Master of Technology
Digital Signal Processing
Under the guidance of
Department of Electronics & Communication Engineering
National Institute of Technology
National Institute of Technology
This is to certify that the Thesis Report entitled “DSP Implementation of OFDM
Acoustic Modem” submitted by Mr. Madhu.A (20607021) in partial fulfillment of the
requirements for the award of Master of Technology degree in Electronics and
Communication Engineering with specialization in “Digital Signal Processing” during
session 2007-2008 at National Institute Of Technology, Rourkela (Deemed University)
and is an authentic work by him under my supervision and guidance.
To the best of my knowledge, the matter embodied in the thesis has not been submitted to
any other university/institute for the award of any Degree or Diploma.
Prof. G.S.RATH
Dept. of E.C.E
National Institute of Technology
First of all, I would like to express my deep sense of respect and gratitude towards
my advisor and guide Prof. G.S.Rath, who has been the guiding force behind this work.
I am greatly indebted to him for his constant encouragement, invaluable advice and for
propelling me further in every aspect of my academic life. His presence and optimism
have provided an invaluable influence on my career and outlook for the future. I
consider it my good fortune to have got an opportunity to work with such a wonderful
Next, I want to express my respects to Prof. S.K.Patra, Prof.G.Panda, Prof.
K.K. Mahapatra, and Dr. S. Meher for teaching me and also helping me how to learn.
They have been great sources of inspiration to me and I thank them from the bottom of
my heart.
I would like to thank all faculty members and staff of the Department of
Electronics and Communication Engineering, N.I.T. Rourkela for their generous help in
various ways for the completion of this thesis.
I would also like to mention the name of Jithendra Kumar Das (Ph.D) for
helping me a lot during the thesis period.
I would like to thank all my friends and especially my classmates for all the
thoughtful and mind stimulating discussions we had, which prompted us to think beyond
the obvious. I’ve enjoyed their companionship so much during my stay at NIT, Rourkela.
I am especially indebted to my parents for their love, sacrifice, and support. They
are my first teachers after I came to this world and have set great examples for me about
how to live, study, and work.
Roll No: 20607021
Dept of ECE, NIT, Rourkela.
List of figures
List of tables
Chapter1 introduction
1.1 Introduction
1.2 Motivation
1.3 Background literature survey
1.3.1 Digital audio broadcasting
1.3.2 Digital video broadcasting
1.3.3 HperLAN2 and IEEE 802.11a
1.3.4 Under Water Sensor Networks
1.4 Thesis contribution
1.5 Thesis outline
Chapter2 Under Water Sensor Networks
2.1 Introduction
2.2 Characterstics of the Enviornment
2.2.1.Basics of Acoustic Communications
2.2.2. Underwater acoustic channels
2.2.3. Distinctions between mobile UWSNs and ground-based networks
2.2.4. Current underwater network systems
Chapter 3 OFDM
3.1 Introduction
3.2 The single carrier modulation system
3.3 Frequency division multiplexing modulation system
3.4 Orthogonality and OFDM
3.5 Mathematical analysis
3.6 OFDM generation and reception
3.6.1 Error correction codes
3.6.2 Data Interleaving
3.6.3 Sub carrier Modulation
3.7 OFDM symbol
3.7.1 OFDM Transmitter
3.7.2 Pilot Insertion
3.7.3 Preamable
3.8 Frequency to Time domain conversion
3.9 RF modulation
3.10 Guard period
3.10.1 Protection against time offset
3.10.2 Guard period overhead and subcarrier spacing
3.10.3 Intersymbol interference
3.10.4 Intrasymbol interference
3.11 Advantages and disadvantages of OFDM as compared to single carrier mod 33
3.11.1 Advantages
3.11.2 Disadvantages
Chapter 4 Digital Signal Processing
4.1 introduction to DSP
4.2 How DSP’s different fromohter Microprocessors
4.3 Introduction to the TMS320C6000 Platform of DSP’s
4.3.1 TMS320C6713 DSP Description
4.4 DSP Implementation
4.4.1 Tramsmitter Design
4.4.2 Channel Noise
4.4.3 Receiver Design
Chapter 5 Simulation results & discussions
5.1 Introduction
5.2 simulation model
5.3 OFDM Frame synchronization
5.4 Performance tests
5.5 conclusion
The success of multicarrier modulation in the form of OFDM in radio channels
illuminates a path one could take towards high-rate underwater acoustic communications,
and recently there are intensive investigations on underwater OFDM. Processing power
has increased to a point where orthogonal frequency division multiplexing (OFDM) has
become feasible and economical. Since many wireless communication systems being
developed use OFDM, it is a worthwhile research topic. Some examples of applications
using OFDM include Digital subscriber line (DSL), Digital Audio Broadcasting (DAB),
High definition television (HDTV) broadcasting, IEEE 802.11 (wireless networking
standard).OFDM is a strong candidate and has been suggested or standardized in high
speed communication systems.
In this Thesis in first phase ,we analyzes the factor that affects the OFDM
performance. The performance of OFDM was assessed by using computer simulations
performed using Matlab7.2 .it was simulated under Additive white Gaussian noise
(AWGN) ,Exponential Multipath channel and Carrier frequency offset conditions for
different modulation schemes like binary phase shift keying (BPSK), Quadrature phase
shift keying (QPSK), 16-Quadrature amplitude modulation (16-QAM), 64-Quadrature
amplitude modulation (64-QAM) which are used for achieving high data rates.
In second phase we implement the acoustic OFDM transmitter and receiver design of [4,
5] on a TMS320C6713 DSP board. We analyze the workload and identify the most timeconsuming operations. Based on the workload analysis, we tune the algorithms and
optimize the code to substantially reduce the synchronization time to 0.2 seconds and the
processing time of one OFDM block to 2.7235 seconds on a DSP processor at 225 MHz.
This experimentation provides guidelines on our future work to reduce the per-block
processing time to be less than the block duration of 0.23 seconds for real time operations
2.1 Scenario of the mobile UWSN architecture
3.1 Single carrier spectrum
3.2 FDM signal spectrum
3.3 Block diagram of a basic OFDM transceiver
3.4. Convolutional encoder
3.5 ASK modulation
3.6 FSK modulation
3.7 IQ modulation constellation, 16-QAM
3.8 Sub carrier spacing
3.7 OFDM generation, IFFT stage
3.8 RF modulation of complex base band OFDM signal, using analog techniques 29
3.10 Addition of a guard period to an OFDM signal
3.11 Example of intersymbol interference. The green symbol was transmitted first,
followed by the blue symbol.
4.1 Typical components of DSP system .
4.2 TMS320c6000 block diagram
4.3 Architecture os TMS320c6713DSK
4.4Implemented on TMS320c6713DSP using TMDSDSK6713 Evaluation Board 42
4.5. Stored speech signal in Buffer
4.6 Recovered speech signal
5.1 simulation model of OFDM
5.2 Inter Frame Interference
5.3 Detail of frame Synchronization Technique
5.4 Detail of frame synchronization technique under SNR=10,delay spread=50ms 51
5.5 Probability of synchronization failure curves under different channels
5. Variation of MSE Vs SNR under different channels
5.7 BER vs. SNR plot for OFDM using BPSK, QPSK, 16-QAM, 64-QAM
5.1 OFDM simulation parameters
5.2 Execution Time(in sec) Measured in CCS profiler
additive White Gaussian Noise
asymmetric digital subscriber line
binary phase shift keying
complementary code keying
code composer studio
code division multiple access
digital signal processors
digital audio broadcasting
digital video broadcasting
discrete Fourier transform
direct sequence spread spectrum
European Telecommunications Standards Institute
fast Fourier transform
frequency division multiplexing
forward error correction
high definition television
Institute of Electrical and Electronics Engineers
inverse Fourier transform
inverse discrete Fourier transform
inter symbol interference
inter carrier interference
local area network
National Television Systems Committee
orthogonal frequency division multiplexing
quadrature phase shift keying
quadrature amplitude modulation
signal to noise ratio
time division multiplexing
time division multiple access
ultra high frequency
very large scale integration
wireless local area networks
A C (t)
Amplitude of the carrier
ωc (t)
Carrier frequency
φc (t)
Phase of the carrier
ss (t)
Complex signal of OFDM
Carrier frequency
Sampling rate
Length of the symbol in samples
Length of the guard period in samples
FFT period in samples
Time of the channel in samples
Cyclic prefix length in samples
x (n)
original signal
X (k)
Fourier transform of x (n)
Twiddle factors
Subcarrier spacing
size of the FFT
f1 (n)
even numbered samples
f2 (n)
odd numbered samples
F1 (k)
N/2-point DFT of f1 (n)
F2 (k)
N/2-point DFT of f2 (n)
The ever increasing demand for very high rate wireless data transmission calls for
technologies which make use of the available electromagnetic resource in the most
intelligent way. Key objectives are spectrum efficiency (bits per second per Hertz),
implementation complexity. These objectives are often conflicting, so techniques and
implementations are sought which offer the best possible tradeoff between them.
The Internet revolution has created the need for wireless technologies that can
deliver data at high speeds in a spectrally efficient manner. However, supporting such
high data rates with sufficient robustness to radio channel impairments requires careful
selection of modulation techniques. Currently, the most suitable choice appears to be
OFDM (Orthogonal Frequency Division Multiplexing).The main reason that the OFDM
technique has taken a long time to become a prominence has been practical. It has been
difficult to generate such a signal, and even harder to receive and demodulate the signal.
The hardware solution, which makes use of multiple modulators and demodulators, was
somewhat impractical for use in the civil systems.
OFDM transmits a large number of narrowband carriers, closely spaced in the
frequency domain. In order to avoid a large number of modulators and filters at the
transmitter and complementary filters and demodulators at the receiver, it is desirable to
be able to use modern digital signal processing techniques, such as fast Fourier transform
The ability to define the signal in the frequency domain, in software on VLSI
(very large scale integration) processors, and to generate the signal using the inverse
Fourier transform is the key to its current popularity. Although the original proposals
were made a long time ago, it has taken some time for technology to catch up.OFDM is
currently being used for digital audio and video broadcasting. OFDM for wireless LANs
is being used every where now, is operating in the unlicensed bands and is also being
considered as a serious candidate for fourth generation cellular systems.
This chapter begins with an exposition of the principle motivation behind the
work undertaken in this thesis. Following this section 1.3 provides literature survey on
OFDM. Section 1.4 discusses the contribution in this thesis. At the end, section 1.5
presents thesis outline.
OFDM is the modulation technique used in many new and emerging broadband
communication systems including wireless local area networks (WLANs), high definition
television (HDTV) and 4G systems. To achieve high data rates OFDM is used in wireless
LAN standards like IEEE 802.11a, IEEE 802.11g. The key component in an OFDM
transmitter is an inverse fast Fourier transform (IFFT) and in the receiver, an FFT. The
increasing computational power and performance capabilities of DSPs make them ideal
for the practical implementation of OFDM functions.
The motivation for using OFDM techniques over TDMA techniques is twofold.
First, TDMA limits the total number of users that can be sent efficiently over a channel.
In addition, since the symbol rate of each channel is high, problems with multipath delay
spread invariably occur. In stark contrast, each carrier in an OFDM signal has a very
narrow bandwidth (i.e. 1kHz); thus the resulting symbol rate is low. This results in the
signal having a high degree of tolerance to multipath delay spread, as the delay spread
must be very long to cause significant inter-symbol interference.
Orthogonal Frequency Division Multiplexing (OFDM) is an alternative wireless
modulation technology to CDMA. OFDM has the potential to surpass the capacity of
CDMA systems and provide the wireless access method for 4G systems. OFDM is a
modulation scheme that allows digital data to be efficiently and reliably transmitted over
a radio channel, even in multipath environments. In a typical orthogonal frequency
division multiplexing (OFDM) broadband wireless communication system, a guard
interval using cyclic prefix is inserted to avoid the intersymbol interference and the intercarrier interference. This guard interval is required to be at least equal to, or longer than
the maximum channel delay spread. This method is very simple, but it reduces the
transmission efficiency. This efficiency is very low in the communication systems, which
inhibit a long channel delay spread with a small number of sub-carriers such as the IEEE
802.11a wireless LAN (WLAN).
The origins of OFDM development started in the late 1950’s [11]. with the
introduction of Frequency Division Multiplexing (FDM) for data communications. In
1966 Chang patented the structure of OFDM [2] and published [13] the concept of using
orthogonal overlapping multi-tone signals for data communications. In 1971 Weinstein
[14] introduced the idea of using a Discrete Fourier Transform (DFT) for implementation
of the generation and reception of OFDM signals, eliminating the requirement for banks
of analog subcarrier oscillators. This presented an opportunity for an easy implementation
of OFDM, especially with the use of Fast Fourier Transforms (FFT), which are an
efficient implementation of the DFT. This suggested that the easiest implementation of
OFDM is with the use of Digital Signal Processing (DSP), which can implement FFT
algorithms. It is only recently that the advances in integrated circuit technology have
made the implementation of OFDM cost effective. The reliance on DSP prevented the
wide spread use of OFDM during the early development of OFDM. It wasn’t until the
late 1980’s that work began on the development of OFDM for commercial use, with the
introduction of the Digital Audio Broadcasting (DAB) system.
1.3.1 Digital audio broadcasting
DAB was the first commercial use of OFDM technology [5]. Development of DAB
started in 1987 and services began in U.K and Sweden in1995. DAB is a replacement for
FM audio broadcasting, by providing high quality digital audio and information services.
OFDM was used for DAB due to its multipath tolerance.
Broadcast systems operate with potentially very long transmission distances (20 100 km). As a result, multipath is a major problem as it causes extensive ghosting of the
transmission. This ghosting causes Inter-Symbol Interference (ISI), blurring the time
domain signal.
For single carrier transmissions the effects of ISI are normally mitigated using
adaptive equalization. This process uses adaptive filtering to approximate the impulse
response of the radio channel. An inverse channel response filter is then used to
recombine the blurred copies of the symbol bits. This process is however complex and
slow due to the locking time of the adaptive equalizer. Additionally it becomes increasing
difficult to equalize signals that suffer ISI of more than a couple of symbol periods.
OFDM overcomes the effects of multipath by breaking the signal into many
narrow bandwidth carriers. This results in a low symbol rate reducing the amount of ISI.
In addition to this, a guard period is added to the start of each symbol, removing the
effects of ISI for multipath signals delayed less than the guard period. The high tolerance
to multipath makes OFDM more suited to high data transmissions in terrestrial
environments than single carrier transmissions.
The data throughput of DAB varies from 0.6 - 1.8 Mbps depending on the amount
of Forward Error Correction (FEC) applied. This data payload allows multiple channels
to be broadcast as part of the one transmission ensemble. The number of audio channels
is variable depending on the quality of the audio and the amount of FEC used to protect
the signal. For telephone quality audio (24 kbps) up to 64 audio channels can be
provided, while for CD quality audio (256 kb/s), with maximum protection, three
channels are available.
1.3.2 Digital video broadcasting
The development of the Digital Video Broadcasting (DVB) standards was started in
1993. DVB is a transmission scheme based on the MPEG-2 standard, as a method for
point to multipoint delivery of high quality compressed digital audio and video. It is an
enhanced replacement of the analogue television broadcast standard, as DVB provides a
flexible transmission medium for delivery of video, audio and data services [6]. The
DVB standards specify the delivery mechanism for a wide range of applications,
including satellite TV (DVB-S), cable systems (DVB-C) and terrestrial transmissions
(DVB-T). The physical layer of each of these standards is optimized for the transmission
channel being used. Satellite broadcasts use a single carrier transmission, with QPSK
modulation, which is optimized for this application as a single carrier allows for large
Doppler shifts, and QPSK allows for maximum energy efficiency [7]. This transmission
method is however unsuitable for terrestrial transmissions as multipath severely degrades
the performance of high-speed single carrier transmissions. For this reason, OFDM was
used for the terrestrial transmission standard for DVB. The physical layer of the DVB-T
transmission is similar to DAB, in that the OFDM transmission uses a large number of
subcarriers to mitigate the effects of multipath. DVB-T allows for two transmission
modes depending on the number of subcarriers used [8].The major difference between
DAB and DVB-T is the larger bandwidth used and the use of higher modulation schemes
to achieve a higher data throughput. The DVB-T allows for three subcarrier modulation
schemes: QPSK, 16-QAM (Quadrature Amplitude Modulation) and 64- QAM; and a
range of guard period lengths and coding rates. This allows the robustness of the
transmission link to be traded at the expense of link capacity.
1.3.3 Hiperlan2 and IEEE802.11a
Development of the European Hiperlan standard was started in 1995, with the final
standard of HiperLAN2 being defined in June 1999. HiperLAN2 pushes the performance
of WLAN systems, allowing a data rate of up to 54 Mbps [9]. HiperLAN2 uses 48 data
and 4 pilot subcarriers in a 16 MHz channel, with 2 MHz on either side of the signal to
allow out of band roll off. User allocation is achieved by using TDM, and subcarriers are
allocated using a range of modulation schemes, from BPSK up to 64-QAM, depending
on the link quality. Forward Error Correction is used to compensate for frequency
selective fading. IEEE802.11a has the same physical layer as HiperLAN2 with the main
difference between the standard corresponding to the higher-level network protocols
used.HiperLAN2 is used extensively as an example OFDM system in this thesis. Since
the physical layer of HiperLAN2 is very similar to the IEEE802.11a standard these
examples are applicable to both standards.
1.3.4 Underwater Wireless Sensor Networks (UWSN)
Recently, there has been a growing interest in monitoring aqueous environments
including oceans, rivers, lakes, ponds,and reservoirs, etc.) for scientific exploration,
commercial exploitation, and protection from attacks. The ideal vehicle for this type of
extensive monitoring is a networked underwater wireless sensor distributed system,
referred to as the Underwater Wireless Sensor Network (UWSN). Establishing effective
communications among a distributed set of both stationary and mobile sensors is one key
step toward UWSNs. Since electromagnetic waves do not propagate well in underwater
environments, underwater communications have to rely on other physical means, such as
sound, to transmit signals . Unlike the rapid growth of wireless networks over radio
channels, the development of underwater communication has been at a much slower
pace. The last two decades have witnessed only two fundamental advances in underwater
acoustic communications. One is the introduction of digital communication techniques,
namely, noncoherent frequency shift keying (FSK), in the early 1980s, and the other
is the application of coherent modulation, including phase shift keying (PSK) and
quadrature amplitude modulation (QAM) in early 1990s. Existing underwater coherent
communication has mainly relied on serial single-carrier transmission and equalization
techniques over the challenging underwater acoustic media . As the data rates increase,
the symbol durations decrease, and thus the same physical underwater channel contains
more channel taps in the baseband discretetime model (easily on the order of several
hundreds of taps).This poses great challenges for the channel equalizer. Receiver
complexity will prevent any substantial rate improvement with existing approaches.
Due to its low equalization complexity in the presence of highly-dispersive channels,
multicarrier modulation in the form of orthogonal frequency division multiplexing
(OFDM) has prevailed in recent broadband wireless systems. Motivated by the success of
OFDM in radio channels, there is a recent re-emergence of interest in applying OFDM in
underwater acoustic channels.
Multicarrier modulation in the form of orthogonal frequency division multiplexing
(OFDM) has been quite successful in broadband wireless communication over radio
channels, e.g., wireless local area networks (IEEE 802.11a/g/n). Motivated by this fact,
researchers have long attempted to apply OFDM in underwater acoustic channels.
Recently, we have seen intensive investigations on underwater OFDM, including [2] on a
low-complexity adaptive OFDM receiver, and [3, 4] on a pilot-tone based block-by-block
receiver. As a senior design project, an undergraduate team at University of Connecticut
has demonstrated multicarrier OFDM transmission and reception in air and in a water
tank, where the algorithms in [3, 4] are implemented by Matlab programs in two laptops
In this Dissertation, in the first phase we simulated the OFDM transmission and reception
algorithms of [3, 4] in MATLAB 7.2.and compared the results. In the second phase we
generated the “c” code to execute on a TI TMS320C6713 DSP board with a processor
running at 225 MHz. In-wired communications are successfully tested. We analyze the
workload and identify the most time-consuming operations..
Following this introduction chapter, Chapter2 describe the motivations, features of
aquatic environment, the difficulties of underwater acoustic channels, and the open
questions in mobile underwater sensor network design.
Chapter3 provides an introduction to OFDM in general and outlines some of the
problems associated with it. This chapter describes what OFDM is, and how it can be
generated and received. It also looks at why OFDM is a robust modulation scheme and
some of its advantages and disadvantages over single carrier modulation schemes. It also
discusses the some of the applications of OFDM.
Chapter 4 starts with features of Digital Signal Processors especially
TMS3206000 (TMS320c6713DSK) family device which we used for the current modem
design, and also discusses the design parameters of Transmitter and Receiver.
Chapter 5 provides the results obtained in this thesis, and their discussions. It
provides the OFDM system model used in the simulation. It shows the results of bit error
rate performance against signal to noise ratio for different modulation schemes used for
the current design. it also discusses about the simulation results of OFDM Frame
Synchronization and probability of error occurring under different channels. This chapter
ends up with the achievement of the thesis work, limitations of the work, and future
directions of the work.
The earth is a water planet. Currently, there has been a growing interest in monitoring
underwater mediums for scientific exploration, commercial exploitation, and attack
protection. A distributed underwater wireless sensor network (UWSN) is the ideal vehicle
for this monitoring. A scalable UWSN is a good solution for exploring the aquatic
environments. By deploying scalable wireless sensor networks in 3-dimensional
underwater space, each underwater sensor can monitor and find environmental events.
The aqueous systems are also dynamic and processes happen within the water mass as it
disperses within the environment. In a mobile underwater sensor network, the sensor
mobility has two major benefits:
1. Mobile sensors injected in the current in relative large numbers can help to track
changes in the water mass, thus provide 4D (space and time) environmental sampling.
2. Floating sensors can help to form dynamic monitoring coverage and increase system
The self-organizing network of mobile sensors produces better supports in sensing,
monitoring, surveillance, scheduling, underwater control, and failing tolerance. Mobile
UWSNs have to use acoustic communications, since radio does not work well in
underwater environments. Due to the unique features of large latency, low bandwidth,
and high error rate, underwater acoustic channels bring much defiance to the protocol
design. Furthermore, the best parts of underwater nodes are mobile due to water currents.
This mobility is another problem to consider in the system design.
Fig. 2.1 Scenario of the mobile UWSN architecture
2.2. Characteristics of the environment
2.2.1. Basics of acoustic communications
Underwater acoustic communications depend on path loss, noise, multi-path, Doppler
spread, and high and variable propagation delay. All these aspects establish the temporal
and spatial variability of the acoustic channel. Therefore the available bandwidth of the
underwater acoustic channel is severely limited and dependent on both range and
frequency. In long-range systems and shortrange system these factors lead to low bit
rates. In addition, the communication range is reduced as compared to the terrestrial radio
channel.Underwater acoustic communication links can be classified depending on their
range. M oreover, acoustic links are classified as vertical and horizontal, according to the
direction of the sound ray. Their propagation attributes differ consistently, especially with
respect to time dispersion, multi-path spreads, and delay variance. Now, we analyze the
factors that influence acoustic communications in order to state the challenges posed by
the underwater channels for underwater sensor networking.
Path loss:
Attenuation: Is mainly caused by absorption due to conversion of acoustic energy
into heat, which increases with distance and frequency.It is also caused by
scattering and reverberation, refraction, and dispersion. Water depth is
determinant in the attenuation.
Geometric Spreading: This refers to the spreading of sound energy as a result of
the expansion of the wave-fronts. It increases with the propagation distance and is
independent of frequency. There are two types of geometric spreading: spherical
(Omni-directional point source), and cylindrical (horizontal radiation only). The
cylindrical spreading appears in water with depth less than 100m (shallow water)
because acoustic signals propagate with a cylinder bounded by the surface and the
sea floor. When sea is deep enough the propagation range is not bounded so that
spherical spreading applies.
Man made noise: This is mainly caused by machinery noise and shipping
Ambient Noise: Is related to hydrodynamics, seismic and biological phenomena.
Multi-path propagation: This may be responsible for severe degradation of the
acoustic communication signal, since it generates Inter-Symbol Interference.
The multi-path geometry: It depends on the link configuration. Vertical channels
are characterized by little time dispersion, while horizontal 4 Desenvolupament,
proves de camp i anàlisi de resultats en una xarxa de sensors channels may have
extremely long multi-path spreads, whose value depend on the water depth.
High delay and delay variance:
Delay: The propagation speed in the underwater acoustic channels is five orders
of magnitude lower than in the radio channel. This large propagation delay can
reduce the throughput of the system considerably.
Delay variance: The very high delay variance is even more harmful for efficient
protocol design, as it prevents from accurately estimating the round trip time, key
measure for many common communication protocols.
Doppler spread:
The Doppler frequency spread can be significant in underwater acoustic
channels, causing degradation in the performance of digital communications.
High data rate communications cause many adjacent symbols to interfere at the
receiver, requiring sophisticated signal processing to deal with the generated ISI.
2.2.2. Underwater acoustic channels
Underwater acoustic channels are temporally spatially and variable due to the
characteristics of the transmission medium and physical properties of the environments.
The signal propagation speed in underwater acoustic channel is about 1.5 × 103 m/sec.
The convenient bandwidth of underwater acoustic channels is limited and dramatically
depends on both transmission range and frequency. The acoustic band under water is
restricted due to absorption.
The bandwidth of underwater acoustic channels working over several kilometers is about
several tens of kbps, whereas short-range systems over several tens of meters can reach at
hundreds of kbps. The path loss, noise, multipath, and Doppler spread affect the
underwater acoustic communication channels. All these factors generate high bit-error
and delay variance.
2.2.3. Distinctions between mobile UWSNs and ground-based sensor networks
A mobile UWSN is very different from any ground-based sensor network in the
following aspects:
Communication Method: Electromagnetic waves cannot propagate over a long
distance in underwater environments. Each underwater wireless link features
large latency and low-bandwidth. Due to such distinct network dynamics,
communication protocols used in ground-based sensor networks may not be
appropriate in underwater sensor networks.
Node Mobility: The sensor nodes in ground-based sensor networks are fixed,
though it is possible to implement interactions between these static sensor nodes
and a limit number of mobile nodes. However, the best part of underwater sensor
nodes are with low or medium mobility due to water current and other
underwater activities. From experimental observations, underwater objects may
move at the speed of 3-6 kilometers per hour in a typical underwater condition.
2.2.4. Current underwater network systems
An underwater sensor network is a next step forward with respect to existing small-scale
Underwater Acoustic Networks (UANs). UANs are associations of nodes that collect data
using remote telemetry or assuming point-to-point communications. The different
between UANs and underwater sensor networks are the following:
Scalability: A mobile underwater sensor network is a scalable sensor network,
which relies on localized sensing and coordinated networking among large
numbers of sensors. In contrast, an existing underwater acoustic network is a
small-scale network relying on data collecting strategies like remote telemetry or
assuming that communication is pointto- point. In remote telemetry, long-range
signals remotely collect data. In point-to-point communication, a multi-access
technique is not necessary.
Self-organization: Usually, in underwater acoustic networks nodes are fixed,
while a mobile underwater sensor network is a self-organizing network.
Underwater sensor nodes may be redistributed and moved by the aqueous
processes of advection and dispersion. Thus, sensors should automatically adjust
their buoyancy, moving up and down based on measured data density. In this
way, sensors are mobile in order to track changes in the water mass rather than
make observations at a fixed point.
Localization: In underwater acoustic networks sensors localization is not desired
because nodes are usually fixed. In mobile underwater sensor networks,
localization is required because the majority of the sensors are mobile with the
current. Determining the locations of mobile sensors in aquatic environments is
very challenging. We need to face the limited communication capabilities of
acoustic channels. Moreover, we need improving the localization accuracy
The principle of orthogonal frequency division multiplexing (OFDM) modulation has
been in existence for several decades. However, in recent years these techniques have
quickly moved out of textbooks and research laboratories and into practice in modern
communications systems. The techniques are employed in data delivery systems over the
phone line, digital radio and television, and wireless networking systems [14]. What is
OFDM? And why has it recently become so popular?
This chapter is organized as follows. Following this introduction, section 3.2, 3.3 gives
brief details about single carrier modulation, FDM modulation systems. Section 3.4
discusses definition of orthogonality, and principle of OFDM.section 3.5 discusses the
how FFT maintains orthogonality.section 3.6 discusses the generation and reception of
OFDM in detail. Section 3.7 addresses about the guard period used in OFDM systems.
Section 3.8 presents the advantages, disadvantages and applications of OFDM. Finally
section 3.9 concludes the chapter.
Fig.3.1 Single carrier spectrum
A typical single-carrier modulation spectrum is shown in Figure 3.1. A single carrier
system modulates information onto one carrier using frequency, phase, or amplitude
adjustment of the carrier. For digital signals, the information is in the form of bits, or
collections of bits called symbols, that are modulated onto the carrier. As higher
bandwidths (data rates) are used, the duration of one bit or symbol of information
becomes smaller. The system becomes more susceptible to loss of information from
impulse noise, signal reflections and other impairments. These impairments can impede
the ability to recover the information sent. In addition, as the bandwidth used by a single
carrier system increases, the susceptibility to interference from other continuous signal
sources becomes greater. This type of interference is commonly labeled as carrier wave
(CW) or frequency interference.
A typical Frequency division multiplexing signal spectrum is shown in figure 3.2.FDM
extends the concept of single carrier modulation by using multiple sub carriers within the
same single channel. The total data rate to be sent in the channel is divided between the
various sub carriers. The data do not have to be divided evenly nor do they have to
originate from the same information source. Advantages include using separate
modulation demodulation customized to a particular type of data, or sending out banks of
dissimilar data that can be best sent using multiple, and possibly different, modulation
Fig 3.2 FDM signal spectrum
Current national television systems committee (NTSC) television and FM stereo
multiplex are good examples of FDM. FDM offers an advantage over single-carrier
modulation in terms of narrowband frequency interference since this interference will
only affect one of the frequency sub bands. The other sub carriers will not be affected by
the interference. Since each sub carrier has a lower information rate, the data symbol
periods in a digital system will be longer, adding some additional immunity to impulse
noise and reflections. FDM systems usually require a guard band between modulated sub
carriers to prevent the spectrum of one sub carrier from interfering with another. These
guard bands lower the system’s effective information rate when compared to a single
carrier system with similar modulation.
If the FDM system above had been able to use a set of sub carriers that were orthogonal
to each other, a higher level of spectral efficiency could have been achieved. The guard
bands that were necessary to allow individual demodulation of sub carriers in an FDM
system would no longer be necessary. The use of orthogonal sub carriers would allow the
sub carriers’ spectra to overlap, thus increasing the spectral efficiency. As long as
orthogonality is maintained, it is still possible to recover the individual sub carriers’
signals despite their overlapping spectrums. If the dot product of two deterministic
signals is equal to zero, these signals are said to be orthogonal to each other.
Orthogonality can also be viewed from the standpoint of stochastic processes. If two
random processes are uncorrelated, then they are orthogonal. Given the random nature of
signals in a communications system, this probabilistic view of orthogonality provides an
intuitive understanding of the implications of orthogonality in OFDM.
OFDM is implemented in practice using the discrete Fourier transform (DFT). Recall from
signals and systems theory that the sinusoids of the DFT form an orthogonal basis set, and
a signal in the vector space of the DFT can be represented as a linear combination of the
orthogonal sinusoids. One view of the DFT is that the transform essentially correlates its
input signal with each of the sinusoidal basis functions. If the input signal has some energy
at a certain frequency, there will be a peak in the correlation of the input signal and the
basis sinusoid that is at that corresponding frequency. This transform is used at the OFDM
transmitter to map an input signal onto a set of orthogonal sub carriers, i.e., the orthogonal
basis functions of the DFT. Similarly, the transform is used again at the OFDM receiver to
process the received sub carriers. The signals from the sub carriers are then combined to
form an estimate of the source signal from the transmitter. The orthogonal and uncorrelated
nature of the sub carriers is exploited in OFDM with powerful results. Since the basis
functions of the DFT are uncorrelated, the correlation performed in the DFT for a given sub
carrier only sees energy for that corresponding sub carrier. The energy from other sub
carriers does not contribute because it is uncorrelated. This separation of signal energy is
the reason that the OFDM sub carriers’ spectrums can overlap without causing interference.
With an overview of the OFDM system, it is valuable to discuss the mathematical
definition of the modulation system. It is important to understand that the carriers
generated by the IFFT chip are mutually orthogonal. This is true from the very basic
definition of an IFFT signal. This will allow understanding how the signal is generated
and how receiver must operate.
Mathematically, each carrier can be described as a complex wave:
SC (t) = A C (t)e j( ωc (t ) +Φc(t ))
The real signal is the real part of sc (t). Ac (t) and φc (t), the amplitude and phase of the carrier, can vary
on a symbol by symbol basis. The values of the parameters are constant over the symbol duration period
t. OFDM consists of many carriers. Thus the complex signal Ss(t) is represented by:
1 N −1
ss (t) = ∑ A N (t)e j[ ωn t +φn (t )]
N n =0
This is of course a continuous signal. If we consider the waveforms of each component of
the signal over one symbol period, then the variables Ac (t) and φc (t) take on fixed
values, which depend on the frequency of that particular carrier, and so can be rewritten:
φn (t) = φn
A n (t) = A n
If the signal is sampled using a sampling frequency of 1/T(48kHz), then the resulting
signal is represented by:
ss (kT) =
1 N −1
A n e[ j( ω0 + nΔω)kT +φn ]
N n =0
At this point, we have restricted the time over which we analyze the signal to N(1024)
samples. It is convenient to sample over the period of one data symbol. Thus we have a
relationship: t=NT If we now simplify equation 3.3, without a loss of generality by letting
ω0=0, then the signal becomes:
1 N −1
s s (kT) = ∑ A n e jφn e j(nΔω)kT
N N =0
Now equation 3.4 can be compared with the general form of the inverse Fourier
g (kT ) =
In Equation 3.4 the function
A n e jφ n
n =0
N −1
is no more than a definition of the signal in the
sampled frequency domain, and s (kT) is the time domain representation. Eqns.4 and 5
are equivalent if:
This is the same condition that was required for orthogonality Thus, one consequence of
maintaining orthogonality is that the OFDM signal can be defined by using Fourier
transform procedures.
OFDM signals are typically generated digitally due to the difficulty in creating large
banks of phase locks oscillators and receivers in the analog domain. Fig 3.3 shows the
block diagram of a typical OFDM transceiver [15]. The transmitter section converts
digital data to be transmitted, into a mapping of subcarrier amplitude and phase. It then
transforms this spectral representation of the data into the time domain using an Inverse
Discrete Fourier Transform (IDFT). The Inverse Fast Fourier Transform (IFFT) performs
the same operations as an IDFT, except that it is much more computationally efficiency,
and so is used in all practical systems. In order to transmit the OFDM signal the
calculated time domain signal is then mixed up to the required frequency.
Fig 3.3 Block diagram of a basic OFDM transceiver.
The receiver performs the reverse operation of the transmitter, mixing the RF signal to
base band for processing, then using a Fast Fourier Transform (FFT) to analyze the signal
in the frequency domain [16]. The amplitude and phase of the sub carriers is then picked
out and converted back to digital data. The IFFT and the FFT are complementary
function and the most appropriate term depends on whether the signal is being received
or generated. In cases where the signal is independent of this distinction then the term
FFT and IFFT is used interchangeably.
3.6.1 Error Correction Codes:
When an OFDM transmission occurs in a multipath radio environment, frequency
selective fading can result in groups of sub carriers being heavily attenuated, which in
turn can result in bit errors. These nulls in the frequency response of the channel can
cause the information sent in neighbouring carriers to be destroyed, resulting in a
clustering of the bit errors in each symbol. Most Forward Error Correction (FEC)
schemes(convolution code(constraint length 7,rate=1/2) tend to work more effectively if
the errors are spread evenly, rather than in large clusters.
Figure 3.4. Convolutional encoder (K=7)
3.6.2 Data Interleaving
Interleaving aims to distribute transmitted bits in time or frequency or both to achieve
desirable bit error distribution after demodulation .All encoded data bits shall be
interleaved by a block interleaver with a block size corresponding to the number of bits in
a single OFDM symbol, NCBPS(288).The interleaver is defined by a two-step
permutation. The first permutation ensures that adjacent coded bits are mapped onto
nonadjacent sub carriers. The second ensures that adjacent coded bits are mapped
alternately onto less and more significant bits of the constellation and, thereby, long runs
of low reliability (LSB) bits are avoided. Deinterleaving is the opposite operation of
interleaving; i.e., the bits are put back into the original order.
3.6.3 Subcarrier modulation
One way to communicate a message signal whose frequency spectrum does not fall
within that fixed frequency range, or one that is otherwise unsuitable for the channel, is to
change a transmittable signal according to the information in the message signal. This
alteration is called modulation, and it is the modulated signal that is transmitted. The
receiver then recovers the original signal through a process called demodulation.
Modulation is a process by which a carrier signal is altered according to information in a
message signal. The carrier frequency, denoted Fc, is the frequency of the carrier signal.
The sampling rate, Fs, is the rate at which the message signal is sampled during the
simulation. The frequency of the carrier signal is usually much greater than the highest
frequency of the input message signal. The Nyquist sampling theorem requires that the
simulation sampling rate Fs be greater than two times the sum of the carrier frequency
and the highest frequency of the modulated signal, in order for the demodulator to
recover the message correctly.
Baseband versus Pass band Simulation
For a given modulation technique, two ways to simulate modulation techniques are called
baseband and pass band. Baseband simulation requires less computation. In this thesis,
baseband simulation will be used.
Digital Modulation Techniques
a) Amplitude Shift Key (ASK) Modulation
Fig 3.4 ASK modulation
In this method the amplitude of the carrier assumes one of the two amplitudes dependent
on the logic states of the input bit stream. A typical output waveform of an ASK
modulation is shown in Fig3.4.
b) Frequency Shift Key (FSK) Modulation
In this method the frequency of the carrier is changed to two different frequencies
depending on the logic state of the input bit stream. The typical output waveform of an
FSK is shown in Fig 3.5. Notice that logic high causes the centre frequency to increase to
a maximum and a logic low causes the centre frequency to decrease to a minimum.
Fig. 3.5 FSK Modulation
c) Phase Shift Key (PSK) Modulation
With this method the phase of the carrier changes between different phases determined
by the logic states of the input bit stream. There are several different types of Phase Shift
Key (PSK) modulators. These are:
1 Two-phase (2 PSK)
2 Four-phase (4 PSK)
3 Eight-phase (8 PSK)
4 Sixteen-phase (16 PSK) etc.
d) Quadrature Amplitude Modulation (QAM)
QAM is a method for sending two separate (and uniquely different) channels of
information. The carrier is shifted to create two carriers namely the sine and cosine
versions. The outputs of both modulators are algebraically summed and the result of
which is a single signal to be transmitted, containing the In-phase (I) and Quadrature (Q)
information. The set of possible combinations of amplitudes is a pattern of dots known as
a QAM constellation.
Once each subcarrier has been allocated bits for transmission, they are mapped using a
modulation scheme to a subcarrier amplitude and phase, which is represented by a
complex In-phase and Quadrature-phase (IQ) vector. Fig 3.6 shows an example of
subcarrier modulation mapping. This example shows 16-QAM, which maps 4 bits for
each symbol. Each combination of the 4 bits of data corresponds to a unique IQvector,
shown as a dot on the figure. A large number of modulation schemes are available
allowing the number of bits transmitted per carrier per symbol to be varied [17].
64qam constellation diagram
Fig 3.6 IQ modulation constellation, 64-QAM
Subcarrier modulation can be implemented using a lookup table, making it very efficient
to implement. In the receiver, mapping the received IQ vector back to the data word
performs sub carrier demodulation.
3.7 OFDM symbols
The serial signal is transformed to parallel alter the modulation using a reshape block.
3.7.1 OFDM Transmitter
OFDM (orthogonal frequency division multiplexing) transmission uses 1024 Sub
carriers, 256 pilots,56 null carriers, 1024-point FFTs, and a 128-sample cyclic prefix. The
figure illustrates the OFDM transmission.
The OFDM Transmitter subsystem performs the following task:
• Pilots and preamble insertion
• Cyclic prefix addition
3.7.2 Pilot insertion:
In each OFDM symbol, four of the sub carriers are dedicated to pilot signals in order to
make the coherent detection robust against frequency offsets and phase noise. These pilot
signals shall be placed equally in sub carriers ..
Figure.3.8. 1024 sub carriers, 256 pilots equally spaced
As explained, four pilots are inserted in each OFDM symbol. Pilots . Two fifty six pilots
are inserted in each OFDM symbol in the required subcarriers. The insertion is achieved
with a matrix concatenation block, pilots are inserted in its proper place in each symbol.
3.7.3 Preamble
The preamble is used to detect the start of the packet and to synchronize the receiver as
well. The OFDM symbols should be packed into frames before being sent. A preamble is
added at the beginning of each frame.[23] It helps the receiver to estimate phase and
amplitude errors, thereby allowing it to correct the received signal. In the simulation
exposed in this work a preamble consisting on two long training symbols, like the
following one, has been used:
L[-256,255}= {…..1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1,-1, 1, 1, -1,1, -1, 1, 1, 1, 1,
0, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1,1, -1, -1, 1,-1, 1, -1, 1, 1, 1, 1………}
A long OFDM training symbol consists of 512 sub carriers (including a zero value at
3.8 Frequency to time domain conversion
Fig 3.9. OFDM generation, IFFT(1024) stage
After the subcarrier modulation stage each of the data sub carriers is set to amplitude and
phase based on the data being sent and the modulation scheme. All unused sub carriers
are set to zero. This sets up the OFDM signal in the frequency domain. An IFFT is then
used to convert this signal to the time domain, allowing it to be transmitted. Fig 3.7
shows the IFFT section of the OFDM transmitter. In the frequency domain, before
applying the IFFT, each of the discrete samples of the IFFT corresponds to an individual
sub carrier. Most of the sub carriers are modulated with data. The outer sub carriers are
unmodulated and set to zero amplitude. These zero sub carriers provide a frequency
guard band before the nyquist frequency and effectively act as an interpolation of the
signal and allows for a realistic roll off in the analog anti-aliasing reconstruction filters.
3.9 RF modulation
The output of the OFDM modulator generates a base band signal, which must be mixed
up to the required transmission frequency. This can be implemented using analog
techniques as shown in Fig 3.8 or using a Digital up Converter as shown in Fig 3.9.
Fig 3.8 RF modulation of complex base band OFDM signal, using analog techniques
For a given system bandwidth the symbol rate for an OFDM signal is much lower than a
single carrier transmission scheme. For example for a single carrier BPSK modulation,
the symbol rate corresponds to the bit rate of the transmission. However for OFDM the
system bandwidth is broken up into NC sub carriers, resulting in a symbol rate that is NC
times lower than the single carrier transmission. This low symbol rate makes OFDM
naturally resistant to effects of Inter-Symbol Interference (ISI) caused by multipath
propagation. Multipath propagation is caused by the radio transmission signal reflecting
off objects in the propagation environment, such as walls, buildings, mountains, etc.
These multiple signals arrive at the receiver at different times due to the
transmission distances being different. This spreads the symbol boundaries causing
energy leakage between them. The effect of ISI on an OFDM signal can be further
improved by the addition of a guard period to the start of each symbol. This guard period
is a cyclic copy that extends the length of the symbol waveform. Each sub carrier, in the
data section of the symbol, (i.e. the OFDM symbol with no guard period added, which is
equal to the length of the IFFT size used to generate the signal) has an integer number of
cycles. Because of this, placing copies of the symbol end-to-end results in a continuous
signal, with no discontinuities at the joins. Thus by copying the end of a symbol and
appending this to the start results in a longer symbol time. Fig 3.10 shows the insertion of
a guard period.
Fig. 3.10 Addition of a guard period to an OFDM signal
The total length of the symbol is TS=TG + TFFT, where Ts is the total length of the symbol
in samples, TG is the length of the guard period in samples, and TFFT is the size of the
IFFT used to generate the OFDM signal. In addition to protecting the OFDM from ISI,
the guard period also provides protection against time-offset errors in the receiver. The
effects of multipath propagation and how cyclic prefix reduces the inter symbol
interference is discussed in detail in chapter4.
3.10.1 Protection against time offset
To decode the OFDM signal the receiver has to take the FFT of each received symbol, to
work out the phase and amplitude of the sub carriers. For an OFDM system that has the
same sample rate for both the transmitter and receiver, it must use The same FFT size at
both the receiver and transmitted signal in order to maintain sub carrier orthogonality.
Each received symbol has TG + TFFT samples due to the added guard period. The
receiver only needs TFFT samples of the received symbol to decode the signal [18]. The
remaining TG samples are redundant and are not needed. For an ideal channel with no
delay spread the receiver can pick any time offset, up to the length of the guard period,
and still get the correct number of samples, without crossing a symbol boundary. Because
of the cyclic nature of the guard period changing the time offset simply results in a phase
rotation of all the sub carriers in the signal. The amount of this phase rotation is
proportional to the sub carrier frequency, with a sub carrier at the nyquist frequency
changing by 180° for each sample time offset. Provided the time offset is held constant
from symbol to symbol, the phase rotation due to a time offset can be removed out as part
of the channel equalization [19]. In multipath environments ISI reduces the effective
length of the guard period leading to a corresponding reduction in the allowable time
offset error. The addition of guard period removes most of the effects of ISI. However in
practice, multipath components tend to decay slowly with time, resulting in some ISI
even when a relatively long guard period is used.
3.10.2 Guard period overhead and sub carrier spacing
Adding a guard period lowers the symbol rate, however it does not affect the sub carrier
spacing seen by the receiver. The sub carrier spacing is determined by the sample rate
and the FFT size used to analyze the received signal.
Δf =
In Equation (3.6), Δf is the sub carrier spacing in Hz, Fs is the sample rate in Hz, and
NFFT is the size of the FFT. The guard period adds time overhead, decreasing the overall
spectral efficiency of the system.
3.10.3 Intersymbol interference
Assume that the time span of the channel is Lc samples long. Instead of a single carrier
with a data rate of R symbols/ second, an OFDM system has N subcarriers, each with a
data rate of R/N symbols/second. Because the data rate is reduced by a factor of N, the
OFDM symbol period is increased by a factor of N. By choosing an
Fig 3.11 Example of intersymbol interference. The green symbol was transmitted first,
followed by the blue symbol.
Appropriate value for N, the length of the OFDM symbol becomes longer than the time
span of the channel. Because of this configuration, the effect of intersymbol interference
is the distortion of the first Lc samples of the received OFDM symbol. An example of this
effect is shown in Fig 3.11. By noting that only the first few samples of the symbol are
distorted, one can consider the use of a guard interval to remove the effect of intersymbol
interference. The guard interval could be a section of all zero samples transmitted in front
of each OFDM symbol [20]. Since it does not contain any useful information, the guard
interval would be discarded at the receiver. If the length of the guard interval is properly
chosen such that it is longer than the time span of the channel, the OFDM symbol itself
will not be distorted. Thus, by discarding the guard interval, the effects of intersymbol
interference are thrown away as well.
3.10.4 Intrasymbol interference
The guard interval is not used in practical systems because it does not prevent an OFDM
symbol from interfering with itself. This type of interference is called intrasymbol
interference [21]. The solution to the problem of intrasymbol interference involves a
discrete-time property. Recall that in continuous-time, a convolution in time is equivalent
to a multiplication in the frequency-domain. This property is true in discrete-time only if
the signals are of infinite length or if at least one of the signals is periodic over the range
of the convolution. It is not practical to have an infinite-length OFDM symbol, however,
it is possible to make the OFDM symbol appear periodic.
This periodic form is achieved by replacing the guard interval with something
known as a cyclic prefix of length Lp samples. The cyclic prefix is a replica of the last Lp
samples of the OFDM symbol where Lp > Lc. Since it contains redundant information,
the cyclic prefix is discarded at the receiver. Like the case of the guard interval, this step
removes the effects of intersymbol interference. Because of the way in which the cyclic
prefix was formed, the cyclically-extended OFDM symbol now appears periodic when
convolved with the channel. An important result is that the effect of the channel becomes
In a digital communications system, the symbols that arrive at the receiver have
been convolved with the time domain channel impulse response of Length Lc samples.
Thus, the effect of the channel is convolution. In order to undo the effects of the channel,
another convolution must be performed at the receiver using a time domain filter known
as an equalizer. The length of the equalizer needs to be on the order of the time span of
the channel. The equalizer processes symbols in order to adapt its response in an attempt
to remove the effects of the channel. Such an equalizer can be expensive to implement in
hardware and often requires a large number of symbols in order to adapt its response to a
good setting. In OFDM, the time-domain signal is still convolved with the channel
response [22]. However, the data will ultimately be transformed back into the frequencydomain by the FFT in the receiver. Because of the periodic nature of the cyclicallyextended OFDM symbol, this time-domain convolution will result in the multiplication of
the spectrum of the OFDM signal (i.e., the frequency- domain constellation points) with
the frequency response of the channel.
The result is that each sub carrier’s symbol will be multiplied by a complex
number equal to the channel’s frequency response at that sub carrier’s frequency. Each
received sub carrier experiences a complex gain (amplitude and phase distortion) due to
the channel. In order to undo these effects, a frequency- domain equalizer is employed.
Such an equalizer is much simpler than a time-domain equalizer. The frequency domain
equalizer consists of a single complex multiplication for each sub carrier. For the simple
case of no noise, the ideal value of the equalizer’s response is the inverse of the channel’s
frequency response [24].
3.11 Advantages and Disadvantages of OFDM as Compared to Single Carrier
3.11.1 Advantages
1 Makes efficient use of the spectrum by allowing overlap.
2 By dividing the channel into narrowband flat fading sub channels, OFDM is more
resistant to frequency selective fading than single carrier systems.
3 Eliminates ISI and IFI through use of a cyclic prefix.
4 Using adequate channel coding and interleaving one can recover symbols lost due to
the frequency selectivity of the channel.
5 Channel equalization becomes simpler than by using adaptive equalization techniques
with single carrier systems.
6 It is possible to use maximum likelihood decoding with reasonable complexity.
7 OFDM is computationally efficient by using FFT techniques to implement the
modulation and demodulation functions.
8 Is less sensitive to sample timing offsets than single carrier systems are.
9 Provides good protection against co-channel interference and impulsive parasitic
3.11.2 Disadvantages
1 The OFDM signal has a noise like amplitude with a very large dynamic range,
therefore it requires RF power amplifiers with a high peak to average power ratio.
2 It is more sensitive to carrier frequency offset and drift than single carrier systems are
due to leakage of the DFT.
Digital Signal processing (DSP) is one of the fastest growing fields of technology and
computer science in the world. In today's world almost everyone uses DSPs in their
everyday life but, unlike PC users, almost no one knows that he/she is using DSPs.
Digital Signal Processors are special purpose microprocessors used in all kind of
electronic products, from mobile phones, modems and CD players to the automotive
industry; medical imaging systems to the electronic battlefield and from dishwashers to
satellites.[17] DSP is all about analysing and processing real-world or analogue signals,
i.e. the kind of signals that humans interact with, for example speech. These signals are
converted to a format that computers can understand (digital) and, once this has
happened, process. The following diagram shows the typical component parts of a DSP
Figure.4.1. Typical components of a DSP system
In order to process analog signals with digital computers they must first be converted to
digital signals using analog to digital converters. Similarly, the digital signals must be
converted back to analog ones for them to be used outside the computer.
There are many reasons why we process these analog signals in the digital world.
Traditional signal processing was achieved by using analogue components such as
resistors, capacitors and inductors. However, the inherent tolerance associated with this
components, temperature and voltage changes and mechanical vibrations can
dramatically affect the effectiveness of analogue circuitry. On the other hand, DSP is
inherently stable, reliable and repeatable.With DSP it is easy to chance, correct or update
applications. Additionally, DSP reduces noise susceptibility, chip count, development
time, cost and power consumption.
DSP has many unique properties. It is a Super Mathematician thanks to its arithmetic
logic units and its optimized multipliers. DSPs do really well in application where the
data to be processed is arriving in a continuous flow, often referred to as a stream. It uses
almost no power compared to a PC microprocessor. Next, some features that make DSP
different from other microprocessors are going to be described:
1. High
multiplications together. DSP proccesors usually have hardware adders and
multipliers which can be used in parallel within a single instruction, so both, an
addition and a multiplication, can be executed in a single cycle. Thus, DSP
processors arithmetic speed is very high compared with microprocessors.
2. Data transfer to and from real world. In a typical DSP application the processor
will have to deal with multiple sources of data from the real world. In each case,
the processor may have to be able to receive and transmit data in real time,
without interrupting its internal mathematical operations. These multiple
communications routes mark the most important distinctions between DSP
processors and general purpose processors.
3. Multiple access memory architectures: Typical DSP operations require many
simple additions and multiplications. To fetch the two operations in a single
instruction cycle the two memory accesses should be able to operate
simultaneously. For this reason DSP processors usually support multiple memory
accesses in the same instruction cycle.
4. Digital Signal Processors also have the advantage of consuming less power and
being relatively cheap.
The DSP architecture is a well defined but quite complex hardware structure that needs
much time to be explained in detail. An overview of this architecture is going to be
exposed here in order to make it as much understandable as possible.
The TMS320C6000 family of processors from the company Texas Instruments is
designed to meet the real-time requirements of high performance digital signal
processing. With a performance of up to 2000 million instructions per second (MIPS) at
250 MHz and a complete set of development tools, the TMS320C6000 DSPs offer cost
effective solutions to higher-performance DSP programming challenges.
The TMS320C6000 DSPs give the system architects unlimited possibilities to
differentiate their products. High performance, easy use, and affordable pricing make the
TMS320c6000 platform the ideal solution for a large number of applications
(multichannel multifunction applications such as: pooled modems, wireless local loop
base stations, multichannel telephony systems, etc). First of all, a DSP device must be
considered as a specific microprocessor whose components have been linked in a clever
way to process faster.
The TMS320C6xxx family are processors currently running at a clock speed of up to
300MHz (225MHz in the TMS320C6713 case). The C62xx processors are fixed-point
processors whereas the C67xx are floating-point processors. These refer to the format
used to store and manipulate numbers withing the devices. Figure 24 shows the main
components of the TMS320C6000 DSP under a block diagram form.
It is composed of:
1. External Memory Interface (EMIF) to access external data at the specified
2. Memory, which is the internal memory where a set of instructions and data
values can be stored (FFT algorithm for example).
3. Peripherals are the possible connectable devices that can be associated with the
DSP (DMA/EDMA, Serial port, Timer/Counter…).
4. Internal buses; they allow the components to quickly communicate together
differentiating addresses and data.
5. CPU, which is the most important component since it performs all the operation.
Figure .4.2.TMS320C6000 block diagram
4.3.1 TMS320C6713 DSP Description
The TMS320C67 DSPs (including the TMS320C6713 device) compose the floating–
point DSP generation in the TMS320C6000 DSP platform. The TMS320C6713 (C6713)
device is based on the high-performance, advanced VelociTI very-long-instruction-word
(VLIW) architecture developed by Texas Instruments (TI), making this DSP an excellent
choice for multichannel and multifunction applications.
The DSK features the TMS320C6713 DSP, a 225 MHz device delivering up to 1800
million instructions per second (MIPs) and 1350 MFLOPS. This DSP generation is
designed for applications that require high precision accuracy. The C6713 is based on the
TMS320C6000 DSP platform designed to fit the needs of high-performing high-precision
applications such as pro-audio, medical and diagnostic. Other hardware features of the
TMS320C6713 DSK board include:
• Embedded JTAG support via USB
• High-quality 24-bit stereo codec
• Four 3.5mm audio jacks for microphone, line in, speaker and line out
• 512K words of Flash and 8 MB SDRAM
• Expansion port connector for plug-in modules
• On-board standard IEEE JTAG interface
• +5V universal power supply
The DSP environment used in this project is the TMS320C6713 DSK. Figure
25 shows the architecture of the TMS320C6713 DSK. Key features include:
Figure .4.3. Architecture of the TMS320C6713 DSK
Fig.4.4.Implemented on TMS320c6713DSP using TMDSDSK6713 Evaluation
In this work we used two 6713DSK’s for testing. One is used as Transmitter and other as
Receiver. The boards connected by stereo cables for data transmission.
CCS3.1v is used for code generation
Normally Design Parameters for Designing a Wireless Modem are
2.Multipath Delay Spread
3.Data rate
Based on above parameters, The following specifications we considered for the current
design of Acoustic Modem.
1. Carrier frequency 12.5kHz
2. Sampling rate 48kHz
3. 1024 sub carriers,256 pilot carriers,56 null carriers
4. Band width 5kHz
5. Guard Interval Tg=25 msec.
6. OFDM Symbol Duration T= 0.2298(0.2048+0.025) sec.
7. Preamable duration 0.266sec
8. Sub carrier frequency spacing is 5Hz(1/(T-Tg))
9. Data rate 3.1kbps
10. QPSK sub carrier Modulation
11. FEC code (convoutional)
We taken audio signal as input to the system,To sample audio signals, one of the
multichannel buffered serial ports (McBSPs) is configured to connect the AIC23 codec.
The audio data is transferred between the codec and the internal L2 memory through the
enhanced direct memory access (EDMA) channel. To save the raw audio data from the
AIC23 codec continuously, the commonly-known double-buffering method is used.
When one of the two buffers is filled, a DMA interrupt is initiated and the data is passed
to the interrupt service routine (ISR) and then is processed. At the same time, the codec
keeps sampling and saves data into the other buffer. So data sampling and processing can
be done simultaneously and no incoming signals are missed even if the DSP is processing
previously received data.
Processing steps:
1. The stored data to be converted into binary stream for transmission.
2. Forward error correction convolution code is considered with constraint
length7,rate=1/2. decreased the data rate from 6.2 kbps to 3.1 kbps.
3. Block interleaving is taken place to avoid burst of errors .where adjacent bits
mapped onto non adjacent sub carriers.
4. K/4(256) equally spacing pilots inserted with unit amplitude and zero phase for
channel estimation. here search is one dimensional.
5. Kn(56)null sub carriers inserted in the middle portion for the over sampling and
also to mitigate the effect of ICI and ISI..
6. 1024 ifft provides tme domain complex output with real and imaginary values are
cyclically extended by K/8 samples to avoid the mutipah effects.
7. To reduce the effct of ICI/ISI we do apply RRC windowing to provide constant
envelope for the particular ofdm symbol duration .
8. At last that OFDM symbols get modulated by qpsk carrier(12.5kHz) to tranimit
through the cable.
9. preamble appended to the OFDM symbol at the beginning for timing and
frequency synchronization (to know ofdm frame starting).
10. all the above specifications adopted from IEEE802.11a Standard
Figure 4.5. Stored speech signal in Buffer
1. AWGN (Additive White Gaussian Noise)
2. Multipath channel noise(real world exponential channel with delay spread
50msec,max excess delay 276.3 msec,7tap filter)
3. Carrier frequency offset(0.2Hz,40%ppm of operating frequency(12.5khz)
1. Qpsk Demodultion takes place to extract the binay symols from carrier.(techinge
applied PLL.
2. Autocorrelation and cross correlation peaks of long preamble of two period sequences
with 60% cyclic prefix decide the starting symbol of OFDM frame.This technique
provides us coarse frequency tuning for 50% overlap of sub carries.
3. FFT will be calculated to covert time domain symbols into frequency domain where
already effected by Phase Noise and frequency noise
4. CFO estimation done by taking FFT of Kn sub carriers where minargJ(n) minimum
then ICI would be greatly reduced.
5. One dimensional channel estimation done by calculating the FFT of K/4 Pilot sub
6. Corrupted qpsk symbols extracted by quantization technique.
7. Demapping (mapped symbols converted back into bits).
8. Vertebra decoder(hard decision algorithm) decodes the corrupted data and provides the
output to the speaker.
Figure 4.6 Recovered speech signal
An OFDM system was modeled using Matlab to allow various parameters of the system
to be varied and tested. The aim of doing the simulations was to measure the performance
of OFDM under AWGN, Multipath(real world exponential channel considered) channels,
Carrier frequency offset conditions, for different modulation schemes like BPSK, QPSK,
16-QAM, 64-QAM used in IEEE 802.11a wireless LAN standard. and in CCS for
Modem design we considered only QPSK modulation in wired environment.
Following this introduction, section 5.2 discusses model used in simulation, steps
in OFDM simulation and parameters used for the Modem design. Section 5.3 presents
one important block in receiver Frame Synchronization in detail. Section 5.4 provides the
simulation results of OFDM system for different channel schemes.
The OFDM system that was simulated using matlab for the model shown in Fig 5.1.
Fig 5.1 simulation model of OFDM
Following are the parameters used in simulation of OFDM system.
modulations used
FFT size
Number of carriers used 1024
Guard time
128 samples
Guard period type
Cyclic extension of the symbol
Table 5.1 OFDM simulation parameters
5.3 OFDM Frame Synchronization
In order to properly demodulate the transmitted data, the start of each OFDM frame
needs to be found with reasonably accuracy. This is the task of the OFDM frame
synchronization subsystem. The OFDM frame synchronization subsystem ignores input
before the preamble comes in and then aligns the input directly after the preamble on
frame boundaries..
Figure 5.2 shows the need for frame synchronization
Each OFDM symbol consists of a window interval, WI, a guard interval, the OFDM
data,and then another WI. In order to properly demodulate the OFDM symbol, the FFT in
the receiver needs to be filled with data from only one OFDM symbol. If it gets
information from overlapping symbols, as shown in Figure 24, the data will be corrupt.
Since every symbol is the same length, if the start of the OFDM symbols can be found,
then the decoding of the symbols can be properly performed.
A pseudo-random sequence has a unique property in that its autocorrelation is very
peaked. This is very useful in determining the location of a sequence in s signal and can
be used to implement frame synchronization in communication systems. The 802.16a
specification defines an initial pseudo-random preamble sequence that is used for frame
synchronization. What is given is 512 QPSK symbol pseudorandom in a sequence show
in Equation 7 [2].
S–228, 227 = √(13/6) × {………..0, 0, 1+j, 0, 0, 0, –1–j, 0, 0, 0, 1+j, 0, 0, 0, –1–j, 0, 0, 0,
–1–j, 0, 0, 0, 1+j, 0, 0, 0, 0, 0, 0, 0, –1–j, 0, 0, 0, –1–j, 0, 0, 0, 1+j, 0, 0, 0, 1+j, 0, 0, 0, 1+j,
0, 0, 0, 1+j, 0,0………}
This long sequence, S, is then padded with zeros on either side to bring it to S-256,255, a
512 length vector. This padded vector is then fed into a 512point IFFT to bring it to the
time domain and then cyclically extended to 1332(0.6*S+S+S) samples, according to
specification. This set of 1332samples in the time domain, is then windowed to reduce
inter symbol interference to create one longt symbol. This short symbol is then repeated
10 times to create the whole short preamble, r, that will be used to find the start of the
OFDM frame. Sridhar Nandula and K Giridhar [26] detail a technique that can be used to
synchronize with this preamble. Figure 25 from Nandula and Giridhar’s article shows this
timing synchronization under no noise
cross correlation
auto correlation
received samples
Figure 5.3 - Detail of frame synchronization technique
The “dome” in Figure 5.3 shows the results of the signal being auto correlated with itself
delayed by one symbol length (Equation 8).
Equation 5.1 - Autocorrelation Equation
In above Equation, L is the length of one short symbol, r is the whole transmitted
preamble, and N is the length of the OFDM data (512 samples). The result of Equation 8
is rather noisy. It is then smoothed with a moving average filter as shown in below
Equation 5.2 - Moving Average Equation
In Equation The spikes in Figure 5.3 are generated by Equation 5.4. In this equation, the
whole preamble, r, is cross correlated with one short symbol, s. S is the number of FFT
samples, as in Equation 5.2 and M is the number of short symbols that the cross
correlation is averaged over.
Equation 5.4 - Equation for Cross Correlation with the Preamble
The start of the OFDM symbol can then be found by timing off of the last large spike
inside the flat part of the dome.
In this way for frame synchronization we perform several tests under different channels
and for different modulations, ploted the BER Vs SNR and MSE Vs SNR graphs.
Timing synchronization under SNR-10,delay spread=50ms
Cross corr
Auto corr
received short preamble
Figure 5.4. Detail of frame synchronization technique under SNR=10,delay spread=50ms
Probability of synchronization failure curves
AWGN channel
Multipath DS 50ms
Multipath DS 50ms&Freq.offset 0.2Hz
Probability of synchronization failure
Fig.5.5 Probability of synchronization failure curves under different channels
AWGN channel freq.offset 0.2Hz
multipath channel DS 50ms &freq. offset 0.2Hz
Fig.5.6 Variation of MSE Vs SNR under different channels
Fig.5.7 BER vs. SNR plot for OFDM using BPSK, QPSK, 16-QAM, 64-QAM
Table 5.2 Execution Time(in sec) Measured in CCS profiler
The above simulation results exposed out the probability of occurring of Frame
Synchronization errors under different noisy channels including (AWGN,Multi path,
Carrier frequency offset) .
On the DSK board corresponding MatLab programs converted into “c” code and loaded
onto two boards and perform the following tests
5.4 Performance tests
Base Band Modulation techniques:
First Test:
Speech input 16 bit converted into binary and transmitted though cable successfully
received with out noise
Maximum input frequency=1 kHz
Sampling frequency=48 kHz
Second Test
Applied mu law for bit compression and used LPC coder to provide prediction error
minimum to get less coded bits (reduced from 16 to 8) observed signal clearly .
Maximum input frequency=1 kHz
Sampling frequency =16 kHz
Single carrier Modulation:
Successfully tested, components band pass filter (16 kHz), multiplier,lowpass
filter(4kHz)(with 30th order and FIR filter with Kaiser window),decimator
Maximum input frequency=1 kHz
Sampling frequency =8 kHz
Filtering operations done by overlap add FFT method to improving the speed factor than
convolution method
Multi carrier Modulation:
Tested Successfully in SIMULINK and that model Embedded into DSP target for
execution. Frequencies separated by band pas filters (IIR design with 30 order)
Input frequencies=1 kHz, 2 kHz, 3 kHz
Sampling frequency =8 kHz
The code we generated Loaded onto the 2 DSKS (Transmitter, Receiver) And When
tested we were hearing more noise than signal. We tried with all possible conditions but
could not improve the system performance. and at last we measured all the subroutines
code length and their execution time by using CCS profiler And we found that for one
block OFDM our processor taking approxly 2.7235 sec Actually which could finish in
OFDM symbol duration which is(0.2298sec) We tried our best at optimizing the code and
utilize the resources to the maximum extent.
We implemented the coherent OFDM algorithm on a TMS320C6713 DSP board for
acoustic communications. Based on the program profiling, we identified the timeconsuming operations of the OFDM algorithms. We optimized the code and achieved
significant speedups. We reduced the processing time per OFDM block to about 2.7235
seconds. Since the duration of an OFDM block is 0.23 seconds, the current
implementation does not meet the real-time operation requirements yet. In the future,
anyone can motivated to pursue a hybrid DSP/FPGA-based solution to construct a realtime OFDM modem.
1.We could not test the system in real time in air to air and in under water communication
2.we did not consider Doppler spread in simulation.
1.the current implementation does not meet the real-time operation requirements yet.
2.In the future, we can motivated to pursue a hybrid DSP/FPGA-based solution to
construct a real-time OFDM modem.
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