure 6-2. What happens if the level at
the mixer drops to –50 dBm? As noted
in Figure 6-1, for every 1 dB change in
the level of the fundamental at the mixer
there is a 2 dB change in the internally
generated second harmonic.
D dB
D dB
2D dB
D dB
3D dB
3D dB
2w 1 – w 2
3D dB
2w 2 – w 1
Figure 6-1. Changing the level of fundamental tones at the mixer
But for measurement purposes, we are
interested only in the relative change, that
is, in what happened to our measurement
range. In this case, for every 1 dB the
fundamental changes at the mixer, our
measurement range also changes by 1 dB.
In our second-harmonic example, then,
when the level at the mixer changes from
–40 to –50 dBm, the internal distortion,
and thus our measurement range, changes
from –75 to –85 dBc. In fact, these
points fall on a line with a slope of 1 that
describes the dynamic range for any input
level at the mixer.
Sometimes third-order performance is
given as TOI (third-order intercept). This
is the mixer level at which the internally
generated third-order distortion would be
equal to the fundamental(s), or 0 dBc. This
situation cannot be realized in practice
because the mixer would be well into
saturation. However, from a mathematical
standpoint, TOI is a perfectly good data
point because we know the slope of the
line. So even with TOI as a starting point,
we can still determine the degree of
internally generated distortion at a given
mixer level.
We can construct a similar line for
third-order distortion. For example,
a data sheet might say third-order
distortion is –85 dBc for a level of
–30 dBm at this mixer. Again, this is our
starting point, and we would plot the
point shown in Figure 6-2. If we now
drop the level at the mixer to –40 dBm,
what happens? Referring again to Figure
6-1, we see that both third-harmonic
distortion and third-order intermodulation
distortion fall by 3 dB for every 1 dB that
the fundamental tone or tones fall. Again,
it is the difference that is important. If
the level at the mixer changes from –30
to –40 dBm, the difference between
fundamental tone or tones and internally
generated distortion changes by 20 dB. So
the internal distortion is –105 dBc. These
two points fall on a line with a slope of 2,
giving us the third-order performance for
any level at the mixer.
We can calculate TOI from data sheet
information. Because third-order dynamic
range changes 2 dB for every 1 dB change
in the level of the fundamental tone(s) at
the mixer, we get TOI by subtracting half of
the specified dynamic range in dBc from
the level of the fundamental(s):
TOI = Afund – d/2
where Afund = level of the fundamental in
d = difference in dBc (a negative
value) between fundamental and
Using the values from the previous
TOI = –30 dBm – (–85 dBc)/2 = +12.5 dBm
For more information on how to construct a dynamic range chart, see Optimizing Dynamic Range for Distortion Measurements – Keysight PSA
Performance Spectrum Analyzer Series Product Note, literature number 5980-3079EN.
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57 | Keysight | Spectrum Analysis Basics – Application Note 150
Attenuator test
–50 Nois
Another constraint on dynamic range is
the noise floor of our spectrum analyzer.
Going back to our definition of dynamic
range as the ratio of the largest to the
smallest signal we can measure, the
average noise of our spectrum analyzer
puts the limit on the smaller signal. So
dynamic range versus noise becomes
signal-to-noise ratio in which the signal is
the fundamental whose distortion we wish
to measure.
Figure 6-3.
Reducing resolution bandwidth
dynamic range
2n order
mixer levels
If we ignore measurement accuracy
considerations for a moment, the
best dynamic range will occur at the
intersection of the appropriate distortion
curve and the noise curve. Figure 6-2 tells
us that our maximum dynamic range for
Figure 6-2.
Dynamic range
versus distortion
and noise
Maximum 2nd order
dynamic range
Maximum 3rd order
dynamic range
–30 –20 –10
Mixer level (dBm)
We can easily plot noise on our dynamic
range chart. For example, suppose the
data sheet for our spectrum analyzer
specifies a displayed average noise
level of –110 dBm in a 10-kHz resolution
bandwidth. If our signal fundamental has
a level of –40 dBm at the mixer, it is 70 dB
above the average noise, so we have a
70-dB signal-to-noise ratio. For every 1 dB
we reduce the signal level at the mixer,
we lose 1 dB of signal-to-noise ratio. Our
noise curve is a straight line having a slope
of –1, as shown in Figure 6-2.
Understanding the distortion graph is
important, but we can use a simple test
to determine whether displayed distortion
components are true input signals or
internally generated signals. Change the
input attenuator. If the displayed value
of the distortion components remains
the same, the components are part of
the input signal. If the displayed value
changes, the distortion components are
generated internally or are the sum of
external and internally generated signals.
We continue changing the attenuator until
the displayed distortion does not change
and then complete the measurement.
–50 No
–60 Nois
e ( Hz B
2nd order
dynamic range improvement
3rd order
dynamic range improvement
–30 –20 –10
Mixer level (dBm)
distortion is 72.5 dB; for third-order
distortion, 81.7 dB. In practice, the
intersection of the noise and distortion
graphs is not a sharply defined point,
because noise adds to the CW-like
distortion products, reducing dynamic
range by 2 dB when you use the log power
scale with log scale averaging.
Figure 6-2 shows the dynamic range for
one resolution bandwidth. We certainly
can improve dynamic range by narrowing
the resolution bandwidth, but there is not
a one-to-one correspondence between the
lowered noise floor and the improvement
in dynamic range. For second-order
distortion, the improvement is one half the
change in the noise floor; for third-order
distortion, two-thirds the change in the
noise floor. See Figure 6-3.
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58 | Keysight | Spectrum Analysis Basics – Application Note 150
The final factor in dynamic range is the
phase noise on our spectrum analyzer
LO, and this affects only third-order
distortion measurements. For example,
suppose we are making a two-tone,
third-order distortion measurement on an
amplifier, and our test tones are separated
by 10 kHz. The third-order distortion
components will also be separated
from the test tones by 10 kHz. For this
measurement, we might find ourselves
using a 1-kHz resolution bandwidth.
Referring to Figure 6-3 and allowing for
a 10-dB decrease in the noise curve, we
would find a maximum dynamic range
of about 88 dB. Suppose however, that
our phase noise at a 10-kHz offset is
only –80 dBc. Then 80 dB becomes the
ultimate limit of dynamic range for this
measurement, as shown in Figure 6-4.
In summary, the dynamic range of a
spectrum analyzer is limited by three
factors: the distortion performance of the
input mixer, the broadband noise floor
(sensitivity) of the system and the phase
noise of the local oscillator.
Dynamic range versus
measurement uncertainty
In our previous discussion of amplitude
accuracy, we included only those items
listed in Table 4-1, plus mismatch. We did
not cover the possibility of an internally
generated distortion product (a sinusoid)
being at the same frequency as an
external signal we wished to measure.
However, internally generated distortion
components fall at exactly the same
frequencies as the distortion components
we wish to measure on external signals.
The problem is that we have no way of
knowing the phase relationship between
the external and internal signals. So we
can determine only a potential range of
Phase noise
(10 kHz offset)
Dynamic range
reduction due
to phase noise
Mixer level (dBm)
Figure 6-4. Phase noise can limit third-order intermodulation tests
Delta (dBc)
0 Maximum
–1 error (dB)
Uncertainty (in dB) = 20 log(l ± 10 d/20)
where d = difference in dB between
the larger and smaller sinusoid
(a negative number)
Figure 6-5. Uncertainty versus difference in amplitude between two sinusoids at the
same frequency
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59 | Keysight | Spectrum Analysis Basics – Application Note 150
5 dB
18 dB
18 dB
Mixer level (dBm)
Figure 6-6. Dynamic range for 1.3-dB maximum error
Error in displayed signal level (dB)
Let’s see what happened to our dynamic
range as a result of our concern with
measurement error. As Figure 6-6 shows,
second-order-distortion dynamic range
changes from 72.5 to 61 dB, a change of
11.5 dB. This is one half the total offsets
for the two curves (18 dB for distortion;
5 dB for noise). Third-order distortion
changes from 81.7 dB to about 72.7 dB for
a change of about 9 dB. In this case, the
change is one third of the 18-dB offset for
the distortion curve plus two thirds of the
5-dB offset for the noise curve.
Next, let’s look at uncertainty due to
low signal-to-noise ratio. The distortion
components we wish to measure are, we
hope, low-level signals, and often they are
at–or very close to–the noise level of our
spectrum analyzer. In such cases, we often
use the video filter to make these low-level
signals more discernible. Figure 6-7 shows
the error in displayed signal level as a
function of displayed signal-to-noise for a
typical spectrum analyzer. The error is only
in one direction, so we could correct for
it. However, we usually do not. So for our
dynamic range measurement, let’s accept
a 0.3-dB error due to noise and offset the
noise curve in our dynamic range chart
by 5 dB, as shown in Figure 6-6. Where
the distortion and noise curves intersect,
the maximum error possible would be less
than 1.3 dB.
See Figure 6-5. For example, if we set
up conditions such that the internally
generated distortion is equal in amplitude
to the distortion on the incoming signal,
the error in the measurement could range
from +6 dB (the two signals exactly in
phase) to negative infinity (the two signals
exactly out of phase and so canceling).
Such uncertainty is unacceptable in most
cases. If we put a limit of ±1 dB on the
measurement uncertainty, Figure 6-5
shows us that the internally generated
distortion product must be about 18 dB
below the distortion product we wish to
measure. To draw dynamic range curves
for second- and third-order measurements
with no more than 1 dB of measurement
error, we must then offset the curves
of Figure 6-2 by 18 dB as shown in
Figure 6-6.
Displayed S/N (dB)
Figure 6-7. Error in displayed signal amplitude due to noise
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60 | Keysight | Spectrum Analysis Basics – Application Note 150
Gain compression
In our discussion of dynamic range, we
did not concern ourselves with how
accurately the larger tone is displayed,
even on a relative basis. As we raise
the level of a sinusoidal input signal,
eventually the level at the input mixer
becomes so high that the desired output
mixing product no longer changes linearly
with respect to the input signal. The
mixer is in saturation, and the displayed
signal amplitude is too low. Saturation is
gradual rather than sudden. To help us
stay away from the saturation condition,
the 1-dB compression point is normally
specified. Typically, this gain compression
occurs at a mixer level in the range of –5
to +5 dBm. Thus we can determine what
input attenuator setting to use for accurate
measurement of high-level signals3.
Spectrum analyzers with a digital IF will
indicate that ADC is over-ranged.
Actually, there are three different methods
of evaluating compression. A traditional
method, called CW compression,
measures the change in gain of a device
(amplifier or mixer or system) as the
input signal power is swept upward.
This method is the one just described.
Note that the CW compression point is
considerably higher than the levels for
the fundamentals indicated previously for
even moderate dynamic range. So we
were correct in not concerning ourselves
with the possibility of compression of the
larger signal(s).
A second method, called two-tone
compression, measures the change in
system gain for a small signal while the
power of a larger signal is swept upward.
Two-tone compression applies to the
measurement of multiple CW signals, such
as sidebands and independent signals. The
threshold of compression of this method
is usually a few dB lower than that of the
CW method. This is the method used by
Keysight Technologies to specify spectrum
analyzer gain compression.
A third method, called pulse compression,
measures the change in system gain to
a narrow (broadband) RF pulse while
the power of the pulse is swept upward.
When measuring pulses, we often use
a resolution bandwidth much narrower
than the bandwidth of the pulse, so our
analyzer displays the signal level well
below the peak pulse power. As a result,
we could be unaware of the fact that
the total signal power is above the mixer
compression threshold. A high threshold
improves signal-to-noise ratio for highpower, ultranarrow or widely-chirped
pulses. The threshold is about 12 dB
higher than for two-tone compression in
the Keysight X-Series signal analyzers.
Nevertheless, because different
compression mechanisms affect CW, twotone and pulse compression differently,
any of the compression thresholds can be
lower than any other.
Display range and
measurement range
Two additional ranges are often confused
with dynamic range: display range and
measurement range. Display range, often
called display dynamic range, refers to
the calibrated amplitude range of the
spectrum analyzer display. For example,
a display with ten divisions would seem
to have a 100-dB display range when we
select 10 dB per division. This is certainly
true for today’s analyzers with digital IF
circuitry, such as the Keysight X-Series. It
is also true for the Keysight ESA-E Series
analyzers when you use the narrow (10- to
300-Hz) digital resolution bandwidths.
However, spectrum analyzers with analog
IF sections typically are calibrated only for
the first 85 or 90 dB below the reference
level. In this case, the bottom line of the
graticule represents signal amplitudes of
zero, so the bottom portion of the display
covers the range from –85 or –90 dB to
infinity, relative to the reference level.
The range of the log amplifier can be
another limitation for spectrum analyzers
with analog IF circuitry. For example,
ESA-L Series spectrum analyzers use
an 85-dB log amplifier. Thus, only
measurements that are within 85 dB
below the reference level are calibrated.
The question is, can the full display range
be used? From the previous discussion
of dynamic range, we know the answer is
generally yes. In fact, dynamic range often
exceeds display range or log amplifier
range. To bring the smaller signals into
the calibrated area of the display, we must
increase IF gain. But in so doing, we may
move the larger signals off the top of the
display, above the reference level. Some
Keysight analyzers, such as the X-Series,
allow measurements of signals above
the reference level without affecting the
accuracy with which the smaller signals
are displayed, as shown in Figure 6-8
(see page 61). So we can indeed take
advantage of the full dynamic range of an
analyzer even when the dynamic range
exceeds the display range. In Figure
6-8, even though the reference level
has changed from –20 dBm to –50 dBm,
driving the signal far above the top of
the screen, the marker readout remains
Measurement range is the ratio of the
largest to the smallest signal that can
be measured under any circumstances.
The maximum safe input level, typically
+30 dBm (1 watt) for most analyzers,
determines the upper limit. These analyzers
have input attenuators you can set to 60
or 70 dB, so you can reduce +30 dBm
signals to levels well below the compression
point of the input mixer and measure
them accurately. The displayed average
noise level sets the other end of the range.
Depending on the minimum resolution
bandwidth of the particular analyzer and
whether or not you are using a preamplifier,
DANL typically ranges from –115 to
–170 dBm. Measurement range, then, can
vary from 145 to 200 dB. Of course, we
cannot view a –170-dBm signal while a
+30-dBm signal is also present at the input.
Many analyzers internally control the combined settings of the input attenuator and IF gain so that a CW signal as high as the compression level at
the input mixer creates a response above the top line of the graticule. This feature keeps us from making incorrect measurements on CW signals
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61 | Keysight | Spectrum Analysis Basics – Application Note 150
Adjacent channel power
TOI, SOI, 1-dB gain compression, and
DANL are all classic measures of spectrum
analyzer performance. However, with the
tremendous growth of digital communication systems, other measures of dynamic
range have become increasingly important. For example, adjacent channel power
(ACP) measurements are often done in
CDMA-based communication systems to
determine how much signal energy leaks
or “spills over” into adjacent or alternate
channels located above and below a
carrier. An example ACP measurement is
shown in Figure 6-9.
Note the relative amplitude difference
between the channel power and the
adjacent and alternate channels. You can
measure up to six channels on either side
of the carrier at a time.
Typically, we are most interested in the
relative difference between the signal
power in the main channel and the signal
power in the adjacent or alternate channel.
Depending on the particular communication standard, these measurements are often described as “adjacent channel power
ratio” (ACPR) or “adjacent channel leakage
ratio” (ACLR) tests. Because digitally
modulated signals and the distortion they
generate are very noise-like in nature,
the industry standards typically define a
channel bandwidth over which the signal
power is integrated.
Figure 6-8. Display range and measurement range on the PXA spectrum analyzer
To accurately measure ACP performance
of a device under test such as a power
amplifier, the spectrum analyzer must
have better ACP performance than the
device being tested. Therefore, spectrum
analyzer ACPR dynamic range has become
a key performance measure for digital
communication systems.
Figure 6-9. Adjacent channel power measurement using a PXA spectrum analyzer
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62 | Keysight | Spectrum Analysis Basics – Application Note 150
Chapter 7. Extending the Frequency Range
As more wireless services continue to be
introduced and deployed, the available
spectrum has become more and more
crowded. As a result, there has been
an ongoing trend toward developing
new products and services at higher
frequencies. In addition, new microwave
technologies continue to evolve, driving the
need for more measurement capability in
the microwave bands. Spectrum analyzer
designers have responded by developing
instruments capable of directly tuning
up to 50 GHz using a coaxial input. Even
higher frequencies can be measured using
external mixing techniques. This chapter
describes the techniques used to enable
tuning the spectrum analyzer to such high
Internal harmonic mixing
In Chapter 2, we described a single-range
spectrum analyzer that tunes to 3.6 GHz.
Now we want to tune higher in frequency.
The most practical way to achieve an
extended range is to use harmonic mixing.
band path
3.6 GHz
But let us take one step at a time. In
developing our tuning equation in Chapter
2, we found that we needed the low-pass
filter shown in Figure 2-1 to prevent higherfrequency signals from reaching the mixer.
The result was a uniquely responding,
single-band analyzer that tuned to 3.6 GHz.
To observe and measure higher-frequency
signals, we must remove the low-pass filter.
Other factors that we explored in
developing the tuning equation were the
choice of LO and intermediate frequencies.
We decided that the IF should not be within
the band of interest because it created a
hole in our tuning range in which we could
not make measurements. So we chose
5.1 GHz, moving the IF above the highest
tuning frequency of interest (3.6 GHz). Our
new tuning range will be above 3.6 GHz,
so it seems logical to move the new IF to
a frequency below 3.6 GHz. A typical first
IF for these higher frequency ranges in
Keysight spectrum analyzers is 322.5 MHz.
5.1225 GHz
322.5 MHz
22.5 MHz
We will use this frequency in our examples.
In summary, for the low band, up to
3.6 GHz, our first IF is 5.1 GHz. For the
upper frequency bands, we switch to a first
IF of 322.5 MHz. In Figure 7-1, the second
IF is already 322.5 MHz, so all we need
to do when we want to tune to the higher
ranges is bypass the first IF.
In Chapter 2, we used a mathematical
approach to conclude that we needed
a low-pass filter. The math becomes
more complex in the situation here, so
we will use a graphical approach to see
what is happening. The low band is the
simpler case, so we’ll start with that.
In all of our graphs, we will plot the LO
frequency along the horizontal axis and
signal frequency along the vertical axis,
as shown in Figure 7-2. We know we get
a mixing product equal to the IF (and
therefore a response on the display)
whenever the input signal differs from the
LO by the IF. Therefore, we can determine
the frequency to which the analyzer
is tuned simply by adding the IF to, or
subtracting it from, the LO frequency.
Analog or
digital IF
band path
3.8 to 8.7 GHz
To external
4.8 GHz
300 MHz
322.5 MHz
Sweep generator
Figure 7-1. Switching arrangement for low band and high bands
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Now let’s add the other fundamentalmixing band by adding the IF to the LO
line in Figure 7-2. This gives us the solid
upper line, labeled 1+, that indicates a
tuning range from 8.9 to 13.8 GHz. Note
that for a given LO frequency, the two
frequencies to which the analyzer is tuned
are separated by twice the IF. Assuming
we have a low-pass filter at the input while
measuring signals in the low band, we
will not be bothered by signals in the 1+
frequency range.
Next let’s see to what extent harmonic
mixing complicates the situation.
Harmonic mixing comes about because
the LO provides a high-level drive signal
to the mixer for efficient mixing, and
because the mixer is a non-linear device,
it generates harmonics of the LO signal.
Incoming signals can mix against LO
harmonics just as well as the fundamental,
and any mixing product that equals the
IF produces a response on the display. In
other words, our tuning (mixing) equation
now becomes:
fsig = nf LO ± f IF
where n = LO harmonic
(Other parameters remain the
same as previously discussed.)
LO frequency,
LO frequency (GHz)
Figure 7-2. Tuning curves for fundamental mixing in the low band, high IF case
Signal frequency (GHz)
To determine our tuning range, we start
by plotting the LO frequency against the
signal frequency axis, as shown by the
dashed line in Figure 7-2. Subtracting
the IF from the dashed line gives us a
tuning range of 0 to 3.6 GHz, the range
we developed in Chapter 2. Note that
this line in Figure 7-2 is labeled “1−” to
indicate fundamental mixing and the use
of the minus sign in the tuning equation.
We can use the graph to determine what
LO frequency is required to receive a
particular signal or to what signal the
analyzer is tuned for a given LO frequency.
To display a 1-GHz signal, the LO must be
tuned to 6.1 GHz. For an LO frequency of
8 GHz, the spectrum analyzer is tuned to
receive a signal frequency of 2.9 GHz. In
our text, we round off the first IF to one
decimal place; the true IF, 5.1225 GHz, is
shown on the block diagram.
Signal frequency (GHz)
63 | Keysight | Spectrum Analysis Basics – Application Note 150
LO frequency (GHz)
Figure 7-3. Signals in the “1 minus” frequency range produce single, unambiguous responses in the
low-band, high-IF case
Let’s add second-harmonic mixing
to our graph in Figure 7-3 and see
to what extent this complicates our
measurement procedure. As before, we
first plot the LO frequency against the
signal frequency axis. Multiplying the
LO frequency by two yields the upper
dashed line of Figure 7-3. As we did for
fundamental mixing, we simply subtract
the IF (5.1 GHz) from and add it to the
LO second-harmonic curve to produce
the 2− and 2+ tuning ranges. Since
neither of these overlap the desired
1− tuning range, we can again argue
that they do not really complicate the
measurement process.
In other words, signals in the 1− tuning
range produce unique, unambiguous
responses on our analyzer display.
The same low-pass filter used in the
fundamental mixing case works equally
well for eliminating responses created in
the harmonic mixing case.
The situation is considerably different for
the high-band, low-IF case. As before,
we start by plotting the LO fundamental
against the signal-frequency axis and then
add and subtract the IF, producing the
results shown in Figure 7-4. Note that the
1− and 1+ tuning ranges are much closer
together, and in fact overlap, because the
IF is a much lower frequency, 322.5 MHz
in this case.
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64 | Keysight | Spectrum Analysis Basics – Application Note 150
With this type of mixing arrangement,
it is possible for signals at different
frequencies to produce responses at
the same point on the display, that is,
at the same LO frequency. As we can
see from Figure 7-4, input signals at 4.7
and 5.3 GHz both produce a response
at the IF when the LO frequency is set to
5 GHz. These signals are known as image
frequencies, and they are also separated
by twice the IF frequency.
Clearly, we need some mechanism to
differentiate between responses generated
on the 1− tuning curve for which our
analyzer is calibrated and those produced
on the 1+ tuning curve.
Signal frequency (GHz)
Does the close spacing of the tuning
ranges complicate the measurement
process? Yes and no. First of all, our
system can be calibrated for only one
tuning range at a time. In this case, we
would choose the 1− tuning to give us a
low-end frequency of about 3.5 GHz, so
we have some overlap with the 3.6-GHz
upper end of our low-band tuning range.
So what are we likely to see on the
display? If we enter the graph at an LO
frequency of 5 GHz, we find there are two
possible signal frequencies that would
give us responses at the same point on
the display: 4.7 and 5.3 GHz (rounding the
numbers again). On the other hand, if we
enter the signal frequency axis at 5.3 GHz,
we find that in addition to the 1+ response
at an LO frequency of 5 GHz, we could
also get a 1− response. This would occur
if we allowed the LO to sweep as high as
5.6 GHz, twice the IF above 5 GHz. Also,
if we entered the signal frequency graph
at 4.7 GHz, we would find a 1+ response
at an LO frequency of about 4.4 GHz
(twice the IF below 5 GHz) in addition to
the 1− response at an LO frequency of
5 GHz. Thus, for every desired response on
the 1− tuning line, there will be a second
response located twice the IF below it.
These pairs of responses are known as
image responses.
Image Frequencies
LO Frequency
LO frequency (GHz)
Figure 7-4. Tuning curves for fundamental mixing in the high-band, low-IF case
However, before we look at signal
identification solutions, let’s add harmonicmixing curves to 26.5 GHz and see if there
are any additional factors we must consider
in the signal identification process. Figure
7-5 shows tuning curves up to the fourth
harmonic of the LO.
In examining Figure 7-5, we find some
additional complications. The spectrum
analyzer is set up to operate in several
tuning bands. Depending on the frequency
to which the analyzer is tuned, the
analyzer display is frequency calibrated
for a specific LO harmonic. For example,
in the 8.3- to 13.6-GHz input frequency
range, the spectrum analyzer is calibrated
for the 2− tuning curve. Suppose we have
an 13.6-GHz signal present at the input.
As the LO sweeps, the signal will produce
IF responses with the 3+, 3-, 2+ and
2− tuning curves. The desired response of
the 2− tuning curve occurs when the LO
frequency satisfies the tuning equation:
The displayed signals created by the
responses to the 3+ and 3− tuning curves
are known as in-band multiple responses.
Because they occur when the LO is tuned
to 4.63 GHz and 4.43 GHz, they will
produce false responses on the display
that appear to be genuine signals at
8.96 GHz and 8.56 GHz.
Other situations can create out-of-band
multiple responses. For example, suppose
we are looking at a 5-GHz signal in band 1
that has a significant third harmonic at
15 GHz (band 3). In addition to the
expected multiple pair caused by the
5-GHz signal on the 1+ and 1− tuning
curves, we also get responses generated
by the 15-GHz signal on the 4+, 4−,
3+, and 3− tuning curves. Since these
responses occur when the LO is tuned to
3.7, 3.8, 4.9, and 5.1 GHz respectively, the
display will show signals that appear to be
located at 3.4, 3.5, 4.6, and 4.8 GHz. This
is shown in Figure 7-6.
13.6 GHz = 2 f LO – 0.3
f LO = 6.95GHz
Similarly, we can calculate that the
response from the 2+ tuning curve
occurs when f LO = 6.65 GHz, resulting in
a displayed signal that appears to be at
13.0 GHz.
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65 | Keysight | Spectrum Analysis Basics – Application Note 150
Apparent location of an input signal
resulting from the response to the
2- tuning curve
Signal frequency (GHz)
Band 4
In-band multiple
Band 3
Band 2
1Band 1
Apparent locations of in-band
multiples of a 13.6 GHz input signal
4.43 4.63
Band 0
6.65 6.95
LO frequency (GHz)
Figure 7-5. Tuning curves up to 4th harmonic of LO showing in-band multiple responses to a 13.6-GHz input signal
Signal frequency (GHz)
multiple responses
Band 4
Band 3
Band 2
1Band 1
Band 0
3.7 3.8
4.9 5.1
LO frequency (GHz)
Figure 7-6. Out-of-band multiple responses in band 1 as a result of a signal in-band 3
Multiple responses generally always come
in pairs1, with a “plus” mixing product
and a “minus” mixing product. When we
use the correct harmonic mixing number
for a given tuning band, the responses
will be separated by 2 times f IF. Because
the slope of each pair of tuning curves
increases linearly with the harmonic
number N, the multiple pairs caused by
any other harmonic mixing number appear
to be separated by:
2f IF (Nc /NA)
where Nc = the correct harmonic number
for the desired tuning band
NA = the actual harmonic number
generating the multiple pair
Often referred to as an “image pair.” This
is inaccurate terminology, since images are
actually two or more real signals present at
the spectrum analyzer input that produce an
IF response at the same LO frequency. The
numbers for your analyzer may differ.
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Signal frequency (GHz)
66 | Keysight | Spectrum Analysis Basics – Application Note 150
2+ LO Doubled
2- LO Doubled
Band 4
1+ LO Doubled
1- LO Doubled
Band 3
Band 2
1Band 1
Band 0
LO frequency (GHz)
Figure 7-7. X-Series analyzer harmonic bands using LO doubling
In X-Series analyzers, the LO is doubled
to produce a new, higher-frequency LO
for harmonic mixing. As a result, the LO
harmonics are twice as far apart as they
would otherwise be and likelihood of multiple
responses is significantly reduced. Compare
Figure 7-6 and Figure 7-7.
Can we conclude from this discussion that
a harmonic mixing spectrum analyzer is
not practical? Not necessarily. In cases
where the signal frequency is known,
we can tune to the signal directly,
knowing that the analyzer will select the
appropriate mixing mode for which it is
calibrated. In controlled environments
with only one or two signals, it is usually
easy to distinguish the real signal from the
image and multiple responses.
However, there are many cases in which
we have no idea how many signals are
involved or what their frequencies might
be. For example, we could be searching for
unknown spurious signals, conducting site
surveillance tests as part of a frequencymonitoring program or performing
EMI tests to measure unwanted device
emissions. In all these cases, we could be
looking for totally unknown signals in a
potentially crowded spectral environment.
Having to perform some form of
identification routine on each and every
response would make measurement time
intolerably long.
Fortunately, there is a way to essentially
eliminate image and multiple responses
through a process of prefiltering the signal. This technique is called preselection.
What form must our preselection take?
Referring back to Figure 7-4, assume we
have two signals at 4.7 and 5.3 GHz present
at the input of our analyzer. If we were
particularly interested in one, we could use
a band-pass filter to allow that signal into
the analyzer and reject the other. However,
the fixed filter does not eliminate multiple
responses; so if the spectrum is crowded,
there is still potential for confusion. More
important, perhaps, is the restriction that
a fixed filter puts on the flexibility of the
analyzer. If we are doing broadband testing,
we certainly do not want to be continually
forced to change bandpass filters.
The solution is a tunable filter configured
such that it automatically tracks the
frequency of the appropriate mixing mode.
Figure 7-8 shows the effect of such a
preselector. Here we take advantage of the
fact that our superheterodyne spectrum
analyzer is not a real-time analyzer; that
is, it tunes to only one frequency at a time.
The dashed lines in Figure 7-8 represent
the bandwidth of the tracking preselector.
Signals beyond the dashed lines are
Let’s continue with our previous example
of 4.7- and 5.3- GHz signals present at
the analyzer input. If we set a center
frequency of 5 GHz and a span of 2
GHz, let’s see what happens as the
analyzer tunes across this range. As the
LO sweeps past 4.4 GHz (the frequency
at which it could mix with the 4.7-GHz
input signal on its 1+ mixing mode), the
preselector is tuned to 4.1 GHz and
therefore rejects the 4.7-GHz signal. The
input signal does not reach the mixer,
so no mixing occurs, and no response
appears on the display. As the LO sweeps
past 5 GHz, the preselector allows the
4.7-GHz signal to reach the mixer, and
we see the appropriate response on the
display. The 5.3-GHz image signal is
rejected, so it creates no mixing product
to interact with the mixing product from
the 4.7-GHz signal and cause a false
display. Finally, as the LO sweeps past
5.6 GHz, the preselector allows the
5.3-GHz signal to reach the mixer, and
we see it properly displayed. Note in
Figure 7-8 that nowhere do the various
mixing modes intersect. So as long as the
preselector bandwidth is narrow enough
(it typically varies from about 35 MHz
at low frequencies to 80 MHz at high
frequencies) it will greatly attenuate all
image and multiple responses.
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67 | Keysight | Spectrum Analysis Basics – Application Note 150
Signal frequency (GHz)
The word “eliminate” may be a little
strong. Preselectors do not have infinite
rejection. Rejection in the 70- to 80-dB
range is more likely. So if we are looking
for very low-level signals in the presence
of very high-level signals, we might
see low-level images or multiples of
the high-level signals. What about the
low band? Most tracking preselectors
use YIG technology, and YIG filters do
not operate well at low frequencies.
Fortunately, there is a simple solution.
Figure 7-3 shows that no other mixing
mode overlaps the 1− mixing mode in the
low-frequency, high-IF case. So a simple
low-pass filter attenuates both image and
multiple responses. Figure 7-9 shows the
input architecture of a typical microwave
spectrum analyzer.
LO frequency (GHz)
Figure 7-8. Preselection; dashed gray lines represent bandwidth of tracking preselector
band path
3.6 GHz
5.1225 GHz
322.5 MHz
22.5 MHz
Analog or
digital IF
band path
3.8 to 8.7 GHz
To external
4.8 GHz
300 MHz
322.5 MHz
Sweep generator
Figure 7-9. Front-end architecture of a typical preselected spectrum analyzer
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68 | Keysight | Spectrum Analysis Basics – Application Note 150
Amplitude calibration
So far, we have looked at how a
harmonic mixing spectrum analyzer
responds to various input frequencies.
What about amplitude?
The conversion loss of a mixer is a
function of harmonic number, and the
loss goes up as the harmonic number
goes up. This means that signals of equal
amplitude would appear at different levels
on the display if they involved different
mixing modes. To preserve amplitude
calibration, something must be done. In
Keysight spectrum analyzers, the IF gain
is changed. The increased conversion
loss at higher LO harmonics causes a loss
of sensitivity just as if we had increased
the input attenuator. And since the IF
gain change occurs after the conversion
loss, the gain change is reflected by a
corresponding change in the displayed
noise level. So we can determine analyzer
sensitivity on the harmonic-mixing ranges
by noting the average displayed noise level
just as we did on fundamental mixing.
In older spectrum analyzers, the increase
in displayed average noise level with each
harmonic band was very noticeable.
More recent models of Keysight spectrum
analyzers use a double-balanced, imageenhanced harmonic mixer to minimize
the increased conversion loss when
using higher harmonics. Thus, the “stair
step” effect on DANL has been replaced
by a gentle sloping increase with higher
frequencies, as shown in Figure 7-10.
Phase noise
In Chapter 2, we noted that instability of an
analyzer LO appears as phase noise around
signals that are displayed far enough
above the noise floor. We also noted
that this phase noise can impose a limit
on our ability to measure closely spaced
signals that differ in amplitude. The level
of the phase noise indicates the angular,
or frequency, deviation of the LO. What
happens to phase noise when a harmonic
of the LO is used in the mixing process?
Relative to fundamental mixing, phase
noise (in decibels) increases by:
Figure 7-10. Rise in noise floor indicates changes in sensitivity with changes in LO harmonic used
Figure 7-11. Phase noise levels for fundamental and 4th harmonic mixing
20 log(N),
where N = harmonic of the LO
For example, suppose that the LO
fundamental has a peak-to-peak deviation
of 10 Hz. The second harmonic then has
a 20-Hz peak-to-peak deviation; the third
harmonic, 30 Hz; and so on. Since the
phase noise indicates the signal (noise
in this case) producing the modulation,
the level of the phase noise must be
higher to produce greater deviation. When
the degree of modulation is very small,
as in the situation here, the amplitude
of the modulation side bands is directly
proportional to the deviation of the carrier
If the deviation doubles, the level of the
side bands must also double in voltage;
that is, increase by 6 dB or 20 log(2).
As a result, the ability of our analyzer to
measure closely spaced signals that are
unequal in amplitude decreases as higher
harmonics of the LO are used for mixing.
Figure 7-11 shows the difference in phase
noise between fundamental mixing of a
5-GHz signal and fourth-harmonic mixing
of a 20-GHz signal.
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69 | Keysight | Spectrum Analysis Basics – Application Note 150
Improved dynamic range
Internal distortion (dBc)
A preselector improves dynamic range
if the signals in question have sufficient
frequency separation. The discussion of
dynamic range in Chapter 6 assumed that
both the large and small signals were
always present at the mixer and their
amplitudes did not change during the
course of the measurement. But as we
have seen, if signals are far enough apart,
a preselector allows one to reach the mixer
while rejecting the others. For example, if
we were to test a microwave oscillator for
harmonics, a preselector would reject the
fundamental when we tuned the analyzer
to one of the harmonics.
Let’s look at the dynamic range of
a second-harmonic test of a 3-GHz
oscillator. Using the example from Chapter
6, suppose that a –40-dBm signal at
the mixer produces a second harmonic
product of –75 dBc. We also know, from
our discussion, that for every 1 dB the level
of the fundamental changes at the mixer,
measurement range also changes by 1 dB.
The second-harmonic distortion curve is
shown in Figure 7-12. For this example, we
assume plenty of power from the oscillator
and set the input attenuator so that when
we measure the oscillator fundamental,
the level at the mixer is –10 dBm, below
the 1-dB compression point.
From the graph, we see that a –10-dBm
signal at the mixer produces a secondharmonic distortion component of
–45 dBc. Now we tune the analyzer to the
6-GHz second harmonic. If the preselector
has 70-dB rejection, the fundamental
at the mixer has dropped to –80 dBm.
Figure 7-12 indicates that for a signal
of –80 dBm at the mixer, the internally
generated distortion is –115 dBc, meaning
115 dB below the new fundamental level
of –80 dBm. This puts the absolute level
of the harmonic at –195 dBm. So the
difference between the fundamental we
tuned to and the internally generated
second harmonic we tuned to is 185 dB!
Mixed level (dBm)
Figure 7-12. Second-order distortion graph
Clearly, for harmonic distortion, dynamic
range is limited on the low-level
(harmonic) end only by the noise floor
(sensitivity) of the analyzer. What
about the upper, high-level end? When
measuring the oscillator fundamental,
we must limit power at the mixer to get
an accurate reading of the level. We can
use either internal or external attenuation
to limit the level of the fundamental
at the mixer to something less than
the 1-dB compression point. However,
the preselector highly attenuates the
fundamental when we are tuned to the
second harmonic, so we can remove some
attenuation if we need better sensitivity
to measure the harmonic. A fundamental
level of +20 dBm at the preselector should
not affect our ability to measure the
Any improvement in dynamic range for
third-order intermodulation measurements
depends upon separation of the test tones
versus preselector bandwidth. As we
noted, typical preselector bandwidth is
about 35 MHz at the low end and 80 MHz
at the high end.
As a conservative figure, we might use
18 dB per octave of bandwidth roll-off
of a typical YIG preselector filter beyond
the 3 dB point. So to determine the
improvement in dynamic range, we must
determine to what extent each of the
fundamental tones is attenuated and
how that affects internally generated
distortion. From the expressions in
Chapter 6 for third-order intermodulation,
we have:
(k4 /8)VLOV12V2 cos[ωLO – (2ω1 – ω2)]t
(k4 /8)VLOV1V22cos[ωLO – (2ω2 – ω1)]t
Looking at these expressions, we see
that the amplitude of the lower distortion
component (2ω1 – ω2) varies as the
square of V1 and linearly with V2. On
the other side, the amplitude of the
upper distortion component (2ω2 – ω1)
varies linearly with V1 and as the square
of V2. However, depending on the
signal frequencies and separation, the
preselector may not attenuate the two
fundamental tones equally.
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70 | Keysight | Spectrum Analysis Basics – Application Note 150
Consider the situation shown in Figure
7-13 in which we are tuned to the lower
distortion component, and the two
fundamental tones are separated by half
the preselector bandwidth. In this case,
the lower-frequency test tone lies at the
edge of the preselector pass band and is
attenuated 3 dB. The upper test tone lies
above the lower distortion component by
an amount equal to the full preselector
bandwidth. It is attenuated approximately
21 dB. Since we are tuned to the lower
distortion component, internally generated
distortion at this frequency drops by a
factor of two relative to the attenuation
of V1 (2 times 3 dB = 6 dB) and equally as
fast as the attenuation of V2 (21 dB). The
improvement in dynamic range is the sum
of 6 dB + 21 dB, or 27 dB. As in the case
of second harmonic distortion, the noise
floor of the analyzer must be considered,
too. For very closely spaced test tones,
the preselector provides no improvement,
and we determine dynamic range as if the
preselector was not there.
The discussion of dynamic range in
Chapter 6 applies to the low-pass-filtered
low band. The only exceptions occur
when a particular harmonic of a low-band
signal falls within the preselected range.
For example, if we measure the second
harmonic of a 2.5- GHz fundamental, we
get the benefit of the preselector when
we tune to the 5- GHz harmonic.
Pluses and minuses
of preselection
We have seen the pluses of preselection:
simpler analyzer operation, uncluttered
displays, improved dynamic range and
wide spans. But there are also some
disadvantages relative to an unpreselected
First of all, the preselector has insertion
loss, typically 6 to 8 dB. This loss comes
prior to the first stage of gain, so system
sensitivity is degraded by the full loss. In
addition, when a preselector is connected
directly to a mixer, the interaction of the
mismatch of the preselector with that of
the input mixer can cause a degradation of
frequency response.
3 dB
21 dB
27 dB
Figure 7-13. Improved third-order intermodulation distortion; test tone separation is significant relative to
preselector bandwidth
You must use proper calibration
techniques to compensate for this ripple.
Another approach to minimize this
interaction would be to insert a matching
pad (fixed attenuator) or isolator
between the preselector and mixer. In
this case, sensitivity would be degraded
by the full value of the pad or isolator.
Some spectrum analyzer architectures
eliminate the need for the matching
pad or isolator. As the electrical length
between the preselector and mixer
increases, the rate of change of phase
of the reflected and re-reflected
signals becomes more rapid for a given
change in input frequency. The result
is a more exaggerated ripple effect on
flatness. Architectures such as those
used in PSA Series analyzers include
the mixer diodes as an integral part of
the preselector/mixer assembly. In such
an assembly, there is minimal electrical
length between the preselector and
mixer. This architecture thus removes the
ripple effect on frequency response and
improves sensitivity by eliminating the
matching pad or isolator.
Even aside from its interaction with
the mixer, a preselector causes some
degradation of frequency response.
The preselector filter pass band is never
perfectly flat, but rather exhibits a certain
amount of ripple. In most configurations,
the tuning ramp for the preselector
and local oscillator come from the
same source, but there is no feedback
mechanism to ensure the preselector
exactly tracks the tuning of the analyzer.
Another source of post-tuning drift is the
self-heating caused by current flowing
in the preselector circuitry. The center
of the preselector pass band will depend
on its temperature and temperature
gradients, which depend on the history
of the preselector tuning. As a result,
you obtain the best flatness by centering
the preselector at each signal. The
centering function is typically built into
the spectrum analyzer firmware and
is selected either by a front-panel key
in manual measurement applications
or programmatically in automated test
systems. When activated, the centering
function adjusts the preselector tuning
DAC to center the preselector pass band
on the signal. The frequency response
specification for most microwave
analyzers applies only after centering
the preselector, and it is generally a best
practice to perform this function (to
mitigate the effects of post-tuning drift)
before making amplitude measurements
of microwave signals.
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71 | Keysight | Spectrum Analysis Basics – Application Note 150
In our discussion of sweep time, we found
that analyzers such as the PXA use FFTs
when the narrower resolution bandwidths
are selected. Because the LO is stepped
and fixed for each FFT segment, the
preseletor must be stepped and fixed as
well. Since the preselector takes several
milliseconds to tune and stabilize, sweep
time may be negatively impacted relative
to similar settings in the low band. The
X-Series signal analyzers allow you to
select the width of each step to minimize
the number of steps. (For details, see
the operating manual for your particular
analyzer.) If your analyzer has Option MPB,
you may bypass the preselector to eliminate its impact on sweep time. However,
be sure your signal is such that no images
or multiples can cause confusion.
External harmonic mixing
We have discussed tuning to higher
frequencies within the signal analyzer.
For internal harmonic mixing, the X-Series
signal analyzers use the second harmonic
(N=2–) to tune to 17.1 GHz and the second
harmonic (N=2–), with the LO doubled,
to tune to 26.5 GHz. However, what
if you want to test outside the upper
frequency range of the signal analyzer?
Some analyzers provide the ability to use
an external mixer to make high-frequency
measurements, where the external mixer
becomes the front end of the analyzer,
bypassing the input attenuator, the
preselector and the first mixers. The
external mixer uses higher harmonics of
the analyzer’s first LO, and in some cases,
the first LO frequency is doubled before
being sent to the external mixer. Higher
fundamental LO frequencies allow for
lower mixer conversion loss. Typically, a
spectrum analyzer that supports external
mixing has one or two additional connectors on the front panel. Early analyzers had
two connectors. An LO “out” port routes
the analyzer’s internal first LO signal to
the external mixer, which uses the higher
harmonics to mix with the high-frequency
The external mixer’s IF output connects
to the analyzer’s IF “in” port. The latest
analyzers have only one front-panel
port, and this is possible because the
LO frequency supplied from the analyzer
is between 3 and 14 GHz, while the IF
output frequency from the external mixer
to the analyzer is 322.5 MHz. Because of
the wide frequency difference between
the LO and IF signals, both signals can
exist on the same coaxial interconnect
cable that attaches the analyzer and the
mixer. As long as the external mixer uses
the same IF as the spectrum analyzer, the
signal can be processed and displayed
internally, just like any signal that came
from the internal first mixer. Figure 7-14
illustrates the block diagram of an
external mixer used in conjunction with a
spectrum analyzer.
External mixer
band path
IF out
IF in
3.6 GHz
5.1225 GHz
322.5 MHz
22.5 MHz
Analog or
digital IF
band path
3.8 to 8.7 GHz
To external
4.8 GHz
300 MHz
322.5 MHz
Sweep generator
Figure 7-14. Spectrum analyzer and external mixer block diagram
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72 | Keysight | Spectrum Analysis Basics – Application Note 150
Table 7-1. Harmonic mixing modes used by X-Series analyzers with external mixers
Other Manufacturer’s
(LO range 3–7 GHz)
Other Manufacturer’s
(LO range 6–14 GHz)
F (90.0 to 140.0 GHz)
D (110.0 to 170.0 GHz)
G (140.0 to 220.0 GHz)
Y (170.0 to 260.0 GHz )
J (220.0 to 325.0 GHz)
(325.0 to 500.0 GHz)
(500.0 to 750.0 GHz)
Keysight M1970 Series
(LO range 6–14 GHz)
Keysight 11970 Series
(LO range 3–7 GHz)
A (26.5 to 40.0 GHz)
6− and 8−
Q (33.0 to 50.0 GHz)
8− and 10−
U (40.0 to 60.0 GHz)
V (50.0 to 75.0 GHz)
12− and 14−
E (60.0 to 90.0 GHz)
6− and 8−
W (75.0 to 110.0 GHz)
(750.0 to 1,100.0 GHz)
Table 7-1 shows the harmonic mixing
modes used by the X-Series analyzers at
various millimeter-wave bands for both
the Keysight M1970 Series and the earlier
11970 Series external mixers. For ease of
use and low conversion loss, the M1970
Series mixers provide a USB connection
that is used to automatically identify the
mixer model number and serial number,
perform an LO adjustment to optimize
performance, and download the mixer
conversion loss table into the analyzer
memory. You also can use external mixers
from other manufactures if you know the
mixer’s conversion loss with frequency.
Some external mixers from other
manufacturers require a bias current to set
the mixer diodes to the proper operating
point. The X-Series analyzers can provide
up to ± 10 mA of DC current through the
front-panel external mixer port to provide
this bias and keep the measurement setup
as simple as possible.
Whether you perform harmonic mixing
with an internal or an external mixer,
the issues are similar. The LO and its
harmonics mix not only with the desired
input signal, but also with any other signal,
including out-of-band signals, that may be
present at the input. This produces mixing
products that can be processed through
the IF just like any other valid signals.
A tunable filter that performs preselection
of the signals reaching the first mixer in
the internal signal path is common in most
signal analyzers. External mixers that are
unpreselected will produce unwanted
responses on screen that are not true
signals. A way to deal with these unwanted
signals has been designed into the signal
analyzer. This function is called “signal
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73 | Keysight | Spectrum Analysis Basics – Application Note 150
Signal identification
It is quite possible that the particular
response we have tuned onto the display
has been generated on an LO harmonic or
mixing mode other than the one for which
the display is calibrated. So our analyzer
must have some way to tell us whether or
not the display is calibrated for the signal
response in question. For this example,
assume we are using a Keysight M1970V
50- to 75-GHz unpreselected mixer, which
uses the 6− mixing mode. The full V-Band
measurement can be seen in Figure 7-15.
Keysight X-Series signal analyzers offer
two different identification methods:
image shift and image suppress. Let’s
first explore the image shift method.
Looking at Figure 7-15, let’s assume we
have tuned the analyzer to a frequency
of 62.50 GHz. The 6th harmonic of the
LO produces a pair of responses, where
the 6− mixing product appears on screen
at the correct frequency of 62.50 GHz,
while the 6+ mixing product produces a
response with an indicated frequency of
61.85 GHz, which is 2 times f IF below the
real response. The X-Series analyzer has
an IF frequency of 322.5 MHz, so the pair
of responses is separated by 645 MHz.
Figure 7-15. Which ones are the real signals?
Harmonic mixing tuning lines
IF Frequency = 322.5 MHz
Signal Frequency (GHz)
RF 6-
RF 6+
RF 8-
RF 8+
LO Frequency (GHz)
Figure 7-16 Harmonic tuning lines for the X-Series analyzers using the M1970 Series mixers
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74 | Keysight | Spectrum Analysis Basics – Application Note 150
Let’s assume we have some idea of the
characteristics of our signal, but we do
not know its exact frequency. How do
we determine which is the real signal?
The image-shift process retunes the LO
fundamental frequency by an amount
equal to 2f IF/N. This causes the Nth
harmonic to shift by 2f IF.
If we are tuned to a real signal, its
corresponding pair will now appear at
the same position on screen that the real
signal occupied in the first sweep. If we
are tuned to another multiple pair created
by some other incorrect harmonic, the
signal will appear to shift in frequency on
the display. The X-Series signal analyzer
shifts the LO on alternate sweeps, creating
the two displays show in Figures 7-17a
and 7-17b. In Figure 7-17a, the real signal
(the 6 − mixing product) is tuned to the
center of the screen. Figure 7-17b shows
how the image shift function moves the
corresponding pair (the 6+ mixing product)
to the center of the screen.
Figure 7-17a: 6− centered (yellow trace)
Figures 7-17a and 7-17b display alternate
sweeps taken with the image shift
Figure 7-17b: 6+ centered (blue trace)
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75 | Keysight | Spectrum Analysis Basics – Application Note 150
Let’s examine the second method of signal
identification, image suppression. In this
mode, two sweeps are taken using the
minimum hold function, which saves the
smaller value of each display point, or
bucket, from the two sweeps. The first
sweep is done using normal LO tuning
values. The second sweep offsets the
LO fundamental frequency by 2f IF/N. As
we saw in the first signal ID method, the
image product generated by the correct
harmonic will land at the same point on
the display as the real signal did on the
first sweep. Therefore, the trace retains a
high amplitude value. Any false response
that shifts in frequency will have its trace
data replaced by a lower value. Thus, all
image and incorrect multiple responses
will appear as noise, as shown in Figure
Note that both signal identification
methods are used for identifying correct
frequencies only. You should not attempt
to make amplitude measurements while
the signal identification function is turned
on. Once we have identified the real
signal of interest, we turn off the signal ID
function and zoom in on it by reducing the
span. We can then measure the signal’s
amplitude and frequency. See Figure 7-19.
To make an accurate amplitude
measurement, it is important that you
first enter the calibration data for your
external mixer. This data is normally
supplied by the mixer manufacturer, and
it is typically presented as a table of mixer
conversion loss, in dB, at a number of
frequency points across the band. This
data is entered into a correction table
on the signal analyzer, and the analyzer
uses this data to compensate for the
mixer conversion loss. If you are using
the M1970 Series harmonic mixers, the
mixer conversion loss is automatically
transferred from the mixer memory to the
X-Series signal analyzer memory, which
eliminates manual entry into a correction
file. The spectrum analyzer reference level
is now calibrated for signals at the input to
the external mixer.
Figure 7-18. The image suppress function displays only real signals
Figure 7-19. Measurement of a positively identified signal
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76 | Keysight | Spectrum Analysis Basics – Application Note 150
Chapter 8. Modern Signal Analyzers
In the previous chapters of this application
note, we have looked at the fundamental
architecture of spectrum analyzers
and basic considerations for making
frequency-domain measurements. On a
practical level, modern spectrum or signal
analyzers must also handle many other
tasks to help you meet your measurement
requirements. These tasks include:
– Providing application-specific
measurements, such as adjacent
channel power (ACP), noise figure, and
phase noise
– Providing digital modulation analysis
measurements defined by industry
or regulatory standards, such as
LTE, GSM, cdma2000 ®, 802.11, or
– Performing vector signal analysis
– Saving, printing or transferring data
– Offering remote control and operation
over GPIB, LAN or the Internet
– Allowing you to update instrument
firmware to add new features and
capabilities, as well as to repair
– Making provisions for self-calibration,
troubleshooting, diagnostics and
– Recognizing and operating with
optional hardware or firmware to add
new capabilities
– Allowing you to make measurements
in the field with a rugged, batterypowered handheld spectrum analyzer
that correlate with data taken
with high-performance bench-top
In addition to measuring general signal
characteristics like frequency and
amplitude, you often need to make
specific measurements of certain signal
parameters. Examples include channel
power measurements and adjacent
channel power (ACP) measurements,
which we described in Chapter 6. Many
signal analyzers now have these built-in
functions available. You simply specify
the channel bandwidth and spacing, then
press a button to activate the automatic
The complementary cumulative
distribution function (CCDF), which shows
power statistics, is another measurement
capability increasingly found in modern
signal analyzers, as you can see in
Figure 8-1.
CCDF measurements provide statistical
information showing the percent of
time the instantaneous power of the
signal exceeds the average power by a
certain number of dB. This information
is important in power amplifier design,
for example, where it is important to
handle instantaneous signal peaks with
minimum distortion while minimizing
cost, weight and power consumption of
the device.
Other examples of built-in
measurement functions include
occupied bandwidth, TOI, harmonic
distortion, and spurious emissions
measurements. The instrument
settings – such as center frequency,
span and resolution bandwidth – for
these measurements depend on the
specific radio standard to which the
device is being tested.
Figure 8-1. CCDF measurement
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77 | Keysight | Spectrum Analysis Basics – Application Note 150
Most modern signal analyzers have these
instrument settings stored in memory so
you can select the desired radio standard
(LTE, MSR, GSM/EDGE, cdma2000,
W-CDMA, 802.11a/b/g/n/ac and so on) to
make the measurements properly.
RF designers are often concerned with
the noise figure of their devices, as noise
figure directly affects the sensitivity
of receivers and other systems. Some
s igna l analyzers, such as the X-Series,
have optional noise figure measurement
capabilities available. This option provides
control for the noise source needed to
drive the input of the device under test
(DUT) as well as firmware to automate
the measurement process and display
the results. Figure 8-2 shows a typical
measurement result, with DUT noise
figure (upper trace) and gain (lower trace)
displayed as a function of frequency.
The need for phase information
Figure 8-2. Noise figure measurement
Phase noise is a common measure
of oscillator performance. In digitally
modulated communication systems, phase
noise can negatively impact bit error rates.
Phase noise can also degrade the ability
of Doppler radar systems to capture the
return pulses from targets. The X-Series
signal analyzers offer optional phase
noise measurement capabilities. These
options provide firmware to control the
measurement and display the phase noise
as a function of frequency offset from the
carrier, as shown in Figure 8-3.
Figure 8-3. Phase noise measurement
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78 | Keysight | Spectrum Analysis Basics – Application Note 150
Today’s digital modulation techniques use
amplitude and phase information to carry
more baseband data in limited spectrum
and time. Therefore, it is essential for a
signal analyzer to handle amplitude and
phase in an appropriate manner. QPSK
(Quadrature Phase-Shift Keying) is one of
the simple examples of digital modulation
techniques, with which two bits of digital
data are transmitted at once, or two bits
per symbol. Figure 8-4 shows an example
of QPSK demodulation analysis with
89601B option AYA. Remember you need
four (22) states to transmit 2 bits at once.
As an easy and intuitive way to understand
what’s going on the digital radio
transmission, use an I/Q plane, which
is a two dimensional chart comprising
in-phase and quadrature components of
the demodulated signal on the horizontal
axis and the vertical axis, respectively. An
example of the chart is again shown on the
top left window of Figure 8-4. The yellow
trace called trajectory shows a vector
combining phase and amplitude moves
around as time goes while red points
indicates the instantaneous position of
trajectory at the time of decision when a
receiver actually judges the symbol value.
Essentially, for digital radios, vectors at
these decision points are most important
for modulation quality. As you can see
on the bottom left window of Figure 8-4,
a “scalar” analyzer meaning traditional
spectrum analyzer may be able to show
the modulated signal in frequency domain
so that you can see whether the signal is
properly modulated in power wise to some
extent, and you can also make sure that
there is no unwanted emission or leakage
power to the adjacent channels. You need,
however, some sort of “vector” analyzer to
perform meaningful analysis of modulation
quality for digital data transmission where
phase information is involved.
Figure 8-4. Modulation analysis of a QPSK signal measured with Keysight’s 89600 VSA software
Figure 8-5. Modulation analysis of WLAN 802.11ac signal using Keysight 89600 VSA software
A newer and much more complicated
system is 802.11ac, which uses 256QAM
(quadrature-amplitude modulation). See
Figure 8-5. The maximum power is limited,
so the data points are much closer in both
phase and magnitude than for QPSK.
The analyzer you use to evaluate the
transmitted signal must be sufficiently
accurate that it does not lead you to
a false conclusion about the quality
of the transmission. Pure amplitude
measurements are also required to
determine signal attributes such as
flatness, adjacent-channel power levels
and distortion.
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79 | Keysight | Spectrum Analysis Basics – Application Note 150
Digital modulation analysis
The common wireless communication
systems used throughout the
world today all have prescribed
measurement techniques defined by
standards-development organizations
and governmental regulatory bodies.
Optional measurement personalities are
available on the X-Series signal analyzers
to perform the key tests defined for
a particular wireless communication
format. For example, if we need to test
a transmitter to the Bluetooth wireless
communication standard, we must
measure parameters such as:
Average/peak output power
Modulation characteristics
Initial carrier frequency tolerance
Carrier frequency drift
Monitor band/channel
Modulation overview
Output spectrum
20-dB bandwidth
Adjacent channel power
These measurements are available on the
Keysight X-Series signal analyzers with
appropriate options.
Other optional measurement
capabilities for a wide variety of wireless
communications standards that are
available on the X-Series signal analyzers:
Multi-standard radio (MSR)
Figure 8-6. EVM measurement of LTE FDD downlink signal
Figure 8-6 illustrates an error vector
magnitude (EVM) measurement performed
on a LTE FDD downlink signal. This
test helps you diagnose modulation or
amplification distortions that lead to bit
errors in the receiver.
Not all digital communication systems are
based on well-defined industry standards.
If you are working on nonstandard
proprietary systems or the early stages
of proposed industry-standard formats,
you need more flexibility to analyze
vector-modulated signals under varying
conditions. You can achieve that flexibility
two ways. First, modulation analysis
personalities are available on the X-Series
signal analyzers. Alternatively, you can
perform more extensive analysis with
software running on an external computer.
For example, you can use Keysight
89600 VSA software with X-Series signal
analyzers to provide flexible vector signal
analysis. In this case, the signal analyzer
acts as an RF downconverter and digitizer.
The software can run internally on the
signal analyzer or communicate with the
analyzer over a GPIB or LAN connection. IQ
data is transferred to the computer, where
it performs the vector signal analysis.
Measurement settings, such as
modulation type, symbol rate, filtering,
triggering and record length, can be
varied as necessary for the particular
signal you are analyzing.
More information
Additional information is
available on the following:
Noise figure measurements, see
Keysight Noise Figure Measurements
of Frequency Converting Devices
Using the Keysight NFA Series Noise
Figure Analyzer − Application Note,
literature number 5989-0400EN.
Measurements involving phase,
see Vector Signal Analysis Basics
– Application Note, literature
number 5989-1121EN.
Bluetooth measurements, see
Performing Bluetooth RF Measurements
Today – Application Note, literature
number 5968-7746E.
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80 | Keysight | Spectrum Analysis Basics – Application Note 150
Real-time spectrum analysis
For the capable RF engineer, continuouswave (CW) and predictably-repeating
signals are no great challenge – but today’s
complex and agile signals and multi-signal
environments are proving to be another
matter. To keep up with evolving analysis
needs, new types of signal analyzers and
application software have emerged in
recent years. The Keysight PXA and MXA
signal analyzers now offer a combination of
swept spectrum, real-time and vector signal
analysis capability – all in one instrument.
Design and troubleshooting tasks are
much more difficult when dealing with
agile signals, and the challenges are often
made more difficult when these signals are
in an environment of other agile signals.
Even the analysis of a single signal can be
a challenge when that signal is very agile
or complex. You can use the Keysight PXA
and MXA real-time spectrum analysis
capability to capture the behavior of
dynamic and elusive signals with true
gap-free spectrum analysis.
An example of a single complex signal
is the agile S-band acquisition radar
signal. The signal at the receiver varies
widely in amplitude over a period of
several seconds, and this long-duration
characteristic, combined with the shortduration characteristics of its pulse length
and repetition interval (and therefore short
duty cycle) make it agile and difficult to
measure well. A basic spectrum analysis
of this signal with a swept spectrum
analyzer shows the measurement difficulty
it poses, as illustrated in Figure 8-7. Even
after many sweeps and the use of a max
hold function, the signal is not clearly
The Keysight PXA real-time spectrum
analyzer screen shown in Figure 8-8, in
contrast with the swept spectrum screen,
readily shows the main characteristics of
the signal using the density or histogram
display. The density or histogram display
collects a large amount of real-time
spectrum data into a single display that
shows both rare and frequent events,
with an indication of relative frequency of
Figure 8-7 . Even when you use fast sweeps and max hold over a period of many seconds, the
swept spectrum analyzer view of the radar signal is not very informative
Figure 8-8. Real-time capture of S-band acquisition radar signal
The PXA’s real-time analyzer mode and
density display provide a fast and insightproducing representation of this wideband,
dynamic and agile signal. The blue color
of all but the noise floor indicates that
the pulses, while prominent, have a very
low frequency-of-occurrence. This is the
principal characteristic that makes it difficult
to measure (or even to rapidly and reliably
find) this signal with a swept spectrum
More information
For additional information on
measurements involving real-time
spectrum analysis, see Measuring Agile
Signals and Dynamic Signal Environments
– Application Note, literature number
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81 | Keysight | Spectrum Analysis Basics – Application Note 150
Chapter 9. Control and Data Transfer
Saving and printing data
After making a measurement, we normally
want to keep a record of the test data.
We might simply want to make a quick
printout of the instrument display.
Depending on the particular analyzer and
printer model, we might use the USB or
LAN ports to connect the two units.
Very often, we may want to save
measurement data as a file, either in the
spectrum analyzer’s internal memory or
on a USB mass-storage device. There are
several different kinds of data we can save
this way:
– An image of the display – Preferably in
a popular file format, such as bitmap,
GIF, PNG or Windows metafile.
– Trace data – Saved as X-Y data pairs
representing frequency and amplitude
points on the screen. The number of
data pairs can vary. Modern spectrum
analyzers such as the X-Series allow
you to select the desired display
resolution by setting a minimum of 1
up to a maximum of 40,001 frequency
sweep points (buckets) on the screen.
This data format is well suited for
transfer to a spreadsheet program on a
– Instrument state – To keep a record
of the spectrum analyzer settings,
such as center frequency, span,
reference level and so on, used in the
measurement. This information is useful
for documenting test setups used for
making measurements. Consistent test
setups are essential for maintaining
repeatable measurements over time.
Data transfer and remote
instrument control
In 1977, Keysight Technologies (part of
Hewlett-Packard at that time) introduced
the world’s first GPIB-controllable
spectrum analyzer, the 8568A. The GPIB
interface (also known as HP-IB or IEEE488) made it possible to control all major
functions of the analyzer from an external
computer and transfer trace data to an
external computer. This innovation paved
the way for a wide variety of automated
spectrum analyzer measurements that
were faster and more repeatable than
manual measurements. By transferring the
raw data to a computer, it could be saved
on disk, analyzed, corrected and operated
on in a variety of ways.
Today, automated test and measurement
equipment has become the norm, and
nearly all modern spectrum analyzers
come with a variety of standard interfaces,
including LAN, USB 2.0 and GPIB. LAN
connectivity is the most commonly
used interface, as it can provide high
data-transfer rates over long distances
and integrates easily into networked
environments such as a factory floor.
Other standard interfaces used widely in
the computer industry are likely to become
available on spectrum analyzers in the
future to simplify connectivity between
instruments and computers.
Keysight’s X-Series signal analyzers
literally have computer firmware running
USB ports and a Windows operating
system. These features greatly simplify
control and data transfer. In addition,
the X-Series analyzers can be operated
remotely, and the analyzer’s display
appears on the remote computer. Details
are beyond the scope of this application
note; see the operating manual for your
particular analyzer.
A variety of commercial software products
are available to control spectrum
analyzers remotely over an I/O bus.
Also, you can write your own software to
control spectrum analyzers in a number of
different ways. One method is to directly
send programming commands to the
instrument. Older spectrum analyzers
typically used proprietary command sets,
but newer instruments, such as Keysight’s
X-Series signal analyzers, use industrystandard SCPI (standard commands
for programmable instrumentation)
commands. A more common method is
to use standard software drivers, such
as VXI plug&play drivers, which enable
higher-level functional commands to the
instrument without the need for detailed
knowledge of the SCPI commands. Most
recently, a new generation of languageindependent instrument drivers, known
as “interchangeable virtual instrument,”
or IVI-COM drivers, has become available
for the X-Series signal analyzers. The IVICOM drivers are based on the Microsoft
Component Object Model standard
and work in a variety of PC application
development environments, such as the
Keysight T&M Programmers Toolkit and
Microsoft’s Visual Studio .NET.
Some applications require you to control
the spectrum analyzer and collect
measurement data from a very long
distance. For example, you may want to
monitor satellite signals from a central
control room, collecting data from remote
tracking stations located hundreds or
even thousands of kilometers from the
central site. The X-Series signal analyzers
have software available to control these
units, capture screen images and transfer
trace data over the Internet using a
standard Web browser.
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82 | Keysight | Spectrum Analysis Basics – Application Note 150
Firmware updates
Modern spectrum analyzers have much
more software inside them than do
instruments from just a few years ago. As
new features are added to the software
and defects repaired, it becomes highly
desirable to update the spectrum analyzer’s
firmware to take advantage of the improved
The latest revisions of spectrum and signal
analyzer firmware can be found on the
Keysight Technologies website. You can
download this firmware to a file on your
local computer. A common method to
transfer new firmware into the spectrum
analyzer is to copy the firmware onto a
USB drive and then insert it into one of
the spectrum analyzer’s USB ports. Some
models, such as the X-Series, allow you to
transfer the new firmware directly into the
spectrum analyzer using the instrument’s
Ethernet LAN port.
It is a good practice to periodically check
your spectrum analyzer model’s Web page
to see if updated firmware is available.
Calibration, troubleshooting,
diagnostics and repair
Spectrum analyzers must be periodically
calibrated to insure the instrument
performance meets all published
specifications. Typically, this is done once
a year. However, between these annual
calibrations, the spectrum analyzer must
be aligned periodically to compensate for
thermal drift and aging effects. Modern
spectrum analyzers such as the X-Series
have built-in alignment routines that
operate when the instrument is first turned
on and during retrace (between sweeps)
at predetermined intervals. The alignment
routines also operate if the internal
temperature of the instrument changes.
These alignment routines continuously
adjust the instrument to maintain specified
This application note has provided a
broad survey of basic spectrum analyzer
concepts. However, you may wish to learn
more about many other topics related to
spectrum analysis. An excellent place to
start is to visit the Keysight Technologies
Web site at and
search for signal or spectrum analyzer.
Modern spectrum analyzers usually have
a service menu available. In this area, you
can perform useful diagnostic functions,
such as a test of the front-panel keys.
You also can display more details of the
alignment process, as well as a list of
all optional hardware and measurement
personalities installed in the instrument.
When you upgrade a spectrum analyzer
with a new measurement personality,
Keysight provides a unique license key tied
to the serial number of the instrument. You
install this license key through the USB
port or enter it on the front-panel keypad
to activate the measurement capabilities
of the personality.
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83 | Keysight | Spectrum Analysis Basics – Application Note 150
Glossary of Terms
Absolute amplitude accuracy: The
uncertainty of an amplitude measurement
in absolute terms, either volts or power.
Includes relative uncertainties (see Relative
amplitude accuracy) plus calibrator
uncertainty. For improved accuracy,
some spectrum analyzers have frequency
response specified relative to the calibrator
as well as relative to the midpoint between
peak-to-peak extremes.
ACPR: Adjacent channel power ratio is a
measure of how much signal energy from
one communication channel spills over or
leaks into an adjacent channel. This is an
important metric in digital communication
components and systems, as too much
leakage will cause interference on adjacent
channels. It is sometimes also described as
ACLR, or adjacent channel leakage ratio.
Amplitude accuracy: The uncertainty
of an amplitude measurement. It can be
expressed either as an absolute term or
relative to another reference point.
Amplitude reference signal: A signal of
precise frequency and amplitude that the
analyzer uses for self-calibration.
Analog display: A display technology
where analog signal information (from the
envelope detector) is written directly to an
instrument’s display, typically implemented
on a cathode ray tube (CRT). Analog
displays were once the standard method
of displaying information on spectrum
analyzers. However, modern spectrum
analyzers no longer use this technology;
instead, they now use digital displays.
Average detection: A method of detection
that sums power across a frequency
interval. It is often used for measuring
complex, digitally modulated signals and
other types of signals with noise-like
characteristics. Modern Keysight spectrum
analyzers typically offer three types of
average detection: power (rms) averaging,
which measures the true average power
over a bucket interval; voltage averaging,
which measures the average voltage data
over a bucket interval; and log-power
(video) averaging, which measures the
logarithmic amplitude in dB of the envelope
of the signal during the bucket interval.
Average noise level: See Displayed average
noise level.
Bandwidth selectivity: A measure of an
analyzer’s ability to resolve signals unequal
in amplitude. Also called shape factor,
bandwidth selectivity is the ratio of the
60-dB bandwidth to the 3-dB bandwidth
for a given resolution (IF) filter. For some
analyzers, the 6-dB bandwidth is used in
lieu of the 3-dB bandwidth. In either case,
bandwidth selectivity tells us how steep the
filter skirts are.
Blocking capacitor: A filter that keeps
unwanted low-frequency signals (including
DC) from damaging circuitry. A blocking
capacitor limits the lowest frequency that
can be measured accurately.
CDMA: Code division multiple access
is a method of digital communication in
which multiple communication streams are
orthogonally coded, enabling them to share
a common frequency channel. It is a popular
technique used in a number of widely used
mobile communication systems.
Constellation diagram: A display type
commonly used when analyzing digitally
modulated signals in which the detected
symbol points are plotted on an IQ graph.
Delta marker: A mode in which a fixed,
reference marker has been established and
a second, active marker is available that we
can place anywhere on the displayed trace.
A read-out indicates the relative frequency
separation and amplitude difference
between the reference marker and the
active marker.
Digital display: A display technology
where digitized trace information, stored
in memory, is displayed on an instrument’s
screen. The displayed trace is a series of
points designed to present a continuouslooking trace. While the default number
of display points varies between different
models, most modern spectrum analyzers
allow the user to choose the desired
resolution by controlling the number of
points displayed. The display is refreshed
(rewritten from data in memory) at a
flicker-free rate; the data in memory is
updated at the sweep rate. Nearly all
modern spectrum analyzers have digital
flat-panel LCD displays, rather than
CRT-based analog displays that were used
in earlier analyzers.
Display detector mode: The manner in
which the signal information is processed
prior to being displayed on screen. See Neg
peak, Pos peak,
Normal, Average and Sample.
Digital IF: An architecture found in
modern spectrum analyzers in which the
signal is digitized soon after it has been
downconverted from an RF frequency to an
intermediate frequency (IF). At that point,
all further signal processing is done using
digital signal processing (DSP) techniques.
Display dynamic range: The maximum
dynamic range for which both the
larger and smaller signal may be viewed
simultaneously on the spectrum analyzer
display. For analyzers with a maximum
logarithmic display of 10 dB/div, the actual
dynamic range (see Dynamic range) may
be greater than the display dynamic range.
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84 | Keysight | Spectrum Analysis Basics – Application Note 150
Display scale fidelity: The degree
of uncertainty in measuring relative
differences in amplitude on a spectrum
analyzer. The logarithmic and linear IF
amplifiers found in analyzers with analog
IF sections never have perfect logarithmic
or linear responses, and therefore they
introduce uncertainty. Modern analyzers
with digital IF sections have significantly
better display scale fidelity.
Display range: The calibrated range of the
display for the particular display mode and
scale factor. See Linear and Log display and
Scale factor.
Displayed average noise level: The noise
level as seen on the analyzer’s display
after setting the video bandwidth narrow
enough to reduce the peak-to-peak
noise fluctuations such that the displayed
noise is essentially seen as a straight
line. Usually refers to the analyzer’s own
internally generated noise as a measure of
sensitivity and is typically specified in dBm
under conditions of minimum resolution
bandwidth and minimum input attenuation.
Drift: The very slow (relative to sweep time)
change of signal position on the display as
a result of a change in LO frequency versus
sweep voltage. The primary sources of drift
are the temperature stability and aging rate
of the frequency reference in the spectrum
Dynamic range: The ratio, in dB,
between the largest and smallest signals
simultaneously present at the spectrum
analyzer input that can be measured to
a given degree of accuracy. Dynamic
range generally refers to measurement of
distortion or intermodulation products.
Envelope detector: A circuit element whose
output follows the envelope, but not the
instantaneous variation, of its input signal.
In a superheterodyne spectrum analyzer,
the input to the envelope detector comes
from the final IF, and the output is a video
signal. When we put our analyzer in zero
span, the envelope detector demodulates
the input signal, and we can observe the
modulating signal as a function of time on
the display.
Error vector magnitude (EVM): A quality
metric in digital communication systems.
EVM is the magnitude of the vector
difference at a given instant in time
between the ideal reference signal and the
measured signal.
Frequency resolution: The ability of a
spectrum analyzer to separate closely
spaced spectral components and display
them individually. Resolution of equal
amplitude components is determined by
resolution bandwidth. The ability to resolve
unequal amplitude signals is a function of
both resolution bandwidth and bandwidth
External mixer: An independent mixer,
usually with a waveguide input port, used
to extend the frequency range of spectrum
analyzers that use external mixers. The
analyzer provides the LO signal and, if
needed, mixer bias. Mixing products are
returned to the analyzer’s IF input.
Frequency response: Variation in the
displayed amplitude of a signal as a
function of frequency (flatness). Typically
specified in terms of ± dB relative to the
value midway between the extremes. Also
may be specified relative to the calibrator
FFT (fast Fourier transform): A
mathematical operation performed on a
time-domain signal to yield the individual
spectral components that constitute the
signal. See Spectrum.
Frequency span: The frequency range
represented by the horizontal axis of the
display. Generally, frequency span is given
as the total span across the full display.
Some earlier analyzers indicate frequency
span (scan width) on a per-division basis.
Fast sweep: A digital signal processing
technique that implements complexvalued resolution bandwidth filtering for
a sweeping spectrum analyzer, allowing
faster sweep rates than a traditional analog
or digital resolution bandwidth filter would
Flatness: See Frequency response.
Frequency accuracy: The degree of
uncertainty with which the frequency of a
signal or spectral component is indicated,
either in an absolute sense or relative to
some other signal or spectral component.
Absolute and relative frequency accuracies
are specified independently.
Frequency range: The minimum to
maximum frequencies over which a
spectrum analyzer can tune. While the
maximum frequency is generally thought
of in terms of an analyzer’s coaxial input,
the range of many microwave analyzers
can be extended through use of external
waveguide mixers.
Frequency stability: A general phrase
that covers both short- and long-term
LO instability. The sweep ramp that tunes
the LO also determines where a signal
should appear on the display. Any long
term variation in LO frequency (drift) with
respect to the sweep ramp causes a signal
to slowly shift its horizontal position on the
display. Shorter-term LO instability can
appear as random FM or phase noise on an
otherwise stable signal.
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85 | Keysight | Spectrum Analysis Basics – Application Note 150
Full span: For most modern spectrum
analyzers, full span means a frequency
span that covers the entire tuning range
of the analyzer. These analyzers include
single -band RF analyzers and microwave
analyzers such as the ESA, PSA and
X- Series that use a solid-state switch to
switch between the low and preselected
NOTE: On some earlier spectrum analyzers,
full span referred to a sub-range. For
example, with the Keysight 8566B, a
microwave spectrum analyzer that used
a mechanical switch to switch between
the low and preselected ranges, full span
referred to either the low, non-preselected
range or the high, preselected range.
Gain compression: That signal level at
the input mixer of a spectrum analyzer at
which the displayed amplitude of the signal
is a specified number of dB too low due
just to mixer saturation. The signal level is
generally specified for 1-dB compression
and is usually between +3 and –10 dBm,
depending on the model of spectrum
GSM: The global system for mobile
communication is a widely used digital
standard for mobile communication. It is
a TDMA-based system in which multiple
communication streams are interleaved in
time, enabling them to share a common
frequency channel.
Harmonic distortion: Unwanted frequency
components added to a signal as the result
of the nonlinear behavior of the device (e.g.,
mixer, amplifier) through which the signal
passes. These unwanted components are
harmonically related to the original signal.
Harmonic mixing: Using the LO harmonics
generated in a mixer to extend the tuning
range of a spectrum analyzer beyond
the range achievable using just the LO
IF gain/IF attenuation: Adjusts the vertical
position of signals on the display without
affecting the signal level at the input mixer.
When changed, the value of the reference
level is changed accordingly.
IF feedthrough: A raising of the baseline
trace on the display due to an input signal at
the intermediate frequency
passing through the input mixer. Generally,
this is a potential problem only on nonpreselected spectrum analyzers. The entire
trace is raised because the signal is always
at the IF; mixing with the LO is not required.
Image frequencies: Two or more real
signals present at the spectrum analyzer
input that produce an IF response at the
same LO frequency. Because the mixing
products all occur at the same LO and IF
frequencies, it is impossible to distinguish
between them.
Image response: A displayed signal that
is actually twice the IF away from the
frequency indicated by the spectrum
analyzer. For each harmonic of the LO, there
is an image pair, one below and one above
the LO frequency by the IF. Images usually
appear only on non-preselected spectrum
Incidental FM: Unwanted frequency
modulation on the output of a device (signal
source, amplifier) caused by (incidental
to) some other form of modulation, e.g.,
amplitude modulation.
Input attenuator: A step attenuator
between the input connector and first mixer
of a spectrum analyzer. Also called the RF
attenuator. The input attenuator is used
to adjust level of the signal incident upon
the first mixer. The attenuator is used to
prevent gain compression due to high-level
or broadband signals and to set dynamic
range by controlling the degree of internally
generated distortion. In some analyzers,
the vertical position of displayed signals
is changed when the input attenuator
setting is changed, so the reference level
is also changed accordingly. In modern
Keysight analyzers, the IF gain is changed to
compensate for input attenuator changes,
so signals remain stationary on the display,
and the reference level is not changed.
Input impedance: The terminating
impedance that the analyzer presents to
the signal source. The nominal impedance
for RF and microwave analyzers is usually
50 ohms. For some systems, e.g., cable
TV, 75 ohms is standard. The degree of
mismatch between the nominal and actual
input impedance is given in terms of VSWR
(voltage standing wave ratio).
Intermodulation distortion: Unwanted
frequency components resulting from
the interaction of two or more spectral
components passing through a device with
nonlinear behavior (e.g., mixer, amplifier).
The unwanted components are related
to the fundamental components by sums
and differences of the fundamentals and
various harmonics, e.g. f1 ± f 2, 2f1 ± f 2, 2f 2 ±
f1, 3f1 ± 2f 2, and so forth.
Linear display: The display mode in which
vertical deflection on the display is directly
proportional to the voltage of the input
signal. The bottom line of the graticule
represents 0 V, and the top line, the
reference level, some nonzero value that
depends upon the particular spectrum
analyzer. On most modern analyzers, we
select the reference level, and the scale
factor becomes the reference level value
divided by the number of graticule divisions.
Although the display is linear, modern
analyzers allow reference level and marker
values to be indicated in dBm, dBmV, dBuV,
and in some cases, watts as well as volts.
LO emission or feedout: The emergence of
the LO signal from the input of a spectrum
analyzer. The level can be greater than
0 dBm on non-preselected spectrum
analyzers but is usually less than –70 dBm
on preselected analyzers.
LO feedthrough: The response on the
display when a spectrum analyzer is tuned
to 0 Hz, i.e., when the LO is tuned to the IF.
The LO feedthrough can be used as a 0-Hz
marker, and there is no frequency error.
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Log display: The display mode in which
vertical deflection on the display is a
logarithmic function of the voltage of the
input signal. We set the display calibration
by selecting the value of the top line of the
graticule, the reference level and scale
factor in dB/div. On Keysight analyzers, the
bottom line of the graticule represents zero
volts for scale factors of 10 dB/div or more,
so the bottom division is not calibrated
in these cases. Modern analyzers allow
reference level and marker values to be
indicated in dBm, dBmV, dBuV, volts, and in
some cases, watts. Earlier analyzers usually
offered only one choice of units, and dBm
was the usual choice.
Marker: A visible indicator we can place
anywhere along the displayed signal trace.
A read out indicates the absolute value of
both the frequency and amplitude of the
trace at the marked point. The amplitude
value is given in the currently selected units.
Also see Delta marker and Noise marker.
Measurement range: The ratio, expressed
in dB, of the maximum signal level that can
be measured (usually the maximum safe
input level) to the displayed average noise
level (DANL). This ratio is almost always
much greater than can be realized in a
single measurement. See Dynamic range.
Mixing mode: A description of the particular
circumstance that creates a given response
on a spectrum analyzer. The mixing mode,
e.g., 1+, indicates the harmonic of the LO
used in the mixing process and whether the
input signal is above (+) or below (–) that
Multiple responses: Two or more responses
on a spectrum analyzer display from a single
input signal. Multiple responses occur only
when mixing modes overlap and the LO is
swept over a wide enough range to allow
the input signal to mix on more than one
mixing mode. Normally not encountered in
analyzers with preselectors.
Negative peak: The display detection mode
in which each displayed point indicates the
minimum value of the video signal for that
part of the frequency span or time interval
represented by the point.
Noise floor extension: Developed by
Keysight Technologies, Inc., a modeling
algorithm of the noise power in a signal
analyzer which can be subtracted from the
measurement results to reduce the effective
noise level.
Noise figure: The ratio, usually expressed in
dB, of the signal-to-noise ratio at the input
of a device (mixer, amplifier) to the signalto-noise ratio at the output of the device.
Noise marker: A marker whose value
indicates the noise level in a 1-Hz noise
power bandwidth. When the noise marker
is selected, the sample display detection
mode is activated, the values of a number
of consecutive trace points (the number
depends upon the analyzer) about the
marker are averaged, and this average
value is normalized to an equivalent
value in a 1-Hz noise power bandwidth.
The normalization process accounts for
detection and bandwidth plus the effect
of the log amplifier when we select the log
display mode.
Noise power bandwidth: A fictitious filter
that would pass the same noise power
as the analyzer’s actual filter, making
comparisons of noise measurements among
different analyzers possible.
Noise sidebands: Modulation sidebands
that indicate the short-term instability
of the LO (primarily the first LO) system
of a spectrum analyzer. The modulating
signal is noise, in the LO circuit itself
or in the LO stabilizing circuit, and the
sidebands comprise a noise spectrum.
The mixing process transfers any LO
instability to the mixing products, so the
noise sidebands appear on any spectral
component displayed on the analyzer far
enough above the broadband noise floor.
Because the sidebands are noise, their
level relative to a spectral component is
a function of resolution bandwidth. Noise
sidebands are typically specified in terms
of dBc/Hz (amplitude in a 1-Hz bandwidth
relative to the carrier) at a given offset from
the carrier, the carrier being a spectral
component viewed on the display.
Phase noise: See Noise sidebands.
Positive peak: The display detection mode
in which each displayed point indicates the
maximum value of the video signal for that
part of the frequency span or time interval
represented by the point.
Preamplifier: An external, low-noisefigure amplifier that improves system
(preamplifier/spectrum analyzer) sensitivity
over that of the analyzer itself.
Preselector: A tunable bandpass filter that
precedes the input mixer of a spectrum
analyzer and tracks the appropriate mixing
mode. Preselectors are typically used only
above 2 GHz. They essentially eliminate
multiple and image responses and, for
certain signal conditions, improve dynamic
Quasi-peak detector (QPD): A type of
detector whose output is a function of both
signal amplitude as well as pulse repetition
rate. The QPD gives higher weighting
to signals with higher pulse repetition
rates. In the limit, a QPD will exhibit
the same amplitude as a peak detector
when measuring a signal with a constant
amplitude (CW) signal.
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87 | Keysight | Spectrum Analysis Basics – Application Note 150
Raster display: A TV-like display in which
the image is formed by scanning the
electron beam rapidly across and slowly
down the display face and gating the beam
on as appropriate. The scanning rates
are fast enough to produce a flicker-free
display. Also see Vector display and Sweep
Real-time spectrum analyzer: A method
of signal analysis in which all signal
samples are processed for some sort
of measurement result or triggering
operation. There are no gaps between time
acquisitions while nonreal-time operations
leave gaps.
Reference level: The calibrated vertical
position on the display used as a reference
for amplitude measurements. The reference
level position is normally the top line of the
Relative amplitude accuracy: The
uncertainty of an amplitude measurement
in which the amplitude of one signal is
compared to the amplitude of another
regardless of the absolute amplitude of
either. Distortion measurements are relative
measurements. Contributors to uncertainty
include frequency response and display
fidelity and changes of input attenuation, IF
gain, scale factor and resolution bandwidth.
Rosenfell: The display detection mode in
which the value displayed at each point is
based upon whether or not the video signal
both rose and fell during the frequency
or time interval represented by the point.
If the video signal only rose or only fell,
the maximum value is displayed. If the
video signal did both rise and fall, then
the maximum value during the interval
is displayed by odd-numbered points,
the minimum value, by even-numbered
points. To prevent the loss of a signal that
occurs only in an even-numbered interval,
the maximum value during this interval is
preserved, and in the next (odd-numbered)
interval, the displayed value is the greater
of either the value carried over or the
maximum that occurs in the current interval.
Sample: The display detection mode in
which the value displayed at each point is
the instantaneous value of the video signal
at the end of the frequency span or time
interval represented by the point.
Residual FM: The inherent short-term
frequency instability of an oscillator in the
absence of any other modulation. In the
case of a spectrum analyzer, we usually
expand the definition to include the case
in which the LO is swept. Residual FM is
usually specified in peak-to-peak values
because they are most easily measured on
the display, if visible at all.
Scale factor: The per-division calibration of
the vertical axis of the display.
Sensitivity: The level of the smallest
sinusoid that can be observed on a
spectrum analyzer, usually under optimized
conditions of minimum resolution
bandwidth, 0-dB RF input attenuation and
minimum video bandwidth. Keysight defines
sensitivity as the displayed average noise
level. A sinusoid at that level will appear to
be about 2 dB above the noise.
Residual responses: Discrete responses
seen on a spectrum analyzer display with no
input signal present.
Shape factor: See Bandwidth selectivity.
Resolution: See Frequency resolution.
Resolution bandwidth: The width of
the resolution bandwidth (IF) filter of a
spectrum analyzer at some level below the
minimum insertion loss point (maximum
deflection point on the display). For Keysight
analyzers, the 3-dB bandwidth is specified;
for some others, it is the 6-dB bandwidth.
Signal identification: A routine, either
manual or automatic, that indicates
whether or not a particular response on
the spectrum analyzer’s display is from
the mixing mode for which the display is
calibrated. If automatic, the routine may
change the analyzer’s tuning to show the
signal on the correct mixing mode, or it may
tell us the signal’s frequency and give us
the option of ignoring the signal or having
the analyzer tune itself properly for the
signal. Generally not needed on preselected
Span accuracy: The uncertainty of the
indicated frequency separation of any two
signals on the display.
Spectral purity: See Noise sidebands.
Spectral component: One of the sine waves
comprising a spectrum.
Spectrum: An array of sine waves of
differing frequencies and amplitudes and
properly related with respect to phase that,
taken as a whole, constitute a particular
time-domain signal.
Spectrum analyzer: A device that
effectively performs a Fourier transform and
displays the individual spectral components
(sine waves) that constitute a time-domain
signal. Phase may or may not be preserved,
depending upon the analyzer type and
Spurious responses: The improper
responses that appear on a spectrum
analyzer display as a result of the input
signal. Internally generated distortion
products are spurious responses, as are
image and multiple responses.
Signal analyzer: A spectrum analyzer
that also uses digital signal processing to
perform other more complex measurements
such as vector signal analysis.
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Sweep time: The time to tune the LO
across the selected span. Sweep time
does not include the dead time between
the completion of one sweep and the start
of the next. In zero span, the spectrum
analyzer’s LO is fixed, so the horizontal
axis of the display is calibrated in time
only. In nonzero spans, the horizontal axis
is calibrated in both frequency and time,
and sweep time is usually a function of
frequency span, resolution bandwidth and
video bandwidth.
Time gating: A method of controlling the
frequency sweep of the spectrum analyzer
based on the characteristics of the signal
being measured. It is often useful when
analyzing pulsed or burst modulated
signals’ time-multiplexed signals and
intermittent signals.
TDMA: Time division multiple access is a
digital communication method in which
multiple communication streams are
interleaved in time, enabling them to share a
common frequency channel.
Units: Dimensions of the measured
quantities. Units usually refer to amplitude
quantities because they can be changed.
In modern spectrum analyzers, available
units are dBm (dB relative to 1 milliwatt
dissipated in the nominal input impedance
of the analyzer), dBmV (dB relative to 1
millivolt), dBuV (dB relative to 1 microvolt),
volts, and in some analyzers, watts. In
Keysight analyzers, we can specify any units
in both log and linear displays.
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Vector diagram: A display type commonly
used when analyzing digitally modulated
signals. It is similar to a constellation
display, except that in addition to the
detected symbol points, the instantaneous
power levels during state transitions are
also plotted on an IQ graph.
Vector display: A display type used in
earlier spectrum analyzer designs, in which
the electron beam was directed so that
the image (trace, graticule, annotation)
was written directly on the CRT face, not
created from a series of dots as in the raster
displays commonly used today.
Video: In a spectrum analyzer, a term
describing the output of the envelope
detector. The frequency range extends from
0 Hz to a frequency typically well beyond
the widest resolution bandwidth available
in the analyzer. However, the ultimate
bandwidth of the video chain is determined
by the setting of the video filter.
Video amplifier: A post-detection, DCcoupled amplifier that drives the vertical
deflection plates of the CRT. See Video
bandwidth and Video filter.
Video average: A digital averaging of a
spectrum analyzer’s trace information.
The averaging is done at each point of the
display independently and is completed
over the number of sweeps selected by the
user. The averaging algorithm applies a
weighting factor (1/n, where n is the number
of the current sweep) to the amplitude
value of a given point on the current sweep,
applies another weighting factor [(n – 1)/n]
to the previously stored average, and
combines the two for a current average.
After the designated number of sweeps are
completed, the weighting factors remain
constant, and the display becomes a
running average.
Video bandwidth: The cutoff frequency (3dB point) of an adjustable low-pass filter in
the video circuit. When the video bandwidth
is equal to or less than the resolution
bandwidth, the video circuit cannot fully
respond to the more rapid fluctuations of
the output of the envelope detector. The
result is a smoothing of the trace, i.e., a
reduction in the peak-to-peak excursion of
broadband signals such as noise and pulsed
RF when viewed in the broadband mode.
The degree of averaging or smoothing is a
function of the ratio of the video bandwidth
to the resolution bandwidth.
Video filter: A post-detection, low-pass
filter that determines the bandwidth of the
video amplifier. Used to average or smooth
a trace. See Video bandwidth.
Zero span: That case in which a spectrum
analyzer’s LO remains fixed at a given
frequency so the analyzer becomes a
fixed-tuned receiver. The bandwidth of
the receiver is that of the resolution (IF)
bandwidth. Signal amplitude variations are
displayed as a function of time. To avoid any
loss of signal information, the resolution
bandwidth must be as wide as the signal
bandwidth. To avoid any smoothing, the
video bandwidth must be set wider than the
resolution bandwidth.
Änderungen und Irrtümer vorbehalten. dataTec 19-04-2016 | © Keysight Technologies 2016, April 15, 2016 | 5952-0292
88 | Keysight | Spectrum Analysis Basics – Application Note 150
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