ANALYSIS OF THE IEEE 802.15.4A ULTRA WIDEBAND PHYSICAL LAYER THROUGH

ANALYSIS OF THE IEEE 802.15.4A ULTRA WIDEBAND PHYSICAL LAYER THROUGH
ANALYSIS OF THE IEEE 802.15.4A ULTRA
WIDEBAND PHYSICAL LAYER THROUGH
WIRELESS SENSOR NETWORK SIMULATIONS IN
OMNET++
by
Marthinus Alberts
Submitted in partial fulfillment of the requirements for the degree
Master of Engineering (Computer Engineering)
in the
Faculty of Engineering, the Built Environment and Information Technology
UNIVERSITY OF PRETORIA
February 2011
Document ver. 2.0
© University of Pretoria
ACKNOWLEDGEMENTS
Throughout my life all personal accomplishments, even those meager in the eyes of the
world, are attributed to two constants ... God and my family.
First of all it delights me to be able to thank my creator, Jesus Christ. Without God's ever
abundant grace I would not have been able to achieve anything. To the Master of the
universe be all glory, power and praise. May His Word and His Will find perfection in my
life.
"Be strong and of a good courage; be not afraid, neither be thou dismayed:
for the LORD thy God is with thee whithersoever thou goest." - Jos. 1:9
Secondly God blessed me with a loving, caring family. To my beautiful wife, Simone,
thank you for sacrificing so much time and being devoted to me through the fast falling
troughs and slow rising crests that every voyage has to weather. To the rest of my family,
close by and far, I am indebted to each of you for all the prayers, true words of motivation
and ongoing support. I am a lucky man indeed.
A special word of thanks to my supervisor, Prof. Gerhard Hancke of the Department of
Electrical, Electronic and Computer Engineering at the University of Pretoria for his
guidance and utmost patience with me throughout this research project.
Finally my gratitude also goes out to Jérôme Rousselot, Ph.D. student at the Ecole
Polytechnique Federale de Lausanne, for the Mixim-UWB framework and for granting me
access to his research.
Analysis of the IEEE 802.15.4a ultra wideband physical layer through
wireless sensor network simulations in OMNET++
by
Marthinus Alberts
Supervisor:
Prof. G.P. Hancke
Department:
Electrical, Electronic and Computer Engineering
Degree:
Master of Engineering (Computer Engineering)
KEY TERMS
Ultra Wideband; Wireless Sensor Networks; IEEE 802.15.4; IEEE 802.15.4a; OMNET++;
Zigbee; Wireless Personal Area Networks; WPAN; LR-WPAN; Network Simulator; PAN;
Broadband; Sensor Mobility;
ABSTRACT
Wireless Sensor Networks are the main representative of pervasive computing in largescale physical environments. These networks consist of a large number of small, wireless
devices embedded in the physical world to be used for surveillance, environmental
monitoring or other data capture, processing and transfer applications.
Ultra wideband has emerged as one of the newest and most promising concepts for
wireless technology. Considering all its advantages it seems a likely communication
technology candidate for future wireless sensor networks.
This paper considers the viability of ultra wideband technology in wireless sensor networks
by employing an IEEE 802.15.4a low-rate ultra wideband physical layer model in the
OMNET++ simulation environment.
An elaborate investigation into the inner workings of the IEEE 802.15.4a UWB physical
layer is performed. Simulation experiments are used to provide a detailed analysis of the
performance of the IEEE 802.15.4a UWB physical layer over several communication
distances. A proposal for a cognitive, adaptive communication approach to optimize for
speed and distance is also presented.
Analise van die IEEE 802.15.4a ultra wyeband fisiese laag deur middel
van draadlose sensor netwerk simulasies in OMNET++
deur
Marthinus Alberts
Studieleier:
Prof. G.P. Hancke
Department:
Elektriese, Elektroniese en Rekenaar-Ingenieurswese
Graad:
Meester in Ingenieurswese (Rekenaar Ingenieurswese)
SLEUTELTERME
Ultra wyeband; Draadlose Sensor Netwerke; IEEE 802.15.4; IEEE 802.15.4a; OMNET++;
Zigbee; Draadlose Persoonlike Area Netwerke; Netwerk Simulator; Breëband; Sensor
Mobiliteit;
OPSOMMING
Draadlose Sensor Netwerke is die hoof verteenwoordiger vir deurdringende rekenarisering
in groot skaal fisiese omgewings. Hierdie tipe netwerke bestaan uit ’n groot aantal klein,
draadlose apparate wat in die fisiese wêreld ingesluit word vir die doel van bewaking,
omgewings monitering en vele ander data opvang, verwerk en oordrag applikasies.
Ultra wyeband het opgestaan as een van die nuutste en mees belowend konsepte vir
draadlose kommunikasie tegnologie. As al die voordele van dié kommunikasie tegnologie
in ag geneem word, blyk dit om ’n baie goeie kandidaat te wees vir gebruik in toekomstige
draadlose sensor netwerke.
Hierdie verhandeling oorweeg die vatbaarheid van die gebruik van die ultra wyeband
tegnologie in draadlose sensor netwerke deur ’n IEEE 802.15.4a lae-tempo ultra wyeband
fisiese laag model in die OMNET++ simulasie omgewing toe te pas.
’n Breedvoerige ondersoek word geloots om die fyn binneste werking van die IEEE
802.15.4a UWB fisiese laag te verstaan. Simulasie eksperimente word gebruik om ’n meer
gedetaileerde analiese omtrent die werkverrigting van die IEEE 802.15.4a UWB fisiese
laag te verkry oor verskillende kommunikasie afstande. ’n Voorstel vir ’n omgewings
bewuste, aanpasbare kommunikasie tegniek word bespreek met die doel om die spoed en
afstand van kommunikasie te optimiseer.
LIST OF ABBREVIATIONS
AES
Advanced Encryption Standard
AOA
Angle Of Arrival
BAN
Body Area Network
BER
Bit Error Rate
BI
Beacon Interval
BO
Beacon Order
bps
bits per second
BPSK
Binary Phase Shift Keying
CAP
Contention Access Period
CSMA-CA
Carrier Sense Multiple Access with Collision Avoidance
CFP
Contention-Free Period
CFR
Code of Federal Regulations
CW
Continuous Wave
DCC
Dynamic Channel Coding
DoD
Department of Defense
DS
Direct Sequence
DS-UWB
Direct Sequence Ultra Wideband
ECMA
European Computer Manufacturers Association
FCC
Federal Communications Commission
FCS
Frame Check Sequence
FEC
Forward Error Correction
FEL
Future Event List
FES
Future Event Set
FFD
Full Function Device
FLL
Frequency Locked Loop
Gbps
Giga bits per second
GHz
Giga Hertz
GPL
General Public License
GUI
Graphic User Interface
Hz
Hertz
IC
Integrated Circuit
IDE
Integrated Development Environment
IEEE
Institute of Electrical and Electronic Engineers
IR
Impulse Radio
IRA
Impulse Radiating Antenna
ISI
Information Sciences Institute
Kbps
Kilo bits per second
kHz
Kilo Hertz
LAN
Local Area Network
LLC
Logical Link Control
LOS
Line-Of-Sight
LR-WPAN
Low-rate Wireless Personal Area Network
MAC
Medium Access Control
MB-OFDM
Multi-Band Orthogonal Frequency Division Multiplexing
Mbps
Mega bits per second
MCPS
Medium Access Control Common Part Sublayer
MCPS-SAP
Medium Access Control Common Part Sublayer (MCPS) Data Service
MFR
Medium Access Control Footer
MHR
Medium Access Control Header
MHz
Mega Hertz
MLME
Medium Access Control Sublayer Management Entity
MLME-SAP
Medium Access Control Sublayer Management Entity Service Access
MPDU
Medium Access Control Protocol Data Unit
MSDU
Medium Access Control Service Data Unit
NED
Network Description
NLOS
Non-Line-Of-Sight
OFDMA
Orthogonal Frequency Division Multiple Access
OSI
Open Systems Interconnection
PAN
Personal Area Network
PAR
Project Authorization Request
PD
Physical Layer Data
PD-SAP
Physical Layer Data Service Access Point
PDU
Protocol Data Unit
PHR
Physical Layer Header
PHY
Physical Layer
PLL
Phase Locked Loop
PLME
Physical Layer Management Entity
PLME-SAP
Physical Layer Management Entity Service Access Point
PPDU
Physical Protocol Data Unit
PR
Pseudo Random
PSD
Power Spectral Density
PSDU
Physical Service Data Unit
R&D
Research and Development
RAM
Random Access Memory
RFD
Reduced Function Device
RFID
Radio Frequency Identifier
RSSI
Received Signal Strength Indicator
SAP
Service Access Point
SDU
Service Data Unit
SFD
Start-of-Frame Delimiter
SHR
Synchronization Header
SNR
Signal-to-Noise Ratio
SO
Superframe Order
SSCS
Service-Specific Convergence Sublayer
TOA
Time Of Arrival
TDOA
Time Difference Of Arrival
TG
Task Group
TH
Time Hopping
U.S.
United States
USB
Universal Serial Bus
USB-IF
Universal Serial Bus – Implementers Forum
USC
University of Southern California
UWB
Ultra Wideband
WLAN
Wireless Local Area Network
WPAN
Wireless Personal Area Network
WSN
Wireless Sensor Network
TABLE OF CONTENTS
1. INTRODUCTION ........................................................................................................... 1
1.1
SCOPE ................................................................................................................... 1
1.2
PROBLEM STATEMENT AND MOTIVATION ............................................... 2
1.3
OBJECTIVES ........................................................................................................ 3
1.4
RESEARCH METHODOLOGY .......................................................................... 3
1.5
DOCUMENT OUTLINE ...................................................................................... 4
2. BACKGROUND .............................................................................................................. 5
2.1
WIRELESS DATA COMMUNICATION............................................................ 5
2.1.1
2.1.2
2.1.3
2.1.4
2.2
STANDARDS ACTIVITY OF WPANS .............................................................. 9
2.2.1
2.2.2
2.2.3
2.2.4
2.2.5
2.2.6
2.3
History of wireless ............................................................................................. 5
Narrowband transmission .................................................................................. 5
Shannon’s information capacity theorem .......................................................... 8
History of Ultra Wideband ................................................................................ 9
Task group 1 (Bluetooth)................................................................................. 10
Task group 2 (Coexistence) ............................................................................. 10
Task group 3 (High Rate WPAN) ................................................................... 10
Task group 4 (Low Rate WPAN) .................................................................... 11
Task group 5 (Mesh networking) .................................................................... 13
Task group 6 (BANs) ...................................................................................... 13
WIRELESS SENSOR NETWORKS .................................................................. 13
2.3.1
2.3.2
2.3.3
2.3.4
Applications ..................................................................................................... 14
Sensor nodes .................................................................................................... 15
Power sources .................................................................................................. 15
Challenges ....................................................................................................... 17
3. OVERVIEW OF ULTRA WIDEBAND ..................................................................... 19
3.1
DEFINING ULTRA WIDEBAND ..................................................................... 19
3.1.1 UWB Power Spectral Density ......................................................................... 20
3.1.2 UWB Regulations ............................................................................................ 20
3.2
ADVANTAGES OF UWB ................................................................................. 24
3.2.1
3.2.2
3.2.3
3.2.4
3.2.5
3.2.6
Improved channel capacity .............................................................................. 24
Inherent robustness to multipath fading .......................................................... 24
Noise-like signal .............................................................................................. 24
Low complexity, low equipment cost and small form factor .......................... 25
Low power consumption ................................................................................. 25
Penetration ability ............................................................................................ 25
3.2.7 Accurate ranging and location detection ......................................................... 25
3.3
UWB WAVEFORM GENERATION ................................................................. 26
3.3.1 Gaussian waveforms ........................................................................................ 26
3.3.2 Choice of waveform ........................................................................................ 29
3.4
UWB IMPULSE RADIO .................................................................................... 31
3.4.1 Pulse trains....................................................................................................... 31
3.5
MULTIBAND UWB ........................................................................................... 35
3.6
UWB MODULATION TECHNIQUES .............................................................. 36
3.6.1
3.6.2
3.6.3
3.6.4
3.6.5
3.7
Pulse Position Modulation ............................................................................... 36
Pulse Amplitude Modulation ........................................................................... 37
On-Off Keying................................................................................................. 38
Binary Phase Shift Keying .............................................................................. 38
Pulse Shape Modulation .................................................................................. 39
MULTIPLE ACCESS STRATEGIES ................................................................ 40
3.7.1 Direct Sequence ............................................................................................... 41
3.7.2 Time Hopping .................................................................................................. 41
3.7.3 Orthogonal Frequency Division Multiple Access ........................................... 42
3.8
UWB CHANNEL MODELS .............................................................................. 42
3.8.1
3.8.2
3.8.3
3.8.4
3.9
Free space propagation model ......................................................................... 42
Saleh-Valenzuela path loss model ................................................................... 43
Ghassemzadeh path loss model ....................................................................... 43
IEEE 802.15.4a path loss models .................................................................... 43
UWB TRANSCEIVER ....................................................................................... 43
3.9.1 UWB Transmitter ............................................................................................ 43
3.9.2 UWB Receiver................................................................................................. 44
3.10
UWB ANTENNAS ............................................................................................. 45
3.11
POSITIONING AND RANGING ....................................................................... 46
3.12
TYPICAL UWB APPLICATION AREAS ......................................................... 46
3.13
UWB DISADVANTAGES ................................................................................. 46
4. OVERVIEW OF IEEE 802.15.4A ............................................................................... 48
4.1
INTRODUCING THE PARTS ........................................................................... 48
4.1.1 IEEE 802.15.4 ................................................................................................. 48
4.1.2 Zigbee .............................................................................................................. 48
4.1.3 IEEE 802.15.4a ................................................................................................ 48
4.2
GENERAL WPAN DESCRIPTION ................................................................... 49
4.2.1 Node types ....................................................................................................... 49
4.2.2 Topology.......................................................................................................... 49
4.2.3 Architecture ..................................................................................................... 52
4.3
FUNCTIONAL OVERVIEW ............................................................................. 53
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
4.3.6
4.3.7
4.4
ULTRA WIDEBAND PHYSICAL SPECIFICATION ...................................... 64
4.4.1
4.4.2
4.4.3
4.4.4
4.4.5
4.4.6
4.4.7
4.4.8
4.5
Medium access strategies ................................................................................ 53
Superframe structure........................................................................................ 54
Interval and duration calculations.................................................................... 55
Data transfer .................................................................................................... 58
Frame structure ................................................................................................ 60
Delivery mechanisms ...................................................................................... 62
Ranging............................................................................................................ 64
Channels and operating frequency bands ........................................................ 64
Signal flow....................................................................................................... 67
UWB frame format .......................................................................................... 67
UWB symbol structure .................................................................................... 68
UWB PHY rate and timing parameters ........................................................... 70
SHR preamble ................................................................................................. 72
PHR ................................................................................................................. 75
Data.................................................................................................................. 75
CURRENTLY AVAILABLE 802.15.4A HARDWARE ................................... 78
4.5.1 IMECs Digital UWB Transmitter IC .............................................................. 78
4.5.2 TES IEEE 802.15.4a transceiver with ranging capability ............................... 79
5. THE OMNET++ SIMULATION ENVIRONMENT ................................................. 80
5.1
NETWORK SIMULATORS............................................................................... 80
5.1.1 Available network simulators .......................................................................... 80
5.1.2 OMNET++ vs NS-2 ........................................................................................ 82
5.2
SIMULATION MODELING CONCEPTS ........................................................ 87
5.2.1 Discrete Event Simulation ............................................................................... 87
5.2.2 The event loop ................................................................................................. 89
5.3
OMNET++ INTRODUCTION ........................................................................... 90
5.3.1 OMNET++ model structure ............................................................................ 90
5.4
THE NED LANGUAGE ..................................................................................... 91
5.4.1
5.4.2
5.4.3
5.4.4
5.4.5
5.5
Import directives .............................................................................................. 92
Channel definitions .......................................................................................... 92
Simple module definitions ............................................................................... 93
Compound module definitions ........................................................................ 94
Network definitions ......................................................................................... 96
SIMPLE MODULES ........................................................................................... 96
5.5.1 Handling events ............................................................................................... 97
5.5.2 Passing messages ............................................................................................. 97
5.6
COMPOUND MODULES .................................................................................. 98
5.7
GATES AND CONNECTIONS ......................................................................... 98
5.8
MESSAGES ........................................................................................................ 98
5.8.1 Simulating packets ........................................................................................... 98
5.8.2 Message definitions ......................................................................................... 99
5.9
SUMMARY ........................................................................................................ 99
6. SIMULATING A 802.15.4A ULTRA WIDEBAND PHYSICAL WSN ................. 101
6.1
SIMULATION GOALS .................................................................................... 101
6.2
OMNET++ 802.15.4A UWB PHY SIMULATION MODEL .......................... 102
6.2.1
6.2.2
6.2.3
6.2.4
6.2.5
6.2.6
6.2.7
6.2.8
6.2.9
6.2.10
6.2.11
6.2.12
6.2.13
6.3
Previous work ................................................................................................ 102
Contributions ................................................................................................. 103
Channels ........................................................................................................ 104
UWB PHY rate, timing and preamble parameters ........................................ 105
UWB simulation model process flow............................................................ 105
UWB frame format ........................................................................................ 108
UWB pulse .................................................................................................... 108
SHR preamble generation.............................................................................. 109
UWB symbol generation ............................................................................... 113
Data bits generation ................................................................................... 114
PHR ........................................................................................................... 114
Reed-Solomon encoding and decoding ..................................................... 116
Convolutional encoding and Viterbi decoding .......................................... 116
SIMULATING 802.15.4A WSN....................................................................... 118
6.3.1 Application layer ........................................................................................... 118
6.3.2 Network layer ................................................................................................ 118
6.3.3 MAC layer ..................................................................................................... 118
7. SIMULATION RESULTS .......................................................................................... 120
7.1
ASSUMPTIONS ............................................................................................... 120
7.2
CONVOLUTIONAL ENCODER AND VITERBI DECODER PORTING .... 121
7.3
SIMULATION MESSAGE TRACE BETWEEN 2 NODES ........................... 122
7.4
2-NODE 802.15.4A UWB WSN....................................................................... 126
7.4.1
7.4.2
7.4.3
7.4.4
7.4.5
7.4.6
7.5
Effect of different bit rates............................................................................. 127
Effect of mean pulse repetition frequency (PRF) .......................................... 131
Effect of center frequency ............................................................................. 133
Effect of bandwidth ....................................................................................... 133
Sub-Gigahertz band communication ............................................................. 135
Effect of forward error correction ................................................................. 135
MULTI-NODE 802.15.4A UWB WSN ............................................................ 139
7.5.2 802.15.4a UWB PHY throughput ................................................................. 140
7.6
PROPOSAL FOR A COGNITIVE AND ADAPTIVE TECHNIQUE ............. 144
8. CONCLUSION ............................................................................................................ 148
8.1
CONCLUSION ................................................................................................. 148
8.2
FUTURE WORK\CONTRIBUTIONS ............................................................. 149
8.2.1
8.2.2
8.2.3
8.2.4
8.2.5
8.2.6
8.2.7
Compliant UWB pulse .................................................................................. 149
802.15.4 MAC ............................................................................................... 149
PHR ............................................................................................................... 149
Correlation receiver ....................................................................................... 149
Ranging.......................................................................................................... 149
Cognitive UWB Impulse Radio..................................................................... 150
TOSSIM implementation .............................................................................. 150
REFERENCES ................................................................................................................ 151
A. SOURCE CODE ......................................................................................................... 158
A.1
FOLDER STRUCTURE ................................................................................... 158
A.1.1
A.1.2
A.1.3
A.1.4
Documentation .......................................................................................... 159
OMNET++ ................................................................................................ 159
Matlab ........................................................................................................ 159
Results ....................................................................................................... 159
B. SERVICE PRIMITIVES ........................................................................................... 160
B.1
SERVICES AND PRIMITIVES ....................................................................... 160
B.2
DATA UNITS ................................................................................................... 162
B.2.1
B.2.2
Service Data Unit....................................................................................... 162
Protocol Data Unit ..................................................................................... 162
Chapter 1
1.Introduction
Ultra wideband (UWB) refers to a wireless technology that employs very narrow pulses to
transmit energy across a wide spectrum of frequencies, in comparison with existing
narrowband technologies that makes use of a frequency carrier to transmit data. This
wideband nature of ultra wideband systems incorporates a lot of promising concepts for
wireless communications.
Wireless sensor networks (WSNs) consist of a network of small autonomous wireless
devices with sensors to cooperatively monitor the environment, capture data for
processing, perform home or industrial automation tasks, or to be used for surveillance.
Wireless sensor networks have certain limitations which the features of ultra wideband
technology seem to potentially address.
Tools to evaluate and understand the performance of new wireless communication
technologies are necessary and invaluable. The OMNET++ network simulation
environment provides the necessary framework and tools to enable the simulation of a lowrate ultra wideband implementation in wireless sensor networks. This low-rate ultra
wideband implementation is defined by the IEEE 802.15.4a standard.
1.1
SCOPE
The scope of this research pertains to the analysis of the IEEE 802.15.4a standard for lowrate wireless personal area networks (WPANs) through simulations in software. The IEEE
802.15.4a standard defines two new alternate physical layers for the IEEE 802.15.4
standard better known as Zigbee. This research focuses on the ultra wideband physical
layer introduced by the IEEE 4a task group. Furthermore, this dissertation also explores the
fundamentals of ultra wideband technology and its applicability to wireless sensor
networks by evaluating the performance of a software implemented IEEE 802.15.4a lowrate ultra wideband model in a simulated wireless sensor network.
Chapter 1
1.2
Introduction
PROBLEM STATEMENT AND MOTIVATION
Due to advances in high-speed switching technology ultra wideband gained attraction in
low-cost consumer electronics and computer equipment. While these kinds of applications
do not have a big problem with power, the same can not be said for wireless sensor
networks. Sensor nodes are usually deployed once-off, sometimes without dedicated power
supplies, and even for those that do employ batteries the recharging and replacing of those
batteries is not an option. Size and cost constraints on sensor nodes result in further
constraints on node resources such as memory, speed and bandwidth. Ultra wideband
technology has proven to provide very robust communications with high data rates over
short distances while being very conservative with energy consumption. The carrierless
property of ultra wideband also implies that such radios can be manufactured
inexpensively.
The choice of frequency band is an important factor for a device wanting to communicate
wirelessly because it determines the capacity and possible interference from other systems
that might be communicating in the same frequency band. The public ISM bands for
example have no usage restrictions and systems operating in these bands need to coexist
with each other. Specific ultra wideband impulse radio spreading techniques can be
utilized to ensure coexistence with the numerous other radio systems. In addition, due to
the large bandwidth available, a multiple access system may accommodate many users.
As of this writing, ultra wideband hardware is difficult to get hold of and any hardware that
can be acquired carries an expensive price tag. In such a case, network simulator tools are
of great value in demonstrating the capabilities of new network technologies if the required
models are available. The two most popular network simulators used by academia are NS2 [1] and OMNET++ [2]. For each of these network simulators an ultra wideband model
contributed by former research exists [3], [4]. Both of these models provide excellent
frameworks but do not claim to be complete and will therefore greatly benefit from any
additional academic contributions to address shortcomings.
Much research has been done in recent years on the IEEE 802.15.4 standard and Zigbee
but this is not the case for the low-rate ultra wideband physical layer amendment to the
standard defined in IEEE 802.15.4a. This dissertation contributes to the knowledge area of
low-rate ultra wideband and wireless sensor networks by providing an investigation into
Electrical, Electronic and Computer Engineering
2
Chapter 1
Introduction
the viability of using low-rate ultra wideband as the communication medium for wireless
sensor networks.
1.3
OBJECTIVES
The objective of this research is to do an in depth study into the intricacies of the IEEE
802.15.4a standard as it is defined for the new ultra wideband physical layer in order to
clarify its complex inner workings and examine its viability for wireless sensor network
applications.
To achieve this goal the most suitable simulation environment and an existing IEEE
802.15.4a ultra wideband physical layer simulation model will be chosen, investigated and
adapted to ensure it accommodates all required features. Moreover, this model will be
employed in a wireless sensor network for various performance analyses.
1.4
RESEARCH METHODOLOGY
Ultra wideband is an exciting new technology with lots of potential and this excitement
sparked an extensive literature study to better understand the technology with all of its
principles and characteristics that distinguishes it from other wireless technologies.
Thereafter a thorough study was made into the IEEE 802.15.4 and IEEE 802.15.4a
standards to acquire the necessary know-how to be able to investigate and complement
existing ultra wideband simulation models following this standard.
The study continued into the area of wireless sensor networks with emphasis on the
limitations such networks currently experience.
When implementing a network simulation model, a key component is the network
simulation environment and corresponding framework. A comprehensive study was made
into different wireless network simulators after which the most suitable simulator and
corresponding framework was chosen and closely examined to make proper use of its
features.
The practical simulation work was mainly supported by and built upon the Mixim-UWB
framework [4] implemented by Jérôme Rousselot for the OMNET++ network simulator.
Electrical, Electronic and Computer Engineering
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Chapter 1
1.5
Introduction
DOCUMENT OUTLINE
This dissertation consists of several chapters in which the various aspects of WPAN
standards, ultra wideband technology, Zigbee technology, wireless network simulators,
amendments to the IEEE 802.15.4 standard and simulation thereof are explored and
discussed.
Chapter 2 will present a history and background into wireless communications. The
organization of the different IEEE 802.15 Working Group activities is outlined to give a
bird’s eye view of where the theme of this paper fits into the big picture of communication
standards. The chapter finishes with an overview of wireless sensor networks.
Chapter 3 will engage into a thorough study and discussion of ultra wideband technology,
looking at its advantages, disadvantages, unique characteristics, signal generation,
modulation schemes, antenna considerations, and transmitter and receiver design.
Chapter 4 will engage a thorough investigation of the IEEE 802.15.4 and IEEE 802.15.4a
standards to introduce all the detailed features, methods and techniques specified and
applicable to a low rate ultra wideband PHY and corresponding MAC implementation.
Chapter 5 will regard the OMNET++ discrete event simulation environment in comparison
to other network simulators. A brief outline of the OMNET++ features is also provided.
Chapter 6 discusses the chosen IEEE 802.15.4a UWB model and the utilization of the
model in a simulated wireless sensor network.
Chapter 7 provides the results of the simulation sets and discusses the conclusions reached
from these results.
Chapter 8 finishes with a summary of the research performed and what has been achieved
in this dissertation. It also includes suggestions for further work and future research based
on this topic.
Electrical, Electronic and Computer Engineering
4
Chapter 2
2.Background
2.1
WIRELESS DATA COMMUNICATION
During the past few years the wireless communication world saw the rise of an old
wireless radio technology with booming potential. This section takes an in depth look into
the heart of Ultra Wideband.
2.1.1
History of wireless
The date is December 12, 1901. Using a crude spark-gap transmitter and a balloonsupported antenna for reception, Guglielmo Marconi repeatedly transmitted the Morse
code letter S from Poldhu, Cornwell, in England, across the Atlantic Ocean to St John’s,
Newfoundland, in Canada, totaling a distance of about 3500 kilometers. With this
technological breakthrough Marconi sparked a wireless revolution [5], [6].
Despite the fact that Marconi proved the concept of wireless telegraphy with the spark-gap
transmitter, it had some severe shortcomings. One of which is that each spark caused a
burst of electromagnetic radiation which created pulses of energy with extremely short
duration inducing excessive interference with sidebands. After different frequency and
power limitations over the following years, spark-gap systems were totally banned in the
United States by 1927 [7].
2.1.2
Narrowband transmission
To demonstrate the effect of such a short energy pulse, we have to look at the correlation
between the time domain and frequency domain of the pulse as expressed by the Fourier
transform.
Modeling the short energy pulse with a unit impulse function defined as
= 1,
0,
= 0
≠ 0
(2-1)
Chapter 2
Background
we can see from Figure 2-1 that the Fourier transform of demonstrates how such
a pulse signal radiates as every frequency in the spectrum due to the infinitesimal time
duration of the impulse signal.
Figure 2-1. Fourier transform of an impulse function
The unit impulse signal illustrates the ideal case. In real life the time duration of such a
signal will be finite and somewhere in the nanosecond time range. Still, it is clear that the
transmit power of such signal should be severely limited in order to prevent interference
with other transmitters and receivers wanting to share the same airwaves. Furthermore,
since transmission occurs in many frequency bands, it becomes difficult for the receiver to
distinguish between information and noise.
Marconi’s spark-gap transmitters (largely based on the ingenious research done by Nikola
Tesla [8]) generated fairly broad signals having wavelengths between 250 meters (1.2
MHz) and 550 meters (545 kHz). Frequency band crowding and interference worsened and
Electrical, Electronic and Computer Engineering
6
Chapter 2
Background
as a result legislation were put in place to prohibit the use of spark-gap transmitters.
It is mostly because of these reasons that scientists came up with the concept of separation
by frequency where a specific frequency, which can easily be isolated from other
frequencies via filtering, is used to transmit information.
Shorter wavelengths in the form of continuous wave (CW), which modulate a carrier signal
in some way to convey information, are characterized by these frequency separation
properties. Figure 2-2 shows the Fourier transforms of two sinusoidal waves. If a
sinusoidal wave is employed as a carrier signal it appears as a very narrow pulse in the
frequency domain, making it easy to filter out other transmissions. This method is best
known as narrowband transmission.
Figure 2-2: Fourier transforms of sine waves
To keep people from interfering with valuable wireless services such as emergency and
military broadcasts, the frequency spectrum is divided up and regulated by various
Electrical, Electronic and Computer Engineering
7
Chapter 2
Background
governments of which the Federal Communications Commission (FCC) in the United
States is the largest.
The limited bandwidth nature of a narrowband signal places limitations on the amount of
information the signal can transmit.
2.1.3
Shannon’s information capacity theorem
Shannon’s information capacity theorem is expressed as
S
= log 1 + bps
N
where
(2-2)
is the information capacity of the channel (bps),
is the channel bandwidth (Hz),
S is the total signal power over the bandwidth (Watt),
N is the total noise power over the bandwidth (Watt),
is the received signal-to-noise (SNR) ratio.
[9] defines information capacity as the maximum rate at which information can be
transmitted across the channel without error.
Equation (2-2) distinguishes three things we can do to improve the capacity of the channel.
-
Increase the bandwidth Increase the signal power S
Decrease the noise N
Furthermore we also see that the capacity of a channel grows linearly with increasing
bandwidth , but only logarithmically with signal power S.
With the frequency spectrum becoming more and more crowded and the ever increasing
demand for higher data rates, the bandwidth limitation of narrowband systems coerced
scientists to develop new transmission techniques without using up and interfering with
other reserved parts of the spectrum. One of these new techniques goes back and mimics
the first technology used to transmit wireless signals.
Electrical, Electronic and Computer Engineering
8
Chapter 2
2.1.4
Background
History of Ultra Wideband
The origin of ultra wideband (UWB) technology stems from work in time-domain
electromagnetics begun in 1962. However, it was not until the advent of the sampling
oscilloscope in the early 1906s and the development of techniques for generating subnanosecond baseband pulses, that proper observation and measurements could be done.
Impulse measurement techniques were used to characterize the transient behavior of
certain microwave networks.
From measurement techniques the main focus moved to radar and communication devices
of which radar was given a lot of attention because the low-frequency components were
useful in penetrating objects.
The first UWB communications patent was awarded in 1973.
In 1989 the U.S. Department of Defense (DoD) started to use the term “ultra wideband”
for this baseband, carrier-free, impulse technology which by then had experienced nearly
30 years of extensive development. Most applications and development occurred in
classified military programs whose main driving force was accurate radar and
communications technology that cannot be easily intercepted. Other UWB applications
included automobile collision avoidance, positioning systems, liquid level sensing and
altimetry [10].
In recent years UWB for consumer communication got a lot of attention and companies
such as Alereon [11], Time Domain [12], Wisair [13] and XtremeSpectrum (acquired by
Motorola [14]) were started to investigate and provide solutions for the use of UWB in
personal computing, consumer electronics, and mobile devices.
2.2
STANDARDS ACTIVITY OF WPANS
The standards activity of Wireless Personal Area Networks (WPANs) is taken care of by
the IEEE 802.15 standards working group [15]. IEEE 802.15 is responsible for creating
and maintaining WPAN standards and is divided into six major task groups as shown in
Figure 2-3.
.
Electrical, Electronic and Computer Engineering
9
Chapter 2
Background
Figure 2-3: Organization of IEEE 802.15 Working Group activities
2.2.1
Task group 1 (Bluetooth)
The IEEE Project 802.15.1-2002 used the Bluetooth v1.1 Foundation Specifications to
derive a WPAN standard. It incorporates a medium access control (MAC) and physical
layer specification. The standard was updated to include the additions of Bluetooth v1.2
and published as IEEE 802.15.1-2005 [16].
2.2.2
Task group 2 (Coexistence)
The IEEE Project 802.15.2 developed a Recommended Practices to facilitate coexistence
of WPANs (802.15) and WLANs (802.11). The Task Group also developed a set of
Coexistence Mechanisms to facilitate coexistence of WLAN and WPAN devices [17].
2.2.3
Task group 3 (High Rate WPAN)
The IEEE Project 802.15.3 was chartered to define a new standard for high-rate WPANs.
The new standard will also address the need for portable consumer digital imaging and
multimedia applications by providing low power, low cost solutions [18].
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10
Chapter 2
Background
IEEE 802.15.3a (WPAN High Rate Alternative PHY)
The IEEE 802.15 Task Group 3a (TG3a) was established with the purpose to draft and
publish a higher speed alternative physical layer concept for the existing 802.15.3 standard.
With a minimum data rate of 110 Mbps at 10m, the intend were to address the high data
rate demand of applications incorporating video, imaging and multimedia links [19].
The IEEE 802.15 TG3a managed to consolidate a total of 23 UWB PHY specifications
into 2 proposals backed by two different industry alliances:
•
Multi-Band Orthogonal Frequency Division Multiplexing (MB-OFDM) UWB,
supported by the WiMedia Alliance [20]
•
Direct Sequence UWB (DS-UWB), supported by the UWB Forum [21]
On January 19, 2006 the TG3a members unanimously voted to withdraw the 802.15.3a
project authorization request (PAR) because the process was in total standstill. Consensus
could not be reached between the two industry alliances as to which technology would
become the 802.15.3a standard for high-speed wireless [22].
IEEE 802.15.3b (MAC Amendment)
The IEEE 802.15 Task Group 3b (TG3b) is working on an amendment to the 802.15.3
standard in order to improve implementation and interoperability of the MAC [23].
IEEE 802.15.3c (WPAN Millimeter Wave Alternative PHY)
The IEEE 802.15 Task Group 3c (TG3c) is developing an alternative physical layer for the
802.15.3 standard based on millimeter-wave technology. The millimeter-wave WPAN will
operate in the new and clear band defined by FCC 47 CFR 15.255 and will allow for very
high data rates over 2Gbps [24].
2.2.4
Task group 4 (Low Rate WPAN)
IEEE 802.15.4 (Zigbee)
The IEEE 802.15 Task Group 4 (TG4) was tasked to investigate a low data rate solution
with very low complexity and high energy efficiency that will allow for batteries to live
from multi-months to multi-years. The potential applications are sensors, interactive toys,
Electrical, Electronic and Computer Engineering
11
Chapter 2
Background
smart badges, remote controls, and home automation. The current version of the standard is
the 2006 revision [25].
The Zigbee protocol stack employs the 802.15.4 standard as its base and provides a
complete networking solution by developing the upper layers which are not covered by the
standard. This paper takes an in depth look into the 802.15.4 standard together with its
amendment, 802.15.4a, which is discussed next.
IEEE 802.15.4a (WPAN Low Rate Alternative PHY)
The IEEE 802.15 Task Group 4a (TG4a) provides alternative physical layers for low rate
WPANs and is an amendment to the 802.15.4 standard.
It allows for additional capabilities over the existing 802.15.4 standard by
-
providing communications and high precision ranging capability,
-
high aggregate throughput,
-
ultra-low power,
-
as well as adding scalability to data rates,
-
longer range, and
-
lower cost.
The current status of 802.15.4a is complete and requires no further TG4a effort [26].
The main focus of this dissertation is the analysis of the 802.15.4a ultra wideband physical
through a simulation model that can be used to simulate a WSN. An in-depth discussion of
this amended standard is presented in Chapter 4.
IEEE 802.15.4b (Revisions and Enhancements)
The IEEE 802.15 Task Group 4b (TG4b) was chartered to provide for specific
enhancements and clarifications to the 802.15.4-2003 standard by resolving ambiguities,
reducing unnecessary complexity and allocating frequency that recently became available.
IEEE 802.15.4b was approved in June 2006 and was published in September 2006 as IEEE
802.15.4-2006 [27].
Electrical, Electronic and Computer Engineering
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Chapter 2
2.2.5
Background
Task group 5 (Mesh networking)
The IEEE Project 802.15.5 is responsible for defining a PHY and MAC layers standard to
enable mesh networking in WPANs [28].
Mesh networking is a way to route data between nodes. Packets are “hopping” from node
to node until the destination is reached. Connections between nodes can be reconfigured to
get around broken or blocked paths.
Two connection arrangements are defined:
1. Full mesh topology: All nodes are connected to each other.
2. Partial mesh topology: Some nodes are connected to all the others while some only
connect to those they communicate with most of the time. All nodes can still
communicate with each other through multiple hops.
Mesh networks extend network coverage without increasing the transmit power or receive
sensitivity. Furthermore, they also enhance reliability through route redundancy, drain less
battery power due to fewer transmissions and make it easier to configure the network.
2.2.6
Task group 6 (BANs)
The IEEE Project 802.15.6 is focusing on Body Area Network (BAN) technologies. The
goal is to develop a communication standard optimized for low power devices operating
on, in or around the human body. Typical applications areas include medical, consumer
electronics, personal entertainment, and other [29].
2.3
WIRELESS SENSOR NETWORKS
Wireless Sensor Networks (WSNs) are the main representative of pervasive computing in
large-scale physical environments. These networks consist of a large number of small,
wireless devices embedded in the physical world to interact with their environment by
sensing or controlling physical parameters.
Electrical, Electronic and Computer Engineering
13
Chapter 2
2.3.1
Background
Applications
WSNs show a clear difference between sources of data and sinks of data. Sources are the
actual sensor nodes that sense the data. Sinks are nodes where the data should be delivered
to and can be part of the sensor network or an outside system.
A sensor node reports to the sink(s) once it has detected the occurrence of some event it
was tasked to monitor. For more complicated tasks a sensor node can collaborate with
other sensor nodes to decide whether an event has occurred, as would be required when the
source of the event is mobile. Sensor nodes can also be tasked with periodic reporting of
measured values.
Sensor nodes can be deployed in a number of ways ranging from fixed to random
deployment depending on the application and environment. This causes concerns for the
lifetime of the network because of the limited options available for maintenance and power
source replacement.
The following extensive list of real-life application examples for WSNs is provided by
Karl et al [30]:
•
Disaster relief
•
Environment control and biodiversity mapping
•
Intelligent buildings
•
Facility management
•
Machine surveillance and preventative maintenance
•
Precision agriculture for irrigation and fertilizing
•
Medicine and health care
•
Logistics and passive RFID tags
•
Telematics for traffic information
Electrical, Electronic and Computer Engineering
14
Chapter 2
2.3.2
Background
Sensor nodes
Usually a single sensor node is incapable of fulfilling the tasks of the WSN on its own and
has to collaborate with other sensor nodes using the wireless radio that forms part of the
sensor node. The components a basic sensor node comprises of are shown in Figure 2-4.
Figure 2-4: Hardware components of basic sensor node
Each of these components have to be operated in such a way as to consume as little power
as possible while still fulfilling their required tasks. This is because energy is a very scarce
resource in wireless sensor networks. Mains power is not an option as a truly wireless
system is one where all nodes run without any attached cables, therefore a local power
supply is required. Unfortunately hundreds of wireless sensors can be deployed in an
environment where it is near impossible to reach the nodes and replacement of batteries is
not an option.
2.3.3
Power sources
Mahlknecht [31] lists the following main energy sources available for wireless sensor
networks which is categorized as either stored energy, or salvaged energy.
Stored energy
•
Batteries: Stored energy is conventionally provided through batteries. Batteries
have a finite lifetime and need to be replaced or recharged which can be very
Electrical, Electronic and Computer Engineering
15
Chapter 2
Background
difficult and expensive. The following list indicate the variety of different batteries
available:
o Alkaline Manganese
o Lithium Cells
o Zink Air
o Silver Oxide
And for rechargeable batteries:
o Nickel Metal Hydride
o Lithium Ion
o Lithium Manganese
o Lithium Vanadium Penthoxide
•
Capacitors: Apart from traditional batteries, there are also other forms of energy
reservoirs. High energy capacitors represent an interesting alternative. “Gold caps”
or “Ultra capacitors” are the names given to these high capacity energy stores
which can easily and quickly be recharged and do not wear out over time.
Salvaged energy
The concept is also known as “scavenged” energy and it aims to provide methods that will
allow a node to tap into energy from the environment the node operates in. The drained
energy can then be saved in an energy supply on the node itself. The following approaches
exist:
•
Photovoltaic cells: These are the well-known solar cells. If sufficient light is
available on a regular basis this method can ensure continuous operation of the
sensor node.
•
Mechanical vibrations: With this method mechanical vibrations from sources such
as walls or windows that vibrate, due to the operation of heavy machinery or the
passing of vehicles nearby, are used to generate electrical energy. Several means
Electrical, Electronic and Computer Engineering
16
Chapter 2
Background
exist to convert vibrations into electrical energy based on electromagnetic,
electrostatic and piezoelectric principles [32].
•
Thermo elements: In this method temperature differences are directly converted to
electrical energy. The Peltier effect and Seebeck effect are both thermoelectric
phenomenon that can be used to generate electric energy between metals.
2.3.4
Challenges
The typical application areas of WSNs demand for certain required mechanisms to be in
place before such networks can be properly realized. Therefore, WSNs pose certain
challenges [33] that are discussed next.
Scalability
Since WSNs can have a large number of nodes, the employed architectures and protocols
need to be able to handle these large numbers and any additional node increases. To
transmit information to a destination node not in the source node’s vicinity, might require
higher transmission power which can cause inter-node interference and drain the power of
the transmit node.
Energy-efficient operation
To extend the lifetime of the entire network, power conservation at individual node level is
of utmost importance. The major culprit at exhausting the power supply of a sensor node is
the wireless radio. Hence, avoiding unnecessary communications can save energy
considerably. For long distance communications a multi-hop wireless communication
scheme where intermediate nodes are used as relays can also reduce the energy
consumption of a single node.
Self-organization
A WSN needs to autonomously configure most of its operational parameters so that the
network is able to tolerate the failure of nodes and the integration of new nodes. Selforganization should be performed such that the overall WSN performance is improved
while reducing power consumption.
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Chapter 2
Background
Synchronization
Due to the large sensor population, global synchronization of the whole network is not
viable and therefore focus has to be given to node-by-node synchronization. If nodes are
not synchronized in time with each other they have to continuously poll the network,
listening for transmissions, and needlessly drain power.
Channel estimation
Many methods exist to estimate the quality of the wireless link, but basically it involves
receiving packets and gauging their quality or calculating packet loss ratios. There are two
types of estimators:
•
Active estimator: Special packets are sent out by the node which requests certain
measurement responses from neighbor devices.
•
Passive estimator: Nearby transmissions are observed by the node to estimate loss
rates and quality.
The channel estimation knowledge is crucial since it can be used to overcome the
detrimental effects of noise, multipath, intentional jamming and inter-node interference by
ensuring reliable data transfer between the nodes.
Extensive research has taken place to determine the best possible hardware and software
solutions to all of the above challenges, all based on conventional narrowband technology.
Ultra wideband technology addresses most of these issues just with its core characteristics.
Some of these WSN challenges are addressed by the IEEE 802.15.4 and Zigbee standards,
hence this study also aim to look at the role the IEEE 802.15.4a UWB physical layer
amendment can play in complementing these solutions and dealing with the remaining
challenges.
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Chapter 3
3.Overview of Ultra Wideband
In this chapter we take an in-depth look at the exciting wireless technology known as ultra
wideband (UWB). We start with the definition of ultra wideband and list the properties
distinguishing it from other wireless technologies. UWB waveforms generation,
modulation schemes and multiple access strategies are investigated. Channel models for
UWB are presented with a brief discussion on UWB transceiver and antenna design.
3.1
DEFINING ULTRA WIDEBAND
UWB is a spread spectrum technology. Spread spectrum is an RF communications
technology in which the bandwidth of the baseband signal is intentionally spread over a
larger bandwidth by injecting a higher-frequency signal. Consequently, the transmit energy
is spread over a wider bandwidth and the signal appears as noise. Refer to [34], [35] and
[36] for a broad overview of spread spectrum principles.
Even so, UWB differs from conventional spread spectrum technologies because in a UWB
system information is transmitted through a series of short pulses or a “chirped” signal
while with traditional spread spectrum systems information is transmitted by modulating a
continuous carrier signal. Also, a UWB system occupies a lot more bandwidth than a
spread spectrum system.
The Federal Communications Commission (FCC) formally defines a UWB device as any
intentional radiator with a fractional bandwidth greater than 0.20, or with a UWB
bandwidth equal to or greater than 500 MHz [37].
UWB bandwidth is defined as the frequency band bounded by the points that are 10 dB
below the highest radiated emission.
Fractional bandwidth ( is defined as
=2
" − $ " + $ (3-1)
Chapter 3
Overview of Ultra Wideband
where " is the upper frequency of the -10 dB emission point and $ is the lower frequency
of the -10 dB emission point.
Furthermore, the transmission center frequency % is defined as the average of the upper
and lower -10 dB points, i.e.
% =
3.1.1
$ + " 2
(3-2)
UWB Power Spectral Density
Power spectral density (PSD) describes the distribution of signal power over frequency. It
can be calculated as:
&'( =
&)
*
(3-3)
where &) is the transmit power in Watts and * is the bandwidth of the signal in Hz.
Therefore PSD is measured in Watts/Hz.
Narrowband technologies have a high power spectral density compared to UWB because
in UWB the transmit energy is spread over a very large bandwidth (see Figure 3-1).
It is noteworthy to mention that a very low PSD allows for covert communication.
3.1.2
UWB Regulations
Due to the wide bandwidth occupied by UWB emissions, it could potentially interfere with
other licensed bands in the frequency spectrum if left unregulated.
Many organizations and government entities around the world set rules and
recommendations for UWB usage. On the international level there is the International
Telecommunication Union (ITU). In the Asia-Pacific region the Asia-Pacific
Telecommunity (APT) is responsible for telecommunication recommendations and
guidelines. Japan has the Ministry of Internal Affairs and Communication (MIC) as
regulatory body. The European Conference of Postal & Telecommunications
Administrations (CEPT) created a task group under the Electronic Communications
Committee (ECC) to draft a proposal regarding the use of UWB for Europe with Ofcom
being the independent regulator and competition authority for the communication
Electrical, Electronic and Computer Engineering
20
Chapter 3
Overview of Ultra Wideband
industries in the United Kingdom. In the USA, the Federal Communications Commission
(FCC) is charged with regulating interstate and international communications. The USA
was the first country to legalize UWB for commercial use.
The FCC first set in motion a Notice of Inquiry (NOI) in September of 1998 following the
argument that low power wireless services could operate below authorized out-of-band
emissions limits described in the FCC Part 15 rules for intentional and unintentional
radiators in unlicensed bands.
The emission limits are defined in micro volts per meter (uV/m) and in order to express
this in terms of radiated power, the following formula can be used:
+, 4./ &=
0
(3-4)
where +, represents the electric field strength (V/m), / is the radius of the sphere at which
the field strength is measured, and 0 = 377 ohms is the characteristic impedance of a
vacuum [38].
In May of 2000, the FCC issued a Notice of Proposed Rule Making (NPRM) that could
allow UWB emitters under the Part 15 rules.
On February 14 of 2002, the FCC issued a first Report and Order [37], revising the Part 15
rules regarding UWB transmission systems by permitting UWB intentional emissions
subject to certain frequency and power limitations. A total of 7500 MHz of spectrum in the
3.1 GHz to 10.6 GHz frequency band was allocated for the unlicensed use of UWB
devices.
Figure 3-1 shows the spectrum masks for indoor and outdoor operation permitted under
Part 15 of the Commission’s rules. UWB signals must be transmitted at low radiated
power, with the rules specifying a mean EIRP of -41.3 dBm/MHz. Effective isotropic
radiated power (EIRP) is the amount of power supplied to an isotropic antenna multiplied
by the antenna gain in given direction. An isotropic radiator radiates power equally in all
(theoretically) directions. The gain of an antenna represents how well it increases effective
signal power in a particular direction. So, the EIRP refers to the highest signal strength
measured in any direction at any frequency from the UWB device.
Electrical, Electronic and Computer Engineering
21
Chapter 3
Overview of Ultra Wideband
Table 3-1 and Figure 3-1 gives a comparison between the spectrum allocations for
unlicensed bands in the USA.
Table 3-1: FCC spectrum allocation for unlicensed use in the USA
Unlicensed bands Operating frequency (GHz) Bandwidth (MHz)
ISM
2.4 to 2.4835
83.5
U-NII
5.15 to 5.35
300
UWB
3.1 to 10.6
7500
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22
Chapter 3
Overview of Ultra Wideband
Figure 3-1: FCC unlisensed bands and UWB spectral mask
Electrical, Electronic and Computer Engineering
23
Chapter 3
3.2
Overview of Ultra Wideband
ADVANTAGES OF UWB
UWB offers many advantages over narrowband technology making it very attractive for
consumer communications applications.
3.2.1
Improved channel capacity
In Section 2.1.3, the channel capacity equation of Shannon (Equation 2-2) showed that
increasing the channel capacity requires a linear increase in bandwidth and exponential
increase in power.
It is this property that makes UWB technology ideal for applications requiring very high
data rates whilst using very lower power. However, useful range is limited to about 10
meters due to the low power levels mandated by the FCC. Data rates of over 100 Mbps
have been demonstrated with the potential of even higher data rates over shorter distances.
3.2.2
Inherent robustness to multipath fading
The large bandwidth contributes to another advantage by making UWB very robust to
multipath fading.
Multipath is a signal propagation phenomenon where transmitted signals reach the
receiving antenna by multiple paths. The effects are constructive and destructive
interference, and phase shifting of the signal. The sum of the out of phase signals causes
destructive interference with multipath fading as a result.
Due to the wideband nature of UWB the transmitted signal is resistant to severe multipath
propagation. The narrow pulses prevent multiple reflections from combining destructively
at the receiver. Furthermore, the multipath components can be resolved and used to
improve signal reception using a rake receiver or channel equalization techniques [39],
[40].
3.2.3
Noise-like signal
As mentioned in Section 3.1.1, UWB pulses are transmitted at very low power levels and
in addition with some pseudo-randomness the signal can be made to appear noise-like
ensuring a very low probability of interception or detection.
Electrical, Electronic and Computer Engineering
24
Chapter 3
3.2.4
Overview of Ultra Wideband
Low complexity, low equipment cost and small form factor
UWB transmitters directly modulate a baseband signal onto an antenna. There is no need
for a RF mixer to do any frequency up-conversion or inject a carrier frequency. UWB
receivers may require more complex architectures but again, no frequency downconversion or complex delay and phase lock loops circuits are required. Consequently, all
tuned circuitry can be reduced and UWB radios can be implemented with low cost, low
power CMOS processes.
The reduction in passive components allow for devices with small size to be manufactured,
fulfilling a crucial requirement of certain electronic devices such as wireless sensors.
Designing for small but effective antennas still remains a challenge.
3.2.5
Low power consumption
The simple architecture also allows for the design of power economic circuits where
information is transmitted with very short, low energy pulses. Conserving power, even on
such small scale, holds huge benefits for wireless sensor networks.
3.2.6
Penetration ability
Certain applications benefit or rely on the ability of the communication system to penetrate
through physical objects typically found in home and office environments. UWB systems
operating on the lower center frequencies still provide this capability. The higher the center
frequency the lower the ability of the UWB pulses to pass through objects due to the
shorter wavelength. The relationship between frequency and wavelength is shown in
equation 3-5.
1=
2
(3-5)
where 1 is the wavelength in meters, is the frequency in Hz and 2 is the speed of light
defined as 299,792,458 m/s.
3.2.7
Accurate ranging and location detection
The very narrow transmit pulses give UWB radios a very good time domain resolution
allowing for location and position determination. UWB can be used as a short range
Electrical, Electronic and Computer Engineering
25
Chapter 3
Overview of Ultra Wideband
RADAR (Radio Direction And Ranging). A single receiver can determine the range of its
transmitter, and with the location information of three receivers triangulation can be used
to determine position with much better accuracy than a GPS (Global Positioning System).
3.3
UWB WAVEFORM GENERATION
FCC regulation 47 CFR Section 5.5 (d) [41] state that intentional radiators are prohibited
to produce class B emissions (damped waves). The damping oscillations of such
waveforms cause sharp peaks in the spectrum with only small bandwidth and these peaks
can cause serious interference with existing communication systems.
Desired UWB waveforms therefore, should provide a flat frequency domain spectrum in
order to meet FCC regulations. Gaussian, Rayleigh, Laplacian, cubic waveforms and
modified Hermitian monocycles are all examples of nondamped waveforms with nearly
flat spectrum. In the following pages we will take a closer look at Gaussian waveforms.
3.3.1
Gaussian waveforms
Gaussian waveforms get their name from their mathematically resemblance to the Gauss
function defined as
34 =
where σ is the standard deviation.
1
√2Πσ
8 9:
; /= ;
(3-6)
A Gauss pulse can be represented by the following equation where > is the time decay
constant:
? =
) ;
[email protected] B
A
8
(3-7)
To create a waveform suitable for UWB transmission, the Gaussian pulse is filtered which
gives the same effect as taking the derivative of equation 3-7. The waveform produced by
the first derivative of the Gaussian pulse is called a Gaussian monocycle (see Figure 3-2)
and has a single zero crossing. A Gaussian monocycle is given by
?
C D [email protected]) B;
= E8 A F
D
Electrical, Electronic and Computer Engineering
(3-8)
26
Chapter 3
Overview of Ultra Wideband
−2 [email protected]) B;
= 8 A
>
The waveform produced by the second derivative of the Gaussian pulse is called a
Gaussian doublet (see Figure 3-2) and has two zero crossings. A Gaussian doublet is given
by
?
CC D −2 [email protected]) B;
=
E
8 A F
D > −2 [email protected]) B; 2 −2 [email protected]) B;
= 8 A − G 8 A H
>
>
>
−2 [email protected]) B; 4 [email protected]) B;
= 8 A + I 8 A
>
>
−2
−2 [email protected]) B;
= G1 − H 8 A
>
>
(3-9)
Each additional derivative of the Gaussian pulse will result in an additional zero crossing
decreasing the relative bandwidth and increasing the center frequency for a fixed time
decay value > [42]. Figure 3-2 and Figure 3-3 shows the waveforms given by the first four
derivatives of the base Gauss pulse defined in Equation 3-7 with > = 50ps.
Electrical, Electronic and Computer Engineering
27
Chapter 3
Overview of Ultra Wideband
Figure 3-2: Time domain of Gaussian pulse with 1st and 2nd order derivatives
Figure 3-3: Time domain of Gaussian pulse with 3rd and 4th order derivatives
Electrical, Electronic and Computer Engineering
28
Chapter 3
Overview of Ultra Wideband
In order to get the frequency domain representation of the Gaussian waveforms discussed,
we have to take the Fourier transform of these waveforms. The Fourier transform of the
base Gauss pulse is given by
3 = ℱK?L
= M
N
9N
) ;
[email protected] B
A
8
8 9OPQ) D
= >√.8 9PQ)
;
(3-10)
The same can now be done for the derivative functions of the Gauss pulse. To simplify the
math we make use of the Fourier transform derivative theorem which states that the
derivative of a function 4 is equal to the Fourier transform of the function multiplied by
R2.S where T equals the derivative order. Equations 3-11 to 3-14 gives the Fourier
transforms of the first four derivative functions of the base Gauss pulse with Figure 3-4
and Figure 3-5 illustrating them graphically.
3 = >√.R2.8 9PQ)
;
3 = >√.R2. 8 9PQ)
;
3 = >√.R2.U 8 9PQ)
;
3 = >√.R2.I 8 9PQ)
;
(3-11)
(3-12)
(3-13)
(3-14)
From these figures we can clearly see the effect of each derivative order. As mentioned
each order will cause a decrease in the relative bandwidth and an increase of the center
frequency. It can also be seen that these waveforms are almost uniformly distributed across
the frequency spectrum.
3.3.2
Choice of waveform
Pulse characteristics of the waveform can be varied to define the energy in the frequency
spectrum to meet the design criteria and regulations. The design criteria for a UWB system
typically define the operating bandwidth, the spectral mask and the center frequency within
the spectrum of interest. The following characteristics of the generated waveform influence
these factors:
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Figure 3-4: Frequency domain of Gaussian pulse with 1st and 2nd order derivatives
Figure 3-5: Frequency domain of Gaussian pulse with 3rd and 4th order derivatives
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•
Overview of Ultra Wideband
Pulse duration: The time duration of the pulse and determines the bandwidth in the
frequency domain. As a guideline we can state that the reciprocal of the pulse
duration is approximately equal to bandwidth of the pulse.
•
Pulse repetition frequency (PRF): The number of UWB pulses that is transmitted
per second. If the pulse repetition is almost periodic, the PRF can be used to
determine the center frequency of the spectrum.
•
Pulse shape: The physical shape of the transmitted waveform. As was shown with
the different derivative waveforms of the Gauss pulse (Figure 3-2 to Figure 3-5).
3.4
UWB IMPULSE RADIO
In an impulse radio single short baseband pulses are generated for communicating data.
We now know that each of these pulses has a very wide spectrum which must adhere to
certain spectral requirements. Each pulse will have very low energy to ensure that the low
power levels permitted for UWB transmission is met which reduces the risk of interference
with other narrowband communication technologies. Because of the very low energy for
any given pulse, many pulses are combined to carry one bit of information.
3.4.1
Pulse trains
Such long sequences of very short duration pulses are known as pulse trains. If we take a
Gaussian monocycle ?V defined in equation 3-8 we can write an unmodulated periodic
monocycle pulse train W)XYZS as
S] N
W)XYZS = [ ?V − T\
S] 9N
(3-15)
where \ is the period between pulses. The reciprocal of \ gives us the pulse repetition
frequency (PRF). Figure 3-6 shows a monopulse train.
Pulse trains with a constant pulse interval \, introduce energy spikes as strong spectral
lines into the spectrum of the transmitted signal which might interfere with other RF
communication systems at short range [43]. Figure 3-7 shows that the resulting spectrum
for the pulse train of Figure 3-6 will have an overall envelope with the same shape as the
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spectrum of the single monocycle, but it will consist of harmonics of the PRF rather than a
continuous spectrum.
Figure 3-6: Monocycle pulse train
Examination of the period \ and PRF by Ghavami et al. [44] have shown certain
properties:
•
Increasing the PRF in time domain also increases the magnitude in the frequency
domain.
•
Decreasing the duration of the pulse in the time domain increases the spectrum
width in the frequency domain.
•
If a random pulse interval is used, the frequency components are unevenly spread
across the spectrum which produces a much lower peak magnitude spectrum.
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Figure 3-7: Spectrum of monocycle pulse train
Therefore, to overcome these spectral peaks, randomization techniques are used which
adds a random offset to each pulse to remove common spectral components. Figure 3-8
shows a monocycle with two other exact monocycles offset in time.
By combining the offset pulses of Figure 3-8 randomly, the pulse train in Figure 3-9 is
obtained (notice that the pulses are not evenly spaced). If we look at the resulting spectrum
of this pulse train with the random offset monocycles as depicted in Figure 3-10, we can
clearly see the smoothing effect these offsets have on the magnitude of the spectrum.
Because the random offset is not known by the receiver, a special cyclic sequence is used
instead. This sequence is called a pseudo-random noise (PN) code. At the receiver end
tracking is made easier since the PN codes are known and can easily be reproduced. Time
hopping (TH) and direct sequence (DS) randomization techniques are examined in section
3.7 when we look at multiple access techniques as well.
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Figure 3-8: Monocycle pulse with offset pulses
Figure 3-9: Monocycle pulse train with offset pulses
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Figure 3-10: Spectrum of monocycle pulse train with offset pulses
3.5
MULTIBAND UWB
With this approach the available spectrum is divided into smaller frequency bands, each
with a bandwidth of at least 500 MHz as shown in Figure 3-11. To accomplish this
multiple UWB signals are transmitted at the same time but because they operate at
different frequencies they do not interfere with each other.
The main advantages of multiband are its efficient use of the available spectrum and its
flexibility to coexist with other wireless technologies because the separate bands may be
treated independently.
Section 2.2.3 mentioned that the IEEE 802.15.3a high-speed UWB standard was never
completed because consensus could not be reached for the choice of technology. The
proposals were for Multi-Band Orthogonal Frequency Division Multiplexing (MB-OFDM)
UWB and Direct Sequence (DS-UWB) UWB.
It is interesting to know that after IEEE disbanded the 802.15.3a Task Group, the DSUWB approach was abandoned while the WiMedia Alliance continued to support the MB-
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OFDM approach through the European Computer Manufacturers Association (ECMA).
Today two international ISO-based specifications from ECMA, [45] and [46], exist with an
adoption of the WiMedia Alliance MB-OFDM UWB radio platform by USB-IF for the
highly anticipated Wireless USB [47].
Figure 3-11: Multibands over the UWB spectrum
3.6
UWB MODULATION TECHNIQUES
To limit interference caused by a periodic pulse train and to provide access for multiple
users wanting to communicate on the channel at the same time, the basic modulation must
include techniques to allow these requirements. TH and DS are such techniques which we
discuss in section 3.7.
First, we look at the different modulation methods available. The choice of modulation
scheme depends on the operating conditions and desired system complexity.
3.6.1
Pulse Position Modulation
Modulation method where bit information is encoded by transmitting a time shifted version
of the pulse used for communication. The concept is illustrated in Figure 3-12.
The time shift parameter is very important because the smaller the time shift, the more
synchronized the receiver will have to be (implying complex receiver design). If the
receiver is not properly synchronized it will make a lot of errors while trying to distinguish
between the different pulses. The larger the time shift, the more bandwidth is wasted
(compare with BPSK discussed in section 3.6.4).
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If the time shift between the pulses equals one pulse width, PPM is categorized as an
orthogonal modulation method.
Figure 3-12: Example illustrating PPM pulses
3.6.2
Pulse Amplitude Modulation
PAM is a modulation method where the bit information is encoded into the amplitude of
the transmitted pulse. The concept is illustrated in Figure 3-13.
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Figure 3-13: Example illustrating PAM pulses
3.6.3
On-Off Keying
In this modulation method, the absence of a pulse indicates a ‘0’ bit and the presence of a
pulse a ‘1’ bit.
Albeit a simple modulation scheme it is very susceptible to noise. When no pulse is
transmitted to indicate a ‘0’ bit, interfering signals might constructively add up and cause
an incorrect bit decision by the receiver.
3.6.4
Binary Phase Shift Keying
Binary Phase Shift Keying (BPSK), also known as Bi-Phase Modulation, is classified as an
antipodal modulation method. Basically information is encoded by inverting the
transmitted pulse shape (reverses the pulse phase by 180°). The concept is illustrated in
Figure 3-14.
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Figure 3-14: Example illustrating BPSK pulses
Compared to orthogonal PPM, BPSK provides for better bandwidth utilizations and
therefore increased data throughput. An orthogonal PPM system will have to delay the
pulse by at least one pulse width. During this delay period nothing is transmitted while in a
BPSK system pulses can immediately be transmitted after each other allowing for twice
the throughput.
3.6.5
Pulse Shape Modulation
Information can also be encoded by using two different pulse shapes. This technique is
employed by PSM schemes and is illustrated in Figure 3-15.
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Figure 3-15: Example illustrating PSM pulses
3.7
MULTIPLE ACCESS STRATEGIES
There are three main approaches to enable multiple access in UWB systems [44]. Two of
these techniques make use of unique pseudo random (PR) codes to identify users. The
randomization also serves to limit interference caused by a UWB pulse train by spreading
the RF energy across the frequency band. However the addition of a PR sequence requires
that the receiver knows the PR sequence of every user it communicates with. The third
approach defines unique frequency codes for each user.
PR codes and frequency codes are chosen to be orthogonal to ensure multiple signals from
multiple users do not interfere with each other.
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3.7.1
Overview of Ultra Wideband
Direct Sequence
Direct Sequence is the first of the two techniques that makes use of PR codes. Each user
employs its unique PR code to spread the data into multiple chips. A chip is represented by
a UWB pulse.
In order to transmit a bit of information, a sequence of chips gets transmitted instead of
transmitting bit per bit (see equation 3-16).
\^ = _% . \%
(3-16)
where the chip period \% equals the UWB pulse period \a . Therefore the chip rate is higher
than the data rate and is defined by the Pulse Repetition Frequency (PRF).
If the PRF is kept constant, increasing the number of chips _% will increase the processing
gain as shown in equation 3-17 but it will also reduce the data rate.
&3 = 10 logb, _%
(3-17)
DS allows users to transmit in the same bandwidth at the same time.
3.7.2
Time Hopping
Time Hopping is the second technique making use of PR codes. TH are usually used in
conjunction with PPM modulation i.e. TH-PPM. Each user employs its unique PR code to
hop transmissions in time. Therefore each user has its own time slice to transmit in with a
bit being represented by the position of a pulse in its time slice [48].
With the TH approach the PRF determines the nominal transmit time of each pulse [49].
TH-PPM encodes bit information as a pulse occurring before the nominal transmit time
and a pulse occurring after the nominal transmit time.
The number of users a TH system can support without interference is limited by the PR
code length. A PR code of length cde provides cde unique moments in time that can be
utilized for transmitting information. By increasing the number of possible hops (defined
by cde Multiple Access Interference (MAI) can be reduced but with a penalty in the data
rate.
If a pulse train is used instead of a single pulse, the processing gain defined by equation
3-17 applies. In addition, the low duty cycle also adds to the total processing gain. The low
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duty cycle is due to the fact that the time hopping frame width \Q >> \a requiring the
receiver to only listen for a small timeslot between pulses. The additional processing gain
is defined in equation 3-18 [12].
\Q
&3^f)ghg%ij = 10 logb, G H
\a
3.7.3
(3-18)
Orthogonal Frequency Division Multiple Access
In OFDMA the frequency band used for communication is divided into sub-bands and
each sub-band is assigned a unique frequency code. Each user gets pre-assigned one of
these frequency codes that it uses to frequency hop a UWB pulse train to be transmitted.
Consequently, users can transmit data simultaneously across multiple frequency ranges.
The divided frequency sub-bands are spread apart at precise frequencies to guarantee the
frequency codes are orthogonal.
3.8
UWB CHANNEL MODELS
The channel represents the physical medium through which communication signals will
propagate. In the case of a wireless technology, the signals travel through space and
encounter various distortions from multiple paths due to reflections, electromagnetic
interference from other wireless signals occupying the same space and inter symbol
interference from neighbor devices.
Several path loss models exist to allow for experimentation and simulation of the
attenuation these signal undergo before arriving at the receiver.
3.8.1
Free space propagation model
The free space path loss is the loss in a transmitted signal’s strength due to the spreading of
the electromagnetic wave. The longer the transmit distance, the larger the spread. This
model assumes a line-of-sight (LOS) path through normal free space to the receiver
without any obstacles and as such does not include the effects of reflection, refraction or
diffraction. The resulting path loss is given by
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4.
cD = 20 log + 20log D
1
(3-19)
where 1 is the signal wavelength and D is the distance the signal travels [38].
3.8.2
Saleh-Valenzuela path loss model
The Saleh-Valenzuela model is for indoor environments only. It assumes that the multipath
components arrive in clusters made up from the multiple reflections bouncing off objects
close by. The arrival of multipath components follow a Poisson distribution with
interarrival times corresponding to an exponential distribution [50].
3.8.3
Ghassemzadeh path loss model
The Ghassemzadeh model is a simpler and faster stochastic path loss model for indoor
environments. It presents different path loss model parameters of UWB signals with a
nominal frequency of 5 GHz. The two main parameters used for characterization are the
path loss exponent and shadow fading standard deviation. These differ from location to
location [51].
3.8.4
IEEE 802.15.4a path loss models
A. F. Molisch et al [52] presents path loss models for IEEE 802.15.4a specified by its
working group. Different CM models are specified based on the Saleh-Valenzuela path
loss model. CM1 defines a Residential LOS model, whereas CM2 can be used to model
Residential non-line-of-sight (NLOS) scenarios. C3 is for Indoor Office LOS, CM5 for
Outdoor LOS, CM6 for Outdoor NLOS and CM7 for Open Outdoor NLOS.
3.9
UWB TRANSCEIVER
A brief look at typical transceiver design is provided next. For a comprehensive discussion
refer to the excellent work done by Aaron Orndorff [53].
3.9.1
UWB Transmitter
The transmitter has the task of converting a binary data stream into symbols and to map
these symbols to analog waveforms. Using an antenna, the transmitter then has to transmit
these signals through the channel while adhering to all required regulations.
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A simple UWB transmitter block diagram is shown in Figure 3-16.
Figure 3-16: Simple UWB transmitter block diagram
3.9.2
UWB Receiver
The receiver has the task of amplifying and converting the received electromagnetic energy
from its antenna to reconstruct a transmitted pulse shape. The receiver then maps these
analog pulse shapes to the appropriate symbols and then converts those symbols into a
binary bit stream representing the data.
An example of a simple energy detection receiver is shown in Figure 3-17.
Figure 3-17: Simple UWB receiver block diagram
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Forward Error Correction (FEC)
Error detection and correction plays an important role in receiver design. Redundant data
gets added to the transmitted data at the sender and this extra data allows the receiver to
detect and correct bit errors.
Error correction codes fall into two categories:
•
Block codes: Performed on fixed-size blocks of bits
•
Convolutional codes: Performed on bit streams of arbitrary length
This research study will go into great detail to compare the effects of forward error
correction in the IEEE 802.15.4a UWB physical layer.
3.10 UWB ANTENNAS
Conventional antennas are not suited for UWB systems because they were designed to
radiate over the narrow range of frequencies used in narrowband communication systems.
The following is a list of antennas considered for UWB systems:
•
Monopole antenna
•
Dipole antenna
•
Conical antenna
•
D-dot antenna
•
Folded horn antenna
•
TEM (Transient Electromagnetic) horn antenna
•
IRA (Impulse Radiating Antenna)
It is desirable for a UWB antenna to be small, be embedded as part of the transceiver
circuitry and be portable. An omnidirectional antenna, radiating power uniformly in all
directions, is therefore implied. Furthermore, because of the wide range of frequencies a
nonresonant antenna seems appropriate but the size of nonresonant antennas makes it
unsuitable for mobile applications.
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3.11 POSITIONING AND RANGING
There are many estimation techniques that makes use of radio signals to determine position
and range. A non-exhaustive list of these techniques is:
•
Received Signal Strength Indicator (RSSI)
•
Time Of Arrival (TOA)
•
Angle Of Arrival (AOA)
•
Multilateration by computing the Time Difference Of Arrival (TDOA)
Although a major feature of UWB, this paper will not go into the details of ultra wideband
positioning and ranging. The reader is referred to [44] and [49] for more information.
3.12 TYPICAL UWB APPLICATION AREAS
At the time of this writing, UWB technology is starting to move from prototype to
commercial applications. The list of application areas for UWB will certainly grow in the
future. A few areas are identified:
•
Asset location
•
Home networking
•
Body area networks
•
Sensor networks
•
Video and audio distribution
•
A great deal of suggestions exists for the medical field focusing mainly on imaging
and monitoring applications of which there are a myriad of useful propositions.
3.13 UWB DISADVANTAGES
UWB might sound like a too-good-to-be-true technology, but it is not without certain
disadvantages.
Disadvantages of UWB:
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•
Overview of Ultra Wideband
Requires complex and very accurate timing components making receiver design
difficult
•
Only short range, due to strict limits on transmit energy by regulatory bodies
•
Complex signal demodulation required
•
Antenna design still a huge challenge
•
Material penetration causes high losses
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Chapter 4
4.Overview of IEEE 802.15.4a
A comprehensive study into the IEEE 802.15.4a standard is offered in this chapter.
The IEEE 802.15.4a standard [54] is an amendment to the 2006 revision of the IEEE
802.15.4 standard [55] (original IEEE 802.15.4 standard published in October 2003).
Therefore, the body of work represented by this chapter also includes a study of the IEEE
802.15.4-2006 standard.
4.1
4.1.1
INTRODUCING THE PARTS
IEEE 802.15.4
IEEE 802.15.4 specifies the physical (PHY) and medium access control (MAC) layers of a
low-rate Wireless Personal Area Network (WPAN). It targets applications for wireless
devices requiring low data rates and low power consumption.
4.1.2
Zigbee
Zigbee is an emerging standard from the Zigbee Alliance [56] that is easily confused with
IEEE 802.15.4. Zigbee is based on the IEEE 802.15.4 standard but provides a complete
protocol stack that can be used for low cost, low power wireless personal area network
applications. It adds a network layer with additional capabilities allowing for selforganizing mesh networks to be employed with features such as high security, multicasting, and many-to-many routing.
4.1.3
IEEE 802.15.4a
IEEE 802.15.4a is the result of an amendment project to IEEE 802.15.4 for an alternative
PHY, successfully completed by the IEEE 802.15 Low Rate Alternative PHY Task Group
(TG4a) for Wireless Personal Area Networks (WPANs).
The focus of the committee was to draft an alternate PHY specification that provides
communications with high precision ranging capability, high aggregate throughput, ultra-
Chapter 4
Overview of IEEE 802.15.4a
low transmit power, scalable data rates. In addition, the alternate PHY should provide for
longer range, consume less power, and should be inexpensive.
On 22 March 2007 a baseline consisting of two optional PHYs was approved by the IEEESA Standards Board as a new amendment to IEEE 802.15.4-2006. The two optional PHYs
consisted of a UWB Impulse Radio (IR) operating in the unlicensed UWB spectrum, and a
Chirp Spread Spectrum (CSS) operating in the unlicensed 2.4GHz spectrum. This paper
focuses exclusively on the UWB IR physical layer.
Note: IEEE 802.15.4 standard and IEEE 802.15.4a standard will be referred to as
802.15.4 and 802.15.4a, respectively, hereinafter.
4.2
GENERAL WPAN DESCRIPTION
4.2.1
Node types
Two types of devices are defined by the standard:
•
Full Function Device (FFD): It is a device that supports all network functionalities.
A FFD can have one of three roles:
o PAN Coordinator: A device with the responsibility to set up, manage and
maintain the PAN.
o Coordinator: A device linking RFDs to the PAN coordinator.
o Device: A normal device with dedicated tasks.
A FFD can talk to both FFDs and RFDs.
•
Reduced Function Device (RFD): It is a device that supports only a reduced set of
network functionalities and is intended for applications that are extremely simple
like a typical sensor node. A RFD can only talk to a FFD.
4.2.2
Topology
A WPAN requires at least one FFD to ensure there is a PAN coordinator. The FFD and
RFD devices in a PAN organize themselves by talking to the PAN coordinator responsible
with setting up and maintaining the PAN. To join the PAN a device needs to send an
associate request to a coordinator.
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All 802.15.4/4a devices have unique 64-bit addresses or alternatively a short 16-bit address
can be allocated to a device by the PAN coordinator upon association. To enable
communications across different PANs, each PAN has a unique identifier.
The standard defines two topologies a WPAN may operate in:
•
Star topology: All devices can only communicate with the PAN coordinator.
•
Peer-to-peer topology: Each FFD can communicate with any other FFD or RFD in
its radio sphere of influence. A RFD can only communicate with a coordinator due
to its limitations.
Figure 4-1 illustrates these two topologies and also a derivative of a peer-to-peer topology
known as a cluster tree or mesh topology where small clusters of networks communicate
peer-to-peer.
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Figure 4-1: Examples of star, peer-to-peer and cluster topologies
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4.2.3
Overview of IEEE 802.15.4a
Architecture
The 802.15.4/4a architecture is based on the layered approach followed for the open
systems interconnection (ISO) seven-layer model. Each layer has its own set of
responsibilities and offers its services to the next higher layer.
The standard defines a low-rate wireless personal area network (LR-WPAN) device’s
architecture as shown in Figure 4-2.
Physical (PHY) layer
The PHY specifies the radio frequency (RF) transceiver and low-level control mechanisms.
The PHY provides two services, accessed through service access points (SAPs):
•
PHY data service, accessed through the PHY data SAP (PD-SAP)
•
PHY management service, accessed through the physical layer management entity
SAP (PLME-SAP)
The PHY layer specification is discussed in section 0.
Figure 4-2: LR-WPAN device architecture
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Medium Access Control (MAC) layer
The MAC provides access to the physical channel. The MAC provides two services,
accessed through service access points (SAPs):
MAC data service, accessed through the MAC common part sublayer (MCPS) data
•
SAP (MCPS-SAP)
MAC management service, accessed through the MAC sublayer management entity
•
SAP (MLME-SAP)
SSCS
The service-specific convergence sublayer (SSCS) provides an interface through which the
LLC can access the MAC sublayer.
802.2 LLC
The IEEE 802.2 Type I logical link control (LLC) layer presents a uniform interface to the
upper layers, usually the network layer.
Upper layers
Not defined by the 802.15.4/4a standard. Basically a network layer responsible for network
configuration and message routing tasks, and an application layer providing the functions
for a device to fulfill its purpose.
4.3
FUNCTIONAL OVERVIEW
4.3.1
Medium access strategies
In a LR-WPAN access to the medium is based on a combination of random access and
scheduled access [57]. Medium access is controlled by the PAN coordinator that may
choose between two different modes of operation:
•
Beacon-enabled: The PAN coordinator broadcasts a periodic beacon frame
containing PAN information and providing synchronization. This mode is only
used in star topology.
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•
Overview of IEEE 802.15.4a
Nonbeacon-enabled: The PAN coordinator makes exclusive use of random access
for medium sharing because it does not transmit any beacon frames and as such no
synchronization is provided. This mode is usually used for peer-to-peer topologies
but can be adopted in a star topology network as well.
4.3.2
Superframe structure
The coordinator of a star topology network can choose to operate in the beacon-enabled
mode. If so, the coordinator periodically sends beacons with the period between two
consecutive beacons defining a specific fixed length superframe structure. The superframe
structure is displayed in Figure 4-3 and serves to organize channel access and data
transmission.
Figure 4-3: Superframe structure between beacons
The coordinator defines the format of the superframe. A superframe can have an active
period and an inactive period bounded by beacons. Beacons get transmitted in the first slot
of each superframe.
Active Period
The active period is divided into 16 equally spaced slots with a duration known as the
superframe duration (SD) and consists of three parts: a beacon, a contention access period
(CAP) and a contention-free period (CFP).
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The CAP starts immediately following the beacon and is the time duration in symbols
during which devices contend with each other to access the channel and transmit data. The
contention-free period
The CFP is partitioned into a number of (maximum of seven) guaranteed time slots
(GTSs). A GTS slot may span over more than one superframe slot. A GTS is a period of
time during which certain low-latency application devices are given exclusive rights over
the channel and can directly start transmitting data without having to content with other
devices. The CFP starts immediately after the CAP.
Inactive Period
The inactive period is an interval during which the coordinator may enter a low-power
sleep mode. No PAN interaction takes place so all other devices can also switch of their
transceivers and sleep for the duration of the inactive period. The inactive period can be
zero length.
4.3.3
Interval and duration calculations
Devices in the network compete with each other to get a chance to transmit data. In order
to compete, devices need to know when the CAP starts and for how long it is available or
when a GTS for a specific device starts. This information is provided to each device by the
superframe structure. The length of the active and inactive period as well as the length of a
single time slot can be configured. Basically, the structure of a superframe is determined
by two parameters:
•
Superframe Order (SO): Variable that determines the superframe duration (SD).
•
Beacon Order (BO): Variable that determines the beacon interval (BI). The time
duration between two successive beacons is known as the BI and is given in
symbols.
The BI can be calculated as follows:
k = llm8'nW8oolp8(nol
qT. 2rs
(4-1)
qo 0 ≤ u ≤ 14
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where llm8'nW8oolp8(nol
qTis defined as the number of symbols in a superframe
when SO = 0. llm8'nW8oolp8(nol
qT is calculated as follows:
llm8'nW8oolp8(nol
qT
(4-2)
= llm8'vq(nol
qT. l_np'nW8oolp8'vqm
where llm8'vq(nol
qTis the number of symbols in a superframe slot when 'u = 0
and l_np'nW8oolp8'vqm is the number of equally spaced slots in the active period of
a superframe. The superframe slot duration is given by
mvq(nol
qT = 2ws . llm8'vq(nol
qT
(4-3)
The superframe duration (SD), given in symbols, is calculated as follows:
'( = llm8'nW8oolp8(nol
qT. 2ws
qo 0 ≤ 'u ≤ u ≤ 14
(4-4)
The standard defines llm8'vq(nol
qT = 60 and l_np'nW8oolp8'vqm = 16
which gives us llm8'nW8oolp8(nol
qT = 6016 = 960 symbols, according to
equation 4-2.
The relationship between the SD and the BI can be given by
1 ∶ 2rs9ws
(4-5)
Using llm8'nW8oolp8(nol
qT = 960 symbols this exponential relationship is
illustrated in Figure 4-4.
A value of 15 for BO indicates that the coordinator does not transmit any beacon frames
except when requested to do so through a beacon request command. In such a case the
value of SO is ignored because the superframe does not exist. In addition, GTSs is not
allowed.
Therefore, we can specify the possible values for BO and SO as shown in Table 4-1.
Table 4-1: Permitted values for BO and SO
Beacon-enabled
Nonbeacon-enabled
BO
SO
0 ≤ u ≤ 14
0 ≤ 'u ≤ u
u = 15
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Superframe for SO=12 and BO=13
Active
Inactive
0
2.5
5
7.5
10
12.5
15 Millions
Number of symbols
Superframe for SO=2 and BO=4
Active
Inactive
0
50
100
150
200
250
Number of symbols
Superframe for SO=7 and BO=10
Active
Inactive
0
50
100
150 x 10000
Number of symbols
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Superframe for SO=10 and BO=14
Active
Inactive
0
2.5
5
7.5
10
12.5
15 Millions
Number of symbols
Figure 4-4: Illustrations showing the relationship between SO and BO
4.3.4
Data transfer
If a PAN requires synchronization or support for low-latency devices the PAN is setup to
use beacons. If there is no need for synchronization or low-latency device support, the
PAN can be setup not to make use of beacons for normal transfers. Beacons will still be
required for the network discovery phase.
Three types of data transfer exist for LR-WPANs with different mechanisms for beaconenabled and nonbeacon-enabled networks. Data can be transferred to the coordinator, from
the coordinator or between two peer devices. Let’s take a closer look at each of these data
transfer types.
To coordinator
Beacon-enabled PAN
The device listens for a network beacon and synchronizes to the superframe structure when
a beacon is received. To transmit data, the device uses a slotted Carrier Sensing Multiple
Access with Collision Avoidance (CSMA-CA) scheme to contend for channel access. The
coordinator may reply with an optional acknowledgement frame if the data was received
successfully.
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Nonbeacon-enabled PAN
The device simply starts to transmit its data using unslotted CSMA-CA to the coordinator.
The coordinator may reply with an optional acknowledgement frame if the data was
received successfully.
From coordinator
Beacon-enabled PAN
If coordinator has data pending for a device, it announces so in the network beacon.
Because the device periodically listens to the beacon it will be able to determine if a
pending message is intended for itself. If so, the device transmits a MAC command request
frame, using slotted CSMA-CA, to notify the coordinator that it is ready to receive the
message. The coordinator will acknowledge the data request command and transmit the
data to the device using slotted CSMA-CA. The device may reply with an optional
acknowledgement frame if the data was received successfully. The coordinator will
remove the successfully transmitted message from its list of pending messages to ensure it
is not advertised in the next beacon.
Nonbeacon-enabled PAN
If coordinator has data pending for a device, it stores the data and waits for the device to
make contact and request the data. A device may request the data through a MAC
command request frame using unslotted CSMA-CA. The coordinator will acknowledge the
data request command and if it has data pending for the device, the coordinator will
transmit the data to the device using unslotted CSMA-CA. It the coordinator does not have
any data pending for the device it will indicate so. The device may reply with an optional
acknowledgement frame if the data was received successfully.
Between two peer devices
Communication between peer devices is not applicable to star topology networks because
with a star network communication is only allowed between device and coordinator.
In peer-to-peer communications, all FFDs can communicate directly with each other if
they are in range. To do this, the devices will have to receive and listens constantly to the
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channel and transmit its data using unslotted CSMA-CA. Alternatively, the devices will
have to synchronize with each other through certain measures. The 802.15.4/4a standard
does not specify any methods or techniques for peer device synchronization because it is
beyond the scope of the standard. This is where the Zigbee Alliance with their Zigbee
protocol stack with mesh networking support comes in. Refer to [58] for the Zigbee
network layer standard which provides detailed information on how to accomplish peer-topeer communications between devices.
4.3.5
Frame structure
The standard defines four frame structures to keep things simple.
•
Beacon frame
•
Data frame
•
Acknowledgement frame
•
MAC command frame
Beacon frame
Used by a coordinator for beacon transmissions in a beacon-enabled PAN. It originates
from the MAC sublayer. The structure of a beacon frame is shown in Figure 4-5.
MAC beacon frame
This is the MAC Protocol Data Unit (MPDU) of the MAC sublayer. It consists of a MAC
header (MHR), MAC payload and MAC footer (MFR). The MAC payload contains all
superframe specific information related to the GTSs, pending data and beacon payload.
The MFR is a 16-bit frame check sequence (FCS).
PHY packet
The MAC beacon frame (i.e. MPDU) is passed to the PHY layer as the PHY service data
unit (PSDU). The PSDU becomes the payload of the PHY packet (refer to Addendum
8.2.7B for a description of service and protocol data units). The physical protocol data unit
(PPDU) consists of the PSDU, PHY header (PHR) and synchronization header (SHR). The
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SHR is made up of the Preamble Sequence and Start-of-Frame Delimiter (SFD) fields used
to provide symbol synchronization at the receiver.
Figure 4-5: Structure of a MAC beacon frame and PHY packet
Data frame
Used for all data transfers and originates from the upper layers. The data payload is passed
from the upper layers to the MAC as the MAC service data unit (MSDU). The structure of
a data frame is shown in Figure 4-5. It is similar to the beacon frame except for some
differences in the fields that make up the MHR and the MAC payload is a data payload.
Acknowledgement frame
Used to confirm the successful reception of frames and originates from within the MAC
sublayer. The acknowledgement frame does not have a MAC payload. The structure of an
acknowledgement frame is shown in Figure 4-5. It is similar to the other frame structures
except for the missing MAC payload and some differences in the fields that make up the
MHR.
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MAC command frame
Used to handle all MAC peer entity control transfers and originates from within the MAC
sublayer. The structure of a MAC command frame is shown in Figure 4-5. It is similar to
the beacon and data frame structures except some differences in the fields that make up the
MHR and MAC payload. The MAC payload of a MAC command frame contains the
Command Type field and the command payload.
4.3.6
Delivery mechanisms
The standard employs various mechanisms to ensure the highest probability of successfully
delivering data to its destination.
Data verification
As mentioned, the MFR consists of a FCS used to detect bit errors. The FCS is calculated
over the MHR and MAC payload in the frame. The FCS is a 16-bit International
Telecommunication Union – Telecommunication Standardization Sector (ITU-T) cyclic
redundancy check (CRC). The FCS is calculated using a standard generator polynomial of
degree 16 given by
3b| 4 = 4b| + 4b + 4 } + 1
(4-6)
Frame acknowledgement
An optional acknowledgment can be sent to confirm the successful reception and
validation of a data or MAC command frame. If an acknowledgement is requested, the
process is simple with the originator of the message waiting for some period of time for the
acknowledgement after it has sent the message. If an acknowledgement is not received
after the wait time, the message is sent again. This process repeats for a configured number
of times after which the originator will terminate the transaction. If an acknowledgement is
not requested, the originator assumes the message was transmitted successfully.
CSMA-CA mechanism
802.15.4 specifies two types of CSMA-CA for channel access depending on the network
configuration:
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•
Slotted CSMA-CA, used in beacon-enabled PANs
•
Unslotted CSMA-CA, used in nonbeacon-enabled PANs
Overview of IEEE 802.15.4a
Carrier Sense Multiple Access (CSMA) is a protocol in which a node firsts checks if the
communication channel is idle before transmitting. The node listens for a carrier wave to
determine this idle state of the channel.
CSMA-CD (Collision Detect) improves the CSMA scheme by breaking transmission if a
node detects another signal while it is busy transmitting.
CSMA-CA (Collision Avoidance) improves the CSMA scheme by first checking if no
communication is taking place on the channel for a predetermined period of time before
declaring the channel as idle. Furthermore the node wishing to transmit sends a backoff
message to all other nodes telling them not to transmit before it starts using the channel.
The details of CSMA-CA and its slotted and unslotted approaches are not discussed in this
document.
ALOHA mechanism
802.15.4a specifies the ALOHA method as an alternative channel access strategy. The
ALOHA method, developed at the University of Hawaii, defines a very simple
communication scheme. Any device in the network that has data to transmit will send the
data immediately. If the data was delivered successfully to the destination device, the next
data packet is sent. If the data was not delivered successfully to the destination device, the
same data packet gets sent again.
Obviously the setback with the ALOHA method is collision problems. If two devices
transmit at the same time the transmitted frames collide with each other and the data of
both have to be resent. The number of collisions increases when the volume of
communications increases.
Two types of ALOHA are defined:
•
Pure ALOHA
•
Slotted ALOHA
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Pure ALOHA is the approach discussed above. Slotted ALOHA try to optimize network
efficiency by minimizing the number of collisions. The system employs beacon signals
sent at precise intervals with the effect of splitting time into slots. A device can only
transmit on the beginning of a timeslot, thus reducing the chance of collisions.
If the arrival of packet follows a Poisson distribution pure ALOHA has a maximum
throughput of about 18.4%. This is a loss of about 81.6% of the total available bandwidth.
Optimizations resulting from the slotted ALOHA approach, increased the maximum
throughput to 36.8%.
Di Benedetto et al [59] demonstrated that for light and medium network traffic loads the
ALOHA approach offers satisfactory throughput in UWB networks due to the robustness
UWB technology provides against multi-user interference. Packet loss due to collisions is
also reduced if a time hopping scheme is adopted which will introduce a different delay
one each burst in a packet.
4.3.7
Ranging
This paper will not go into the details of 802.15.4a ranging. The reader is referred to pp. 8 20 of [54] for a detailed ranging overview.
4.4
ULTRA WIDEBAND PHYSICAL SPECIFICATION
We will now take a detailed look into the workings of the ultra wideband alternate physical
layer as specified in the IEEE 802.15.4a standard.
The Direct Sequence UWB approach was chosen for the standard due to its spectral
efficiency, robustness at low transmit powers and support for high-precision ranging.
UWB PHY waveforms employ an impulse radio scheme.
4.4.1
Channels and operating frequency bands
The standards specify three independent frequency bands and a total of 16 channels (or 32
complex channels). The bands, channels and their center frequencies are listed in
Table 4-2 with a pictorial representation provided in Figure 4-6.
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Table 4-2: 802.15.4a UWB operating frequency bands and channel information
Band
Channel Number Center Frequency
Bandwidth
0 (Sub-GHz band)
0
499.2 MHz
499.2 MHz
1 (Low band)
1
3494.4 MHz
499.2 MHz
2
3993.6 MHz
499.2 MHz
3
4492.8 MHz
499.2 MHz
4
3993.6 MHz
1331.2 MHz
5
6489.6 MHz
499.2 MHz
6
6988.8 MHz
499.2 MHz
7
6489.6 MHz
1081.6 MHz
8
7488.0 MHz
499.2 MHz
9
7987.2 MHz
499.2 MHz
10
8486.4 MHz
499.2 MHz
11
7987.2 MHz
1331.2 MHz
12
8985.6 MHz
499.2 MHz
13
9484.8 MHz
499.2 MHz
14
9984.0 MHz
499.2 MHz
15
9484.8 MHz
1354.97 MHz
2 (High band)
A single mandatory channel is specified for each band, one of which a compliant device
must implement to adhere to the standard. The rows marked bold and italic in
Table 4-2 indicate these mandatory channels.
Inside each channel there is support for two complex channels. A complex channel has a
unique 31 bit preamble code used to construct the synchronization header part of a UWB
PHY frame. The channel number together with the preamble code makes up a complex
channel. A compliant device must support both complex channels of each channel it
implements.
Channels 4, 7, 11 and 15 are optional channels and have a bandwidth > 500 MHz. To meet
regulatory PSD constraints the larger bandwidth allows for transmission at higher power
resulting in a longer communication distances and more accurate ranging.
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Figure 4-6: Graphical view of 802.15.4a UWB operating frequency bands and channels
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4.4.2
Overview of IEEE 802.15.4a
Signal flow
The typical steps involved to modulate and create an UWB PHY frame at the transmitter
and then detect and demodulate the frame at the receiver are illustrated in Figure 4-7.
Figure 4-7: Typical 802.15.4a UWB PHY frame processing at transmitter and
receiver
The reasons behind and the functions performed by the different steps in the illustration
will become clear during the discussions in the next sections.
4.4.3
UWB frame format
Figure 4-8 shows the format of an 802.15.4a UWB frame. It consists of three parts:
1. Synchronization Header (SHR)
a. Synchronizations (SYNC) portion
b. Start Of Frame Delimiter (SFD) portion
2. Physical Header (PHR)
3. Payload or Data part
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Figure 4-8: 802.15.4a UWB PHY frame format
Various UWB pulse trains lengths are used to obtain various transmit data rates for the
different frame parts. The data rates for each part can be found in Table 4-3.
Table 4-3: Different data rates for 802.15.4a UWB PHY frame parts
Part
Data rate(s)
SHR 1.
Msymbols/s or
0.25 Msymbols/s depending on the preamble PRF
PHR If data rate is above 850kb/s then
850kbps, else 110kb/s
Data At the information data rate defined by the various
UWB symbol timing parameters
4.4.4
UWB symbol structure
The modulation scheme adopted is a combination of Burst Position Modulation (BPM) and
Binary Phase-Shift Keying (BPSK). BPSK was discussed in section 3.6.4. BPM makes use
of the UWB PHY symbol structure to encode information. The 802.15.4a UWB PHY
symbol structure is shown in Figure 4-9. Notice how the symbol contains two separate
intervals in which a burst of UWB pulses can occur. Only one of these burst intervals in a
symbol are allowed to contain a burst at a time and therefore the position of the interval
containing the burst can be used to carry information.
As such, a single UWB PHY symbol can carry two bits of information at a time. The burst
position determines one bit and the other bit gets modulated in the phase of that same
burst.
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UWB Symbol Structure
BPM Interval 1
st
1 possible burst position
BPM Interval 2
Guard interval
2
nd
possible burst position
Guard interval
UWB symbol duration
2ns
Number of chips per burst
Figure 4-9: Structure of a 802.15.4a UWB PHY symbol
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UWB symbol timing parameters
The UWB symbol duration (\^~gV ) is defined as the total number of chip positions in the
symbol (_% ) multiplied by the duration of one chip interval (\% ≈ 2ns) – see equation 3-16.
For BPM modulation \^~gV is divided by 2 to get the duration of a single BPM interval
(\rd€ ). Only the first half each \rd€ may contain a burst. The second half is used as a
guard interval to limit inter-symbol interference (ISI).
A burst is a grouping of consecutive UWB pulses (chips) and has a duration \fX~) defined
as the number of chips in a burst (_%a ) multiplied by \% . The phase of the burst is
modulated with the second bit of information. The total number of intervals in a symbol
that may host a burst is given by _fX~) and is defined as the duration of the UWB PHY
symbol (\^~gV ) divided by the duration of a burst (\fX~) ).
Note that \fX~) << \rd€ which allows for the time hopping scheme to provide some multi-
user access. For every symbol the position of a burst is varied according to a predefined
time hopping code.
The calculations for the timing parameters introduced in this section are given by equations
4-7 to 4-10.
\^~gV = _% . \%
= _fX~) . \fX~)
\rd€ =
\^~gV
2
\fX~) = _%a . \%
_fX~) =
4.4.5
\^~gV
\fX~)
(4-7)
(4-8)
(4-9)
(4-10)
UWB PHY rate and timing parameters
Several data rates are defined for each channel. By keeping _fX~) constant and varying
_%a the duration of the symbol (\^~gV ) will change resulting in different data rates. In
addition to the UWB symbol timing parameters, the data rate is also affected by other
parameters as presented in Table 4-4.
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Table 4-4: 802.15.4a UWB PHY rate and timing parameters
Peak PRF
For each UWB channel the peak PRF must be 499.2 MHz.
The peak PRF defines the chip duration:
\% =
&/
1
≈ 2Tm
ajY‚
Mean PRF
The average PRF during the data part of the UWB PHY frame.
&/
VjYS
=
_%a
\^~gV
Preamble code length
Together with the channel number the preamble defines a complex channel.
Preamble code is used during the SHR part of the UWB PHY frame.
Preamble code length can be 31 bits or 127 bits.
Viterbi Rate
Indicates the effect of the convolutional encoding on the data rate.
RS Rate
Indicates the effect of the Reed-Solomon encoding on the data rate.
FEC Rate
Indicates the total effect of the Forward Error Correction (FEC) applied.
Symbol Rate
+XY)j = ƒ
8o„
XY)j . /'XY)j
The inverse of the duration of the UWB symbol on the air.
'…p /l8 =
1
\^~gV
Bit Rate
The information throughput rate including the effects of FEC.
/l8 =
2. +XY)j
\^~gV
A complete list of rate-dependent parameters and timing-related parameters for the PSDU
are given in Table 39a of the 802.15.4a specification [54].
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Peak PRF vs Mean PRF
In the data part of the UWB PHY frame, the peak and mean PRFs are different because of
the pulse bursts in the UWB symbols. In the SHR preamble part they are the same because
pulses are distributed uniformly throughout the preamble symbol.
4.4.6
SHR preamble
The SHR preamble aids the receiver with frame detection and synchronization. The SHR
preamble is made up of a SYNC portion and SFD portion.
Table 4-5 introduces the preamble parameters. For the full list with the possible values of
each the reader is referred to Table 39c of the 802.15.4a specification [54].
Table 4-5: 802.15.4a UWB SHR preamble parameters
Mean PRF
The SHR preamble is sent at a slightly higher mean PRF than the data.
The &/
VjYS has
-
4.03 MHz
-
16.10 MHz
-
62.89 MHz
the following three values:
Delta length (†‡ )
The number of chip durations to insert between preamble code symbols.
ˆ‰/Š‹Œ
The number of chips per preamble symbol.
Defined as the preamble code length multiplied by the delta length.
where 4 = 31 or 127.
_%/a~gV = $ . 4
ŽŠ‹Œ
The duration of a single preamble symbol.
ˆ‹Œ‰
The number of preamble symbols in the SYNC portion of the SHR preamble.
The standard defines four _~gS% sizes.
-
Short = 16
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-
Default = 64
-
Medium = 1024
-
Long = 4096
ˆ‹‘
The number of preamble symbols in the SFD portion of the SHR preamble.
Ž‹Œ‰
The duration of the SYNC portion.
Ž‹‘
The duration of the SFD portion.
ŽŠ’“
The duration of the whole SHR preamble.
SHR SYNC
The SYNC portion provides for packet synchronization and channel estimation. It is also
employed for the ranging functionality incorporated in the standard.
Each channel has two unique 31 length preamble codes and extra optional 127 length
preamble codes are also specified. The combination of channel number and preamble code
is known as a complex channel. Preamble codes consist of the code symbols from the
ternary alphabet {-1, 0, 1}. They were chosen such that the codes used in the same channel
have the lowest cross-correlation.
The SYNC portion is constructed by repeating the preamble symbol a certain number of
times. The repeat number is defined by the value of the parameter _~gS% .
The preamble symbol is built up by taking the preamble code and inserting a specific
number of chip durations between the ternary symbols of the preamble code. The number
is defined by the value of the delta length parameter $ . The construction of the preamble
symbol is defined by the Kronecker operation ⊗ as shown in equation 4-11. The concept
is illustrated with the example provided in Figure 4-10. Note that the values used in the
example are not supported by the standard but are for explanation purposes only.
Spreading is accomplished by adding (L-1) zeros after every ternary preamble code
symbol.
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'Z = Z ⊗ $ T
1 ,T = 0
•
ℎ $ = —
0 , T = 1, 2 … , c − 1
(4-11)
Figure 4-10: Example illustrating SHR preamble symbol construction
The construction of the SYNC portion by making use of the preamble symbol is shown in
Figure 4-11.
Figure 4-11: SHR preamble structure – SYNC portion
SHR SFD
The SFD acts as the boundary between the synchronization part and rest of the UWB PHY
frame and is used to establish frame timing.
Two ternary SFD codes are defined - one of length 64 for the nominal data rate of 110
kbps and one of length 8 for the other data rates. The SFD code is spread by the preamble
symbol to make up the SFD part of the UWB PHY frame (see Figure 4-12).
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Figure 4-12: SHR preamble structure – SFD portion
A detailed description of the construction of the SYNC and SFD portions is provided in
section 6.2.8.
4.4.7
PHR
The physical header (PHR) follows the SHR preamble. It is used to help the receiver
successfully decode the packet. The PHR is 19 bits in size and it contains the following
information:
•
Data rate
•
Frame length (number of bytes in PSDU from MAC)
•
Ranging indicator
•
Header extension
•
SHR preamble SYNC portion duration
•
SECDED parity check bits
SECDED bits
To protect the PHR information bits from errors a simple Hamming error detection and
correction block code, consisting of 6 bits, is used. SECDED is the abbreviation for singleerror correct, double-error detect.
4.4.8
Data
The data field is encoded as follows:
1. A systematic Reed-Solomon block code encodes the data.
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2. The encoded Reed-Solomon block code is then encoded with a systematic
convolutional encoder.
3. The encoded convolutional bits are then spread and modulated using BPM-BPSK
modulation.
Reed-Solomon encoding
The Reed-Solomon (R-S) encoder appends 48 parity bits for every 330 bit blocks of data.
For blocks of data with less than 330 bits, dummy zero bits are added. The code is defined
as an R-S (63, 55) code.
Note that the R-S code is systematic and therefore decoding is optional. The original data
can be obtained by just removing the R-S parity bits.
Convolutional encoding
Unlike the R-S coder working with data blocks, convolutional codes work with data bit
streams.
The specified convolutional encoder makes use of the two generator polynomials given in
equation 4-12 which were chosen to be noncatastrophic and systematic.
?, = 010
?b = 101
(4-12)
The convolutional encoder has a rate of R = 1⁄2, signifying that for each input bit it will
produce two output bits. Sequence ?, is known as the position bits and they encode the
position of a burst while sequence ?b is known as the sign bits and encode the polarity of
the pulses in a burst.
Note that a non-coherent receiver is not able to decode the polarity of a burst and as such it
will not be able to make use of the sign bits for decoding. In such a case the receiver can
comfortably ignore the sign bits because the convolutional code is systematic. The original
data will still be intact in the position bit sequence.
Decoding
The 802.15.4a standard does not specify any details about decoding at the receiver.
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If a receiver does not support forward error correction at all, it can simply ignore the code
bits because both the RS-code and convolutional code is systematic.
The mathematical properties of the generator polynomials defined by the Reed-Solomon
encoding algorithms allows for the R-S decoder to detect and correct a limited amount of
errors.
To decode the convolutional code at the receiver the most popular universal decoder is
based on maximum likelihood estimation and is known as the Viterbi decoder.
Spreading and Modulation
As mentioned, two information bits are encoded in one UWB PHY symbol. One into the
burst position and one into the polarity of the burst. To suppress the negative effects of
interference, time hopping and polarity scrambling schemes are employed.
Section 3.7.2 discussed how time hopping can be used to minimize multiple access
interference (MAI) caused by multiple users communicating at the same time. Only one
burst interval in a UWB PHY symbol will host an active burst. The remaining burst
intervals will be empty. The time hopping scheme determines the active burst interval, also
known as the hopping position, in the symbol.
The polarity scrambling aids with interference rejection but in addition provides the
randomness necessary to smooth the spectrum of the transmitted waveform – a side effect
of transmitting impulse trains (bursts). For a review, see the discussion on pulse trains in
section 3.4.1.
A single Pseudo Random Binary Sequence (PRBS) generator will be used to generate both
the spreader sequence and the burst hopping sequence.
The generator polynomial used by the PRBS generator is chosen such that it is a primitive
polynomial and maximum length sequence (MLS) and is given by equation 4-13.
?( = 1 + (bI + (b}
(4-13)
where ( is a delay of one chip interval (\% ≈ 2Tm).
An MLS is generated using a linear feedback shift register (LFSR) of which the Fibonacci
LFSR and Galois LFSR implementations are the most common. The 802.15.4a standard
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specifies a Fibonacci implementation clocked at the peak PRF of 499.2 MHz. The output
of this LFSR gives the scrambler output as
mS = mS9bI ⊕ mS9bI
•
ℎ T = 0,1,2 …
(4-14)
For symbol ›, the LFSR is clocked a number of times equal to the number of chips per
burst (_%a . The resulting output becomes the scrambling code for symbol ›.
To calculate the burst hopping position for symbol › the standard presents the following
three formulas for the three different mean PRFs:
œž“Ÿ = 3.90 MHz
ℎ‚ = m‚ ¡¢£ + 2mb¤‚ ¡¢£ + 4m¤‚ ¡¢£ + 8mU¤‚ ¡¢£ + 16mI¤‚ ¡¢£
(4-15)
œž“Ÿ = 15.60 MHz
ℎ‚ = m‚ ¡¢£ + 2mb¤‚ ¡¢£ + 4m¤‚ ¡¢£
(4-16)
œž“Ÿ = 62.4 MHz
ℎ‚ = m‚ ¡¢£
4.5
(4-17)
CURRENTLY AVAILABLE 802.15.4A HARDWARE
It was mentioned in the introduction that it is getting a little easier to get hold of UWB
hardware but the available UWB integrated circuits (ICs) out there is still very expensive.
We will briefly look at two ICs implementing the IEEE 802.15.4a standard for UWB
communications.
4.5.1
IMECs Digital UWB Transmitter IC
IMEC [60] is a world-leading independent research center in the fields of nanoelectronics,
bioelectronics and nanotechnology. Its research focuses on future generation chips and
state-of-the-art technologies for ambient intelligence.
The IMEC transmitter IC was designed according to the signal structure of the 802.15.4a
standard. To reduce startup time and save on energy, the IC is equipped with a phasealigned FLL (Frequency Locked Loop) instead of the traditional Phase Locked Loop
(PLL).
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4.5.2
Overview of IEEE 802.15.4a
TES IEEE 802.15.4a transceiver with ranging capability
TES Electronic Solutions specializes in custom electronic design and manufacturing
services [61].
The TES IC implements the complete LR-UWB functionality specified by the IEEE
802.15.4a standard.
Typical features of the IC solution include:
•
Highly integrated, low-power transceiver IP core
•
IEEE 802.15.4a conformant PHY
•
IEEE 802.15.4 MAC
•
Localization and tracking algorithms
•
Advanced Encryption Standard (AES) security engine
•
Embedded Flash/RAM and signal conditioning
•
Omni-directional monopole, small footprint dipole and directional UWB antennas
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5.The OMNET++ Simulation
Environment
In this chapter we will take a look at different network simulators for wireless networks.
After a comparison we will look at the reasons for choosing the OMNET++ simulation
environment and provide an introduction into the general concepts of OMNET++.
5.1
NETWORK SIMULATORS
5.1.1
Available network simulators
A wide variety of network simulators, all with different levels of complexity and flexibility
are available to set up network test scenarios and simulate the network behavior. Table 5-1
lists the most common network simulators as identified by Andrea Rizzoli [62].
Table 5-1: List of available network simlators
Simulator Name
Description
OMNET++
OMNeT++ is a component-based, modular and open-architecture
simulation environment with strong GUI support and an embeddable
simulation kernel. The simulator can be used for modeling:
communication protocols, computer networks and traffic modeling,
multi-processors and distributed systems, etc. OMNeT++ also supports
animation and interactive execution. OMNeT++ is freely distributed
under an academic public license [2].
NS-2
Network Simulator v2 (NS-2) is the second version of a discrete event
simulator targeted at networking research. NS provides substantial
support for simulation of TCP, routing, and multicast protocols over
wired and wireless (local and satellite) networks. NS is developed by
the Information Sciences Institute at the University of Southern
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The OMNET++ Simulation Environment
California (USC) school of engineering. NS-2 is free software covered
by the GNU GPLv2 license and is publicly available for research,
development, and use [1].
NS-3
NS-3 is the next major revision of the NS-2 simulator. NS-3 is
presently (April 2008) a work in progress, before even an initial alpha
release. The current source code is available for experimentation [63].
GloMoSim
A wireless and wired network simulator providing a scalable
simulation environment. GloMoSim is designed using parallel
discrete-event simulation capability provided by the Parsec C-based
simulation language. GloMoSim is only freely available to academic
institutions for research purposes. The commercial version of
GloMoSim is QualNet [64].
SWANS
Scalable Wireless Ad hoc Network Simulator (SWANS) is a scalable
wireless network simulator built atop the Java in Simulation Time
(JiST) platform. It was created primarily to provide for additional
research requirements existing network simulation tools do not cater
for. The SWANS is organized as independent software components
that can be composed to form complete wireless network or sensor
network configurations. Its capabilities are similar to NS-2 and
GloMoSim, but it is able to simulate much larger networks. SWANS
leverages the JiST design to achieve high simulation throughput, save
memory, and run standard Java network applications over simulated
networks. In addition, SWANS implements a data structure, called
hierarchical binning, for efficient computation of signal propagation
[65].
QualNet
A commercial network simulation suite, providing modeling software
that predicts performance of networks through simulation and
emulation. QualNet enable the deployment of a plethora of
applications in wireless, wired and mixed network platforms. QualNet
claim to have the fastest real-time traffic modeler and focuses on
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speed, accuracy, portability and extensibility to successfully deploy
virtual networks [66].
OPNET
A leading commercial network simulator, providing solutions for
managing network operations, network R&D, capacity management
and
application
performance
management.
OPNET
provides
customers the capabilities to design, deploy and manage network
infrastructure, the network equipment and network applications [67].
TOSSIM
A Discrete Event Simulation platform for TinyOS sensor networks.
No physical hardware motes running the TinyOS are necessary. It is
primarily targeted for the simulation of TinyOS applications and
therefore focuses on simulating the TinyOS environment rather than
the real world. Even so, TOSSIM is very useful in cause-effect
analysis of sensor networks. It allows a user to separate out the
environment noise to gain better understanding of the implemented
algorithms [68].
Although an excellent simulator, TOSSIM was not chosen because at
the time of this writing the OMNET++ community support and tool
availability far exceeds that of TOSSIM. No radio propagation models
are provided and one has to rely on external programs to accomplish
this. Furthermore, the learning curve is steeper because one has to
learn the TinyOS way of implementing simulation tasks.
The biggest benefit of the TOSSIM simulator is that the applications
written for it can be run on real modes without any modification. For
this reason it is considered as an excellent future work contribution for
the work presented in this paper.
5.1.2
OMNET++ vs NS-2
The two most popular network simulators, especially for research purposes, are
OMNET++ and NS-2 (Note: TOSSIM is also fast gaining acceptance but was not
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considered for the reasons mentioned earlier). In choosing a network simulator to use for
this project, the following features were considered important:
•
Ease of installation and multiple platform support
•
Programming model
•
Flexibility
•
Model management and structure
•
Existing UWB frameworks
•
Debugging ability
•
Documentation and support
•
Logging of results
•
Visualization
Table 5-2: OMNET++ vs NS-2 - Comparing important features
Ease of installation and multiple platform support
OMNET++
Supports both Windows and Linux platforms.
Windows installation: Very easy.
Linux installation: Moderately easy.
NS-2
Supports Windows (using Cygwin) and Linux platforms.
Windows\Cygwin installation: Not tested, but should be similar to
Linux installation.
Linux installation: Moderate to difficult. NS depends on several
externally available components that need to be installed.
Programming Model
OMNET++
Object-oriented, event-driven and written in C++. Topology
descriptions are either written in the NED language which is very
basic and easy to learn, or can be dynamically created in run-time.
The newly released version 4.0 of the simulation software boasts
with a very intuitive IDE providing a graphical interface for various
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tasks such as creating and editing topologies, configurations and
network descriptions.
NS-2
Mixed-mode programming. Use OTcl (Object-oriented Tcl)
scripting language with underlying C++ classes. OTcl is also used
for creating and configuring networks.
Flexibility
OMNET++
OMNeT++ is a flexible and generic simulation framework. The
basic active components of the model are called simple modules
and every simple module lives on its own and exchanges messages
with others to communicate. One can simulate anything that can be
mapped to active components that communicate by passing
messages.
NS-2
NS-2 has been designed as a (TCP/IP) network simulator, and it is
difficult to impossible to simulate things other than packetswitching networks and protocols with it. NS-2 concepts are highly
detailed and deeply hardcoded making it very difficult to do things
differently.
Model management and structure
OMNET++
Models in OMNeT++ are independent of the simulation kernel
because the OMNeT++ simulation kernel is a class library. Simple
modules are reusable and can be combined like blocks to create
compound modules and simulations.
A complex module can be assembled from these self-contained
building blocks which can be reused in different simulations. By
organizing modules in this way, complexity is dealt with in a
hierarchic fashion.
NS-2
In NS-2 the boundary between simulation core and models is
blurred, without a clear API. Installation involves a lot of patching.
Furthermore, in NS-2 all models are "flat" and therefore does not
allow for subnetworks, or complex protocols to be implemented as
a composition of several independent units.
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Existing UWB frameworks
OMNET++
OMNeT++ has a good variety of models for simulating computer
systems, queuing systems etc., but lags behind the ns-2 simulator on
availability of communication protocol models.
OMNET++ has the Mobility framework (recently superseded by
the Mixim framework) and the INET framework, both which
include support for mobile and wireless simulations. The Mixim
framework combines various wireless and mobile framework
features and provides for simulation up to signal level. The MiximUWB variation of the framework follows the 802.15.4a standard
and is an ideal candidate.
NS-2
Since NS-2 has been designed as a network protocol simulator, it
has a much richer set of communication protocol models than
OMNET++.
A model exist that implements an impulse radio UWB physical
layer
with
time-hopping,
pulse
position
modulation
and
convolutional channel codes. The model also supports a DCC-MAC
layer.
Debugging ability
OMNET++
Simple macro is provided to write debug messages to standard
output. Debugging can be enabled or disabled through easy
parameter changes. Watches can also be set on variables.
The Eclipse IDE included with version 4.0 of the software allows
the ability to perform step execution, to set breakpoints and inspect
variables and values while actively running a simulation.
NS-2
Have to consider debugging at both OTcl and C++ levels. Care has
to be taken to prevent memory leaks.
Documentation and support
OMNET++
A lot of documentation and online support available with tutorials
and demos to ease the learning curve.
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A lot of documentation and online support available but
documentation seems to be fragmented.
Logging of results
OMNET++
Allows for the capturing of time series data through output vectors
and single value statistics through scalar output files. Visualization
and analysis tools are also provided to interpret and graph the result
data.
NS-2
Available through OTcl and trace files with the network animator
(NAM) of NS. NAM is a Tcl/Tk based animation tool for viewing
network simulation traces and real world packet tracedata.
Visualization
OMNET++
Provide an interactive graphical user interface through TkEnv and a
command line interface through CmdEnv. TkEnv is a Tcl/Tk based
animation tool. OMNET++ allows individual modules to be
inspected and viewed while the simulation is running.
NS-2
Provide an interactive graphical user interface through NAM. Like
OMNET++ it supports topology layout, packet level animation, and
various data inspection tools.
Simulations are very useful in the pursuit of research, especially for cases where physical
hardware is not readily available or too expensive and time consuming to implement.
Additionally it provides for a flexible environment in which relatively easy
experimentation of various scenarios can be performed.
Even so, J.G. Page et al [69] states that “if a simulation is not accurately mimicking reality
then the results are meaningless”. Furthermore, modeling of the physical layer is essential
in WSN simulations because it affects the performance of the upper layers.
There are several reasons why the research community sometimes questions the credibility
of simulation results:
•
Covering too little detail. If the simulation model provides too little detail the
results will be inaccurate and impractical.
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Covering too much detail. If the simulation model provides too much detail the
•
performance of the simulation might suffer and the model will be difficult to reuse.
Difficult or near impossible to repeat results. Study has shown that most of the
•
existing research provides very little simulation model information and almost no
support documentation, making it hard for anyone to reproduce the results
published.
After considering the above comparison and taking into account the issues that surrounds
network simulations, OMNET++ version 4.0 was chosen for implementing the simulation
of the 802.15.4a standard. More information on the alternative NS-2 UWB MAC and PHY
simulator can be found at [3].
OMNET++ is much easier to work with than NS-2, provides a far better model architecture
with its hierarchical structure, and has a lot more potential in scope and features. The most
useful feature is the availability of an ultra wideband framework based on the IEEE
802.15.4a standard through the Mixim framework.
The Mixim framework together with OMNET++ version 4.0 addresses a lot of the
concerns associated with networks simulations. OMNET++ and the Mixim framework is
very easy to install, works on both the Windows and Linux platforms, includes proper
documentation backed up by an online community. Results can be repeated exactly by
using the same set of random generator seeds used in the original experiment runs. The
Mixim framework models the physical layer up to the signal level for the most accurate
results.
Next we will now look into the basic modeling concepts of discrete-event simulation and
introduce the rudiments of OMNET++.
5.2
5.2.1
SIMULATION MODELING CONCEPTS
Discrete Event Simulation
A discrete-event simulation is one in which the state of a model changes only at discrete,
but possibly random, instances in time.
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An event is an occurrence that changes the state of the model. In a DES events take zero
time to happen and it is assumed that no state changes in the model takes place between
events. The time when an event occurs is called an event timestamp and the time within the
model is referred to as simulation time. Real time (or wall-clock time) refers to how long
the simulation is actually running.
The opposite of discrete event simulation models are continuous simulation models where
states change all the time. Numerous systems like manufacturing, transportation, health
care, communication, defenses, information processing, and queuing systems can be
modeled using discrete event simulation.
An entity can be seen as a traffic unit that moves from one point to another in the model.
An entity can be in one of five states [70]:
1. Active state: The active state is the state in which the currently moving entity is.
2. Ready state: The ready state is a state for entities waiting to enter the active state
(i.e. there is more than one entity ready to move but they have to do it one at a
time).
3. Time-delayed state: The time-delayed state is a state in which an entity is waiting
for a known future simulated time to be reached before entering the Ready state.
4. Condition-delayed state: The condition-delayed state is a state in which an entity is
waiting for a certain condition to be satisfied before entering the Ready state.
5. Dormant state: The dormant state represents a sort of hold or sleep state. When an
entity is in the Dormant state, changes in the model conditions cannot trigger a state
transfer. Modeler logic is required to transfer an entity from the Dormant to the
Ready state.
A DES provides a set of lists for each of the five states to organize and track entities. We
will quickly look at the Future Events List (FEL) used for entities in the Time-delayed
state.
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The OMNET++ Simulation Environment
The event loop
The Future Events List is also known as the Future Event Set (FES) and contains the set of
future events, i.e. entities with a time-based delay are inserted into the FES. Figure 5-1
illustrates the basic steps a DES would typically take to implement the event loop. To
ensure causality (that no event effect earlier events), all events are processed in a strict
timestamp order.
Figure 5-1: Flowchart illustrating typical event loop
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The OMNET++ Simulation Environment
OMNET++ INTRODUCTION
OMNeT++ is an object-oriented modular discrete event simulation environment. Its
primary application area is the simulation of communication networks, but it can be used
to simulate almost any discrete event system [71].
OMNeT++ provides a component based architecture for models. The components of an
OMNET++ model are modules programmed in C++. Using a high-level language called
NED, the modules are hierarchically assembled into larger compound modules and models
that can be reused. OMNET++ also has strong GUI support and an embeddable simulation
kernel.
Modules communicate by passing messages that may contain complex data structures to
each other. Messages can be sent directly or along a predefined path to their destination.
Modules can have their own parameters to customize module behavior. At the lowest level
of the module hierarchy we have simple modules implemented in C++ which encapsulate
the behavior.
5.3.1
OMNET++ model structure
Figure 5-2 shows the OMNET++ model structure with its hierarchically nested modules
[72].
The top level module is the system module. The system module contains submodules,
which can also contain further submodules with no limit on the depth of module nesting.
As mentioned, at the lowest level of the module hierarchy we have the simple modules
containing the model algorithms implemented by the user. Modules that contain
submodules are termed compound modules.
OMNeT++ models are also known as networks. In a given network, simple and compound
modules are instances of user defined module types. When describing the network, these
module types are used to define more complex module types. So the system module is also
an instance of a previously defined module type with all other network modules as
submodule or sub-submodule instances of the system module.
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Therefore the OMNET++ module structure allows the user to reflect the logical structure
of the actual system.
Figure 5-2: OMNET++ model structure
5.4
THE NED LANGUAGE
NED (NEtwork Description) is a simple yet powerful topology description language
employed by OMNET++. NED facilitates the network description process by allowing
different component descriptions for simple modules, compound modules and channels.
These component descriptions can be reused in another network description. A NED
description can contain the following components:
•
Import directives
•
Channel definitions
•
Simple module definitions
•
Compound module definitions
•
Network definitions
Without going in too much detail, let us take a brief look at each of these components.
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The OMNET++ Simulation Environment
Import directives
The import directive is used to import component descriptions from another network
description file. After importing a network description the simple modules, compound
modules and channels defined in it can be used. A NED file has a .ned extension.
5.4.2
Channel definitions
Different connection types can be defined by subclassing from the predefined channel
types. OMNET++ provides three predefined channel types and the extends keyword to
indicate inheritance. Two of the three predefined channel types also provide parameters to
specify common channel attributes as discussed below.
IdealChannel
Lets messages pass through without any side effects or delay.
Parameters: (None)
DelayChannel
Introduces a propagation delay.
Parameters:
•
delay: Propagation delay in simulated seconds
•
disabled: If true, the channel object will drop all messages
DataRateChannel
Adds to the parameters of a DelayChannel to allow for data transfer rates and basic
error rates to be taken into account.
•
ber and per: The bit error rate (BER) and packet error rate (PER) with range
[0,1]
•
datarate: Channel bandwidth in bits per second (bps) or its multiples
The values for these attributes are specified by declaring a connection type with a channel
definition inheriting the required predefined channel type. The newly specified channel
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name can then be used to create connections with the attribute values assigned for that
channel type.
The syntax for a channel definition is:
channel ChannelName
{
//Optional declaration of attributes can be done here
}
Or to make use of the predefined channel type parameters:
channel ChannelName extends ned.DataRateChannel
{
datarate = 100Mbps;
delay = 100us;
ber = 1e-10;
}
5.4.3
Simple module definitions
Simple modules are the basic building blocks for compound modules. A simple module’s
definition consists of parameter and gate declarations.
The syntax for a simple module definition is:
simple SimpleModuleName
{
parameters:
//Define the simple module variables here
gates:
//Define in and out gates here
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}
5.4.4
Compound module definitions
Compound modules consist of one or more submodules. These submodules can be simple
modules or other compound modules. Compound modules can be seen as groupings that
allow the simulation model to be organized and structured, i.e. compound modules have no
active behavior. Like simple module definitions, compound module definitions also have
gates and parameter sections but they also have two additional sections - submodules and
connections. All compound module sections are optional.
The syntax for a compound module definition is:
module CompoundModule
{
types:
//Define any channel and module types used locally by
//the compound module
parameters:
//Define the compound module variables here
gates:
//Define in and out gates here
submodules:
//Define the submodules this module is composed of
//here
connections:
//Define the connections between all the module gates
//here
}
Submodules are identified by names. The syntax to declare submodules is:
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module CompoundModule
{
//...
submodules:
submodule1: ModuleType1 {
parameters:
//Assign values to the
//submodule ModuleType1
parameters
of
this
gatesizes:
//Specify the sizes for any gate vectors of
//submodule ModuleType1
}
submodule2: ModuleType2 {
parameters:
//Assign values to the
//submodule ModuleType2
parameters
of
this
gatesizes:
//Specify the sizes for any gate vectors of
//submodule ModuleType2
}
//...
}
The connections section specifies how the gates of the compound module and the gates of
its submodules are connected. The syntax to specify connections is:
module CompoundModule
{
//...
connections:
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// Connect output gate with -->
node1.output --> node2.input;
// Connect input gate with <-node1.input <-- node2.output;
// Connect inout gate with <-->
node1.inout <--> node2.inout;
}
5.4.5
Network definitions
A network definition is needed to actually get a simulation model that can be run. Module
declarations only define module types. To declare a network simulation model a previously
defined compound module type is instantiated.
The syntax of a network definition is:
network networkName extends CompoundModuleType1
{
parameters:
// Example parameters
param1 = 10;
param2 = true;
param3 = truncnormal(100,60);
}
5.5
SIMPLE MODULES
Simple modules are the active elements in a model with events occurring inside them.
Simple module types are programmed in C++ by subclassing the cSimpleModule class
of the OMNET++ class library. The cSimpleModule class has some virtual member
functions that need to be redefined by the user to implement the required model behavior.
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The OMNET++ Simulation Environment
Handling events
Whenever the simulation kernel receives a message, the handleMessage() method
gets called. The handleMessage() method acts as an event-handler and is one of the
virtual functions of the cSimpleModule class that needs to be redefined with message
processing code. It will get called for every message that arrives at the module. No
simulation time elapses within a call to handleMessage().
5.5.2
Passing messages
Simple modules mostly keep themselves busy with sending and receiving messages.
Messages can be sent via output gates or they can be sent directly to another module.
Sometimes we need to model events that occur within the same module. Consider the case
where we want to implement timers or schedule events that need to happen a certain time
in the future. OMNET++ caters for this by allowing a module to send a message to itself.
Such a message is known as a self message.
When a message is received by a module the simulation kernel registers an event and calls
the handleMessage() function of that module to process the message.
The OMNET++ simulation library provides the functions listed in Table 5-3 to facilitate
the transmission of messages between modules.
Table 5-3: Send functions provided by OMNET++
Function
Description
send(..)
To send a message through an output gate
sendDelayed(..) To send a message through an output gate after some delay (to
simulate processing time etc.)
sendDirect(..)
To ignore any gates or connections and send a message directly to
a remote destination module
scheduleAt(..)
To send a self message
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The OMNET++ Simulation Environment
COMPOUND MODULES
Compound modules are made up of submodules which are other compound modules or
simple modules. Compound modules do not have any active behavior. They transparently
relay messages, acting like black boxes. Refer to section 5.4.4 for more detail.
5.7
GATES AND CONNECTIONS
Modules are connected through points called gates. Gates can be of type input, output
or inout. OMNET++ only supports simplex (one-way) communications for its modules.
Hence, for a module to send and receive it has to define an inout gate type for itself or
both an input and output gate type.
Gates are declared by listing their names, which is also used to identify the gate, in the gate
section of a module description. OMNET++ allows gate vectors to be declared. A gate
vector contains a number of single gates.
A connection is made between a starting point (source module output gate) and an ending
point (destination module input gate).
5.8
MESSAGES
In OMNET++ messages are used to represent events. The cMessage class incorporates
the message functionality in OMNET++. cMessage objects and classes derived from
cMessage may model a lot of different things like events, messages, packets, frames, bits
etc. cMessage also provides a number of message object attributes for greater
programming convenience.
5.8.1
Simulating packets
When simulating telecommunication networks in OMNET++, protocol layers are usually
implemented as modules that exchange packets. The packets are message objects
instantiated from a cMessage derived class. To send additional information OMNET++
allows control info objects subclassed from cPolymorphic to be attached.
Another essential when modeling layered protocols is encapsulation and decapsulation of
packets. OMNET++ provides an encapsulate() function to encapsulate a message
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into another one. To get the encapsulated message back the decapsulate() function
can be used.
Message definitions, discussed next, allows one to add parameters or objects to a message.
5.8.2
Message definitions
To add parameters and objects to a message class involves a lot of coding. Message
definitions are a more convenient way to describe message contents. The OMNET++
message definition syntax is very compact and allows for C++ code to be automatically
generated from the message definition. Message definitions files have a .msg extension.
An example of a message definition is given below:
message MyPacket
{
int srcAddress;
int destAddress;
int hops = 32;
}
A MyPacket message class with setter and getter methods for each of the declared fields
will automatically be generated by OMNET++ when the message definition file gets
compiled.
5.9
SUMMARY
Figure 5-3 gives a quick overview of the steps involved to create an OMNET++ simulation
model.
Refer to the OMNET++ user manual [71] for detailed information about OMNET++.
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Figure 5-3: Basic steps to create an OMNET++ simulation model
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6.Simulating a 802.15.4a Ultra
Wideband Physical WSN
This chapter will explain how the 802.15.4a UWB PHY is simulated in OMNET++. It will
describe the simulation goals of this project, identify the focus areas in the 802.15.4a
standard required to meet these goals and finally discuss the application of the UWB model
in a WSN simulation.
6.1
SIMULATION GOALS
The project aims to achieve the following goals to appropriate the analysis of the IEEE
802.15.4a UWB physical layer:
1. Simulate the 802.15.4a UWB PHY as closely and accurately possible to ensure
credible simulation results.
2. Apply the UWB PHY simulation model to a wireless sensor network.
3. Compare the distance, throughput and bit-error-rate (BER) for the various channels
and timing parameters using several channel models.
4. Analyze the impact of the Reed-Solomon coder on the distance, throughput and
BER.
5. Analyze the impact of the convolutional encoder and Viterbi decoder on the
distance, throughput and BER.
6. Analyze the combined impact of both forward error correction schemes on the
distance, throughput and BER.
7. With the various results consider a mechanism allowing for the adaptive change of
channel, timing parameters and forward error correction to guarantee the best BER
for a given minimum throughput or minimum distance.
Chapter 6
6.2
Simulating a 802.15.4a Ultra Wideband Physical WSN
OMNET++ 802.15.4A UWB PHY SIMULATION MODEL
For reasons mentioned in chapter 5, the Mixim-UWB framework for OMNET++ version
4.0 was chosen for simulation purposes.
The Mixim framework combines several existing frameworks for wireless and mobile
simulation in OMNET++ to furnish a rich protocol library and infrastructure allowing
accurate modeling of the environment, mobility, connectivity, reception, obstacles and
collision [73].
6.2.1
Previous work
Jérôme Rousselot built upon the Mixim framework version 1.0 to develop the MiximUWB framework according to the 802.15.4a specification. The Mixim-UWB framework is
still under development and the source code can be downloaded from the GitHub code
sharing site at [74]. The Mixim-UWB baseline used in this project can be found on the
master branch stamped with the date 9 July 2009.
The Mixim-UWB framework provides the following useful components:
•
Symbol-level UWB Impulse Radio simulator
•
IEEE 802.15.4a transceiver with energy detection based receiver
•
Accurate modeling of path-loss and fading
•
Relatively easy evaluation of the BER
With regards to the 802.15.4a UWB PHY standard, the Mixim-UWB framework includes:
•
UWB signal simulation on a symbol level.
•
SHR and Data parts of the UWB PHY frame format.
•
Burst Position Modulation.
•
Time Hopping.
•
Calculation of spreading sequence.
•
Energy detector (non-coherent receiver).
•
Limited synchronization.
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•
Impact of Multiple Access Interference (MAI).
•
Simple UWB pulse.
•
Limited error correction. Caters for Reed-Solomon coder by making sure the
number of bit errors at receiver does not exceed the number of bits the R-S code
can actually fix.
•
Multiple channel models.
•
Easy adjustment of receiver sensitivity.
•
Mandatory channel of frequency band 1 (channel 3).
•
Mean PRF of 15.60MHz and bit rate of 850kbps.
•
Preamble with default length sequence only with implementation limited to
preamble code with index 5.
6.2.2
Contributions
To be able to achieve the simulation goals the following modifications and additions were
made to the Mixim-UWB framework:
•
Added capability to configure channel, bit rate, mean PRF and number of preamble
symbols with automatic calculation of all UWB PHY rate and timing parameters
based on these configuration values.
•
Improvements to spreading sequence calculations including support for all length
31 and length 127 preamble codes.
•
Moved generation of the UWB symbol from the MAC layer to the PHY layer
where FEC encoding is also performed.
•
Provided a SECDED implementation.
•
Added Binary Phase Shift Keying modulation.
•
Modified energy detector to be able to demodulate the polarity of a burst in
addition to its position.
•
Added Reed-Solomon encoding and decoding.
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•
6.2.3
Simulating a 802.15.4a Ultra Wideband Physical WSN
Added convolutional encoding and Viterbi decoding.
Channels
The Mixim-UWB framework only implements the mandatory channel of frequency band
1, i.e. channel number 3. A new class called IEEE802154A_Config was added to the
model allowing real time selection of a different channel number and\or bit rate.
Upon simulation startup the OMNET++ initialization file requires the parameters listed in
Table 6-1 to be configured for each node in the wireless network.
Table 6-1: Configurable channel parameters and permissible values
Parameter Name
Description
Permissible values
channelNum
The channel number.
0 to 15
complexChannelNum
(Optional – Default = 1)
meanPRF
The mean PRF.
1 = 1st preamble code
The complex channel number specified for channel
indicating the index of the length 31 2 = 2nd preamble code
preamble code to use.
specified for channel
1 = 3.90MHz
2 = 15.60MHz
3 = 62.40MHz
dataRate
The data rate.
0 = (non-UWB PHY)
1 = 110kbps
2 = 850kbps
3 = 1.70Mbps
(for PRF of 3.90MHz)
else = 6.81Mbps
4 = 6.81Mbps
(for PRF of 3.90MHz)
else = 29.24Mbps
nbPreambleSyncSymbols The number of repetitions of the 8 or 64
preamble symbol used in the SHR.
The physical layer of each node maintains a pointer to the IEEE802154A_Config
instance configured for that node allowing its physical layer to issue a channel change
command if requested from the upper layer. The arguments supplied to the channel change
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function are the same parameters listed in Table 6-1 and hence a new channel and\or new
bit rate can be configured through such a call.
Only the PAN coordinator node should be permitted to initiate the broadcast of a channel
change command.
6.2.4
UWB PHY rate, timing and preamble parameters
The IEEE802154A_Config also contains all the UWB PHY rate, timing and preamble
parameter values as class members. Most of these values are automatically calculated
when the IEEE802154A_Config object is initialized or reconfigured. The parameters
that are not calculated make use of constants and lookup tables to obtain their values.
All of these parameters are easily accessible at each node’s physical layer through the
pointer it maintains to the configuration object.
6.2.5
UWB simulation model process flow
An outline of the processes the UWB simulation model performs are illustrated in Figure
6-1 for the transmitter and Figure 6-2 for the receiver. Although a lot of complexity sits
behind these processes, the illustrations provides a logical overview of the steps involved
at the lower layers to transmit a data message from one node to another.
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Figure 6-1: Transmitter: UWB simulation model process flow
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Figure 6-2: Receiver: UWB simulation model process flow
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6.2.6
Simulating a 802.15.4a Ultra Wideband Physical WSN
UWB frame format
The frame format was kept the same – that is, only the SHR and Data parts are generated
as shown in Figure 6-3.
Figure 6-3: UWB Simulation: Frame format
A Mixim Signal object is used with a time mapping container storing the various
transmit power samples of the preamble and UWB PHY symbol repetitions over time. It is
exactly this modeling of the low level detail of each symbol that gives the Mixim-UWB
framework its novel “symbol-level simulator” characteristic.
Evidently the duration of the Signal object, representing the UWB frame, had to be
increased to include the addition of error correction bits. It also made more sense to create
the Signal object at the MAC layer but to generate and populate the transmit power
mapping sample values of the symbols at the PHY layer where the error correction is
performed. The original Mixim-UWB framework differs in that it creates the Signal
object and generates the sample values at the MAC layer.
6.2.7
UWB pulse
The pulse employed is a triangular pulse chosen for its simplicity. To store a single
triangular pulse only requires three samples values and their corresponding times to be
saved in a time mapping container (see Figure 6-4).
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Figure 6-4: Triangular pulse and corresponding time map
6.2.8
SHR preamble generation
Both the SYNC and SFD portions of the SHR are simulated by the Mixim-UWB
framework. First the SYNC portion is generated and added to the transmit power mapping
of the Signal object after which the same is done for the SFD portion. The algorithms
used to generate these are shown in Figure 6-5 and Figure 6-6.
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Figure 6-5: UWB simulation: Generation of SHR SYNC
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SYNC preamble algorithm
The first step in generating the SYNC preamble is to determine the configured preamble
code length (Clength). According to the IEEE 802.15.4a standard the preamble code length
value can only be 31 or 127. The preamble codes are also defined by the standard and were
chosen for their perfect periodic autocorrelation properties.
The next step is to generate a total of Nsync preamble symbols, where Nsync is the number of
symbols in the packet SYNC sequence also defined by the standard.
A single preamble symbol is created as follows:
1. Look up the corresponding preamble code sequence given the code length and
channel number.
2. Spread the preamble code sequence using the delta function and Kronecker
product as defined in equation 4-11. Zeros are added after every preamble code
symbol (from the ternary symbol alphabet {-1, 0, 1}). The number of zeros that
gets added determines the spread and is given by the L parameter of the delta
function. L-1 zeros are always added.
3. To represent the preamble code sequence symbols, the time of each ternary symbol
in relation to the whole UWB frame is calculated and at that point in time the
transmit power mapping is set to have a pulse peak of 1 or -1 depending on the
value of the ternary symbol (this result in the triangular pulses as illustrated in
Figure 6-4).
Therefore, a preamble symbol is the preamble code sequence spread by the delta function
of length L with every non-zero preamble code sequence symbol represented by a
triangular pulse.
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Figure 6-6: UWB simulation: Generation of SHR SFD
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SFD preamble algorithm
The first step in generating the SYNC preamble is to determine if the short or long length
SFD should be used. According to the IEEE 802.15.4a standard the short SFD length is 8
and the preferred SFD to use. The longer SFD has length 64 and is optional for use with
transmissions at data rate of 110kbps. The actual short and long SFD codes are also
defined by the standard.
The next step is to spread the SFD code with the preamble symbol.
The preamble symbol is created in the same way as was done for the SYNC portion. The
steps are as follows:
1. Look up the corresponding preamble code sequence given the code length (Clength)
and channel number.
2. Spread the preamble code sequence using the delta function and Kronecker
product.
3. To represent the preamble code sequence symbols, the time of each ternary symbol
in relation to the whole UWB frame is calculated and at that point in time the
transmit power mapping is set to have a pulse peak of 1 or -1 depending on the
value of the ternary symbol (this result in the triangular pulses as illustrated in
Figure 6-4).
To spread the SFD code with the preamble symbol, each SFD ternary symbol is multiplied
by the preamble symbol.
As an example, the short SFD is defined as [0 +1 0 -1 +1 0 0 -1]. If the preamble symbol is
given by pS, then the result after spreading will be [0 +pS 0 –pS +pS 0 0 –pS].
6.2.9
UWB symbol generation
The same transmit power mapping container is used for the sample values representing the
absence or presence of UWB bursts over the time duration of the Data part. The method
behind the generation of all these UWB symbols making up the Data part of the frame is
shown in Figure 6-7.
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UWB data symbol algorithm
At this point the data encoding is already performed and as such there exist both a position
bit and sign bit for every data bit.
The first step is to set the time where the data starts in the UWB data symbol. Relative to
this time, all the UWB pulse bursts are added.
For every data bit the following takes place:
1. The position of the burst is determined. The varying time hopping code is used to
determine the position of the burst taking into account the position bit and allowing
for a guard interval.
2. For every chip in the burst (number of chips per burst given by Ncpb as defined by
the standard) a triangular pulse is generated as illustrated in Figure 6-4.
3. To represent the triangular pulse, the time of every chip in the burst is determined
in relation to the start of the data in the UWB symbol. At that point in time the
transmit power mapping is set to have a pulse peak of 1 or -1 depending on the sign
bit value.
6.2.10 Data bits generation
The simulation model utilizes a uniform random number generator with seed values that
can easily be varied for different experiment runs. The MAC layer receives the required
number of bytes to transmit from the upper layers and automatically generates a uniform
random bit stream representing the message data.
6.2.11 PHR
The PHR part is not yet included in the simulation model. It consists of 19 bits containing
its own parity through the SECDED bits making it very robust against transmission errors.
Without too much difficulty the PHR part can be generated by future contributors. More
work will have to be done at the receiver though, to process the PHR bits and utilize the
PHR information effectively to aid with reception and synchronization of any new
incoming frames. A structure for the PHR and a SECDED implementation was added to
the model to assist any research requiring the PHR.
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Figure 6-7: UWB simulation: Generation of SHR SYNC
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6.2.12 Reed-Solomon encoding and decoding
To
be
able
to
perform
R-S
encoding
and
decoding
a
new
class
called
IEEE802154A_ReedSolomon was added to the model. This class employs the C++
implementation of the Schifra Reed-Solomon error correction library maintained by Arash
Partow [75].
The Schifra library is free to use for academic purposes under the GNU General Public
License. The Schifra library is completely configurable allowing for the primitive
polynomial and finite field of the 802.15.4a standard to be set up. The library features
errors and erasures, different symbol sizes, variable code block lengths and an optimized
architecture for various hardware platforms.
A few new classes had to be added under the bitio namespace. These allow the data
bitstream to be converted to 6-bit symbols and back. The 6-bit symbol size is defined by
the R-S code Galois field, GF(26), specified by the 802.15.4a UWB standard.
Every 330 bits of data gets an additional 48 parity bits after R-S encoding. If the data is
less than 330 bits, zero bits are added to the front until the block size is 330 bits. R-S
encoding is then performed and the zero bits are removed before transmission. In such a
case the receiver will follow the same procedure by adding zero bits before R-S decoding
the block.
6.2.13 Convolutional encoding and Viterbi decoding
The algorithms used for the convolutional encoder and Viterbi decoder were written by
Chip Fleming and the source together with an excellent tutorial on Viterbi decoding can be
found online at [76]. Through personal correspondence, Chip Fleming granted permission
to use his convolutional encoder and Viterbi decoder implementations with the prerequisite
that it may only be used for academic and research purposes.
The original implementation is in C, which posed a problem if it were to be integrated in
the modular OMNET++ environment. Hence the C implementation was ported to C++
classes to provide configurability and to facilitate the integration of the convolutional
encoder and Viterbi decoder in the Mixim-UWB model. The class diagram of the C++
implementation is made available in Figure 6-8.
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Figure 6-8: Convolutional Encoder & Viterbi Decoder class diagram
Implications on energy detection receiver
An energy detection receiver is a non-coherent receiver and as such it cannot make use of
the polarity information modulated in the UWB PHY symbol. It is exactly this polarity
information that is stored by the sign bit stream output from the convolutional encoder. In
order to be able to evaluate the performance of the convolutional code and decode the sign
bit stream at the receiver, the energy detection receiver was modified to take the polarity of
each burst into account before that information is lost.
Note that this is definitely not the preferred way of making use of the polarity information.
The correct approach is to use a correlation receiver.
A correlation receiver is a coherent receiver because it knows something about the
transmitted waveform used by the transmitter. A correlation receiver incorporates a known
reference signal. By multiplying the received signal with the reference signal and then
integrating over the signal duration, the receiver can very accurately distinguish noise and
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decide if it is the recipient of a particular signal. Such receivers require very accurate and
fine synchronization to line up the received signal with the reference signal.
The implementation of a correlation receiver for the simulation model is outside the scope
of this project and therefore the energy detection receiver was modified instead.
6.3
SIMULATING 802.15.4A WSN
The OMNET++ environment allows for easy configuration of the number of nodes in a
network and various other network parameters. Network description files specify the
different components (OMNET++ modules) to use in your network and it is through these
description files that the 802.15.4a UWB IR implementation is incorporated. A graphical
view of the components specified in the network description files for the simulated
802.15.4a WSN is given in Figure 6-9.
6.3.1
Application layer
The Mixim-UWB model makes use of a simple TestApplicationLayer. It basically
requests a configured number of packets of a certain size to be transmitted at certain time
intervals throughout the simulation. If all the number of packets had been send,
transmission stops. Incoming packets are deleted after some statistics were collected.
6.3.2
Network layer
As network layer DummyRoute is used. No special routing functions are performed – it
simply translates the network packet information to MAC packet information.
6.3.3
MAC layer
An AlohaMacLayer is defined by the Mixim-UWB model. This MAC layer inherits
from the base UWB MAC layer provided by the framework, called UWBIRMac. The
AlohaMacLayer supports the Pure ALOHA approach – if a node has data to send, it
sends it. If this message collides with another node’s message then retransmission will
occur sometime later.
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The UWBIRMac also creates the Signal object used to carry the preamble and data
symbol information and generates random bits to serve as the physical data bits of a
message.
Figure 6-9: Simulated 802.15.4a WSN components
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7.Simulation Results
The various simulation results and how they were obtained will be presented and discussed
in this chapter. All results are easy to reproduce and the reader can browse through the
contents on the accompanied DVD to acquire more information and the required software
tools (see Addendum A).
7.1
ASSUMPTIONS
For the simulations performed using the 802.15.4a UWB model the following assumptions
were made:
1. Perfect synchronization: The Mixim-UWB model allows for basic synchronization on
the first preamble. The simulations experiments performed assumed perfect
synchronization.
2. Outside interference from other technologies: Other communication systems such as
802.15.11 also introduce interference. This is not taken into account.
3. MAI results not considered: Although the simulation model provides for MAI
modeling and a multiple node WSN is presented, no MAI results were considered.
4. Uniform random number generator: Data bits are randomly generated with a uniform
distribution.
5. Energy detection receiver used to demodulate bit burst polarity: As mentioned before,
an energy detection receiver is non-coherent in nature and cannot take the burst
polarity into account. The simulations that make use of the Viterbi decoding algorithm
employ a modified version of the energy detection receiver that can read the polarity of
the burst. The correct approach will be a correlation receiver.
6. Triangular pulse: A triangular pulse is used as UWB pulse in a chip interval. Although
such a pulse might not meet the exact power constraints set by the FCC, it enables a
simple UWB signal implementation that does not require a lot of memory to store.
Chapter 7
Simulation Results
7. Clock drift: Different systems have different clocks making clock drift unavoidable.
Clock drift is not modeled or taken into account.
7.2
CONVOLUTIONAL ENCODER AND VITERBI DECODER PORTING
In order to verify that the ported C++ implementation of the convolutional and Viterbi
algorithms is working, some tests were performed to compare the results against those of
the original C implementation.
Gaussian random noise variables were generated and added to the signal by calculating the
standard deviation of Additive White Gaussian Noise (AWGN) for varying Eb/No values.
NASA did a lot of experiments and proved that for the constraint length K = 3 the best
polynomials is (7, 5). For the comparison tests between the C and C++ implementations
these polynomials were used by the convolutional encoder.
Figure 7-1 shows the average results after a few test runs. It is clear that the C++ Viterbi
implementation is working properly and gives almost the exact same results as the C
Viterbi implementation.
Although the polynomials (7, 5) were proven to be the best for constraint length K =3, the
802.15.4a standard specifies the polynomials (2, 5) be used for the UWB PHY
convolutional encoder. The same test were performed using the C++ implementation but
with polynomials (2, 5) and the results are also shown on the graph in Figure 7-1. The
results clearly show that the polynomials (2, 5) perform considerably worse.
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1.00E+00
0
1
2
3
4
5
6
7
UWBPoly (C++)
1.00E-01
C Implementation
C++ Implementation
BER
1.00E-02
1.00E-03
1.00E-04
1.00E-05
1.00E-06
Eb/No (dB)
Figure 7-1: Comparing C & C++ implementations of Viterbi algorithm
7.3
SIMULATION MESSAGE TRACE BETWEEN 2 NODES
The scheduling of simulation timers (for self messages) and flow of messages between two
communicating nodes are illustrated in Figure 7-2 where node1 transmits a single packet to
node0. Events points in the figure are identified by a number prefixed with a # character.
Event
Description
#0
At time 0 the simulation starts. The app layer of node1 schedules a timer (self
message) called “app-delay-timer”. This provides a time delay before data is
transmitted by the app layer. During this time delay the node can initialize and
prepare itself to transmit data.
The phy layer of node 1 also schedules a timer message called
‘RadioSwitchingOver” to simulate the time it would take for the radio circuitry
of a real node to change state. The default state for a node is RECEIVE but
because node1 has to transmit data, the node state is set to TRANSMIT and a
“RadioSwitchingOver” timer is scheduled.
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#1
Simulation Results
The “RadioSwitchingOver” timer expired (self message was received) and the
node1 phy radio state is now considered to be TRANSMIT.
Another “RadioSwitchingOver” timer is scheduled which simulates the time it
would take for the phy layer to notify the mac layer about the radio state
change.
#2
The mac layer of node1 receives the message and prepares the mac to receive
data from the network layer.
#3
The “app-delay-timer” expires after 5 seconds. The node is now initialized and
ready to transmit data. The node1 application layer creates a message called
“DataMessage” and sends it down to the node1 network layer.
#4
The node1 network layer gets “DataMessage”, does the necessary processing
and sends “DataMessage” off to the node1 mac layer.
#5
The node1 mac layer gets “DataMessage”, does the necessary processing and
sends “DataMessage” off to the node1 phy layer.
#6
The node1 phy layer gets “DataMessage” and transmits it on the
communication channel as an Airframe. The node1 phy layer also creates a
“RadioTxOver” timer to simulate the time it takes the radio circuitry to transmit
all the data bits on the channel.
#7
The radio of node0 is in the RECEIVE state and actively listening for any
airframes. At this event it detected and synchronized to the start of the airframe
transmitted by node1 and schedules an “airframe” self message as a short delay
to allow all bits of the airframe to be received.
#8
All bits of the airframe is now received and the node0 phy layer schedules
another “airframe” self message for the purpose of simulating the “over air”
transmit time and to add any noise as defined by the channel propagation model
currently utilized.
#9
Node1 phy layer “RadioTxOver” timer expires, indicating that the radio has
finished transmitting all data bits. A “TxOver” control message is also send to
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the node1 mac layer to report this to the upper layers.
#10
The node0 phy does the final processing of the airframe, decodes the
transmitted data contained in the airframe and sends this data off to the node0
mac layer.
#11
The “transmission over” message notification is received by the node1 mac
layer and forwarded to the node1 net layer.
#12
The node0 mac layer receives the data bits transmitted all the way from node1
app layer. The mac layer decides if the message was received correctly, does
some statistic calculations and then throws away the message (in this case there
is no acknowledgement message).
Note that the simulation could easily be adapted to also model the processing of
the received message by the upper layers (net & app) of node0.
#13
The “transmission over” message notification is received by the node1 net layer
and forwarded to the node1 app layer.
#14
The “transmission over” message notification is received by the node1 app
layer which logs the occurrence.
.
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Figure 7-2: 802.15.4a UWB model – Message flow between 2 nodes
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7.4
Simulation Results
2-NODE 802.15.4A UWB WSN
In order to analyze the 802.15.4a UWB model, the simulation scenario depicted in Figure
7-3 was used. Although simple, this simulation allows for a lot of 802.15.4a physical layer
characteristics to be analyzed.
The World module creates a simulation playground for the two nodes of 500m x 500m x
500m. Only node1 are allowed to send messages which node0 receive and then compares
with the originally transmitted data. Node0 also calculates and logs information such as the
bit error rate (BER) and data bit rate.
Figure 7-3: 802.15.4a UWB model – 2 node WSN scenario
Each node has the ability to move. Node1, after sending a set number of packets of a
certain size, uses this functionality to move away from node0 before starting to send new
packets again. The simulation of this mobility allows for performance measurements to be
taken over different communication distances.
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BER and data bit rate are the performance measurements employed in the analyses
performed for this result set. The channel propagation models utilized to model signal
attenuation are:
•
CM1 (LOS)
•
CM2 (NLOS)
•
Ghassemzadeh-LOS
•
Ghassemzadeh-NLOS
7.4.1
Effect of different bit rates
The 802.15.4a standard defines the nominal data rates indicated in Table 7-1 for the UWB
physical layer.
Table 7-1: 802.15.4a UWB PHY nominal data rates
Mean PRF
3.90MHz 15.60MHz 62.40MHz
DataRate0 (Mbps)
0.11
0.11
0.11
DataRate1 (Mbps)
0.85
0.85
0.85
DataRate2 (Mbps)
1.70
6.81
6.81
DataRate3 (Mbps)
6.81
27.24
27.24
As a first result, the mandatory channel 3 in frequency band 1 is considered. For this
mandatory channel the mean PRF is 15.6MHz, the bandwidth is 499.2MHz and the
preamble code length equal to 31. By varying the number of chips per burst (_%a ) while
keeping the number of possible burst positions (_fX~) ) in a data symbol constant, the four
data bit rates listed in Table 7-1 is achieved.
Figure 7-4 shows how the BER changes over distance when the data bit rate is 850kpbs –
the experiment is run over the four channel models and the corresponding results are
plotted together.
The configuration settings for this simulation scenario are:
-
100 packets sent from node1 to node0
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-
7 byte MAC header
-
73 byte PHY payload
-
64 000 bits transmitted per experiment run
-
Forward error correction not performed at the receiver
Simulation Results
The CM2 channel model is most harsh and in such environments any nodes
communicating further than 4 meters apart will require a lot of retransmissions due to a
high number of bit errors. The Ghassemzadeh-NLOS channel model does not allow for
much longer distances and compared to the CM2 channel model it performs much the
same.
The LOS channel models, CM1 and Ghassemzadeh-LOS, yields the best results. Further
experiments with larger datasets demonstrated that for communication distances up to 50m
a BER of 1e-6 can be maintained.
Figure 7-5 to Figure 7-7 depicts the distance performance results of the same mandatory
channel 3 setup, but with the data bit rate at 110kbps, 6.85Mbps and 27Mbps respectively.
From these results it can be observed that for a given distance and channel model the BER
gets worse with increasing bit rate. The reason for this is found in a symbol burst - a large
number of chips per burst _%a results in a high processing gain because the increased
burst duration (\fX~) ) allows for more efficient reception. Longer bursts means longer
symbol durations if the number of burst positions (_fX~) ) remains constant. Consequently
a penalty hit is taken on the bit rate.
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Figure 7-4: BER vs Distance – Chan 3, PRF 15.6MHz, Bit rate 850kbps
Figure 7-5: BER vs Distance – Chan 3, PRF 15.6MHz, Bit rate 110kbps
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Figure 7-6: BER vs Distance – Chan 3, PRF 15.6MHz, Bit rate 6.8Mbps
Figure 7-7: BER vs Distance – Chan 3, PRF 15.6MHz, Bit rate 27Mbps
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Simulation Results
Effect of mean pulse repetition frequency (PRF)
The other two mean PRFs defined by the standard is 3.90MHz and 62.40MHz. The
difference between the three specified PRFs is captured in the number of burst positions
per symbol _fX~) as defined in Table 7-2.
Table 7-2: 802.15.4a UWB PHY number of burst positions for a given mean PRF
Mean PRF 3.90MHz 15.60MHz 62.40MHz
_fX~)
128
32
8
The resulting BER performance over distance for channel 3 at 850kbps with PRF of
3.90MHz is shown in Figure 7-8. Compared to a PRF of 15.6MHz (Figure 7-4) it performs
a little less well because it has less number of chips per burst _%a .
The resulting BER performance with a mean PRF of 62.40MHz is shown in Figure 7-9.
Compared to a PRF of 15.6MHz (Figure 7-4) it performs much better, especially for the
LOS channels. Again this is due to the 62.40MHz configuration having more _%a than the
15.6MHz configuration. The standard also defines a preamble length of 127 for a mean
PRF of 62.40MHz, allowing for better synchronization at the faster rate.
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Figure 7-8: BER vs Distance – Chan 3, PRF 3.90MHz, Bit rate 850kbps
Figure 7-9: BER vs Distance – Chan 3, PRF 62.40MHz, Bit rate 850kbps
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7.4.3
Simulation Results
Effect of center frequency
Figure 7-10 can be used to observe the effect the center frequency has on the
communication performance. The center frequency of channel 3 is specified at
3993.6MHz, and that of channel 14 at 9984.0MHz.
A low frequency signifies a long wavelength, while a high frequency indicates a short
wavelength. In practice it has been shown that long wavelength signals carries further than
short wavelength signals and the longer wavelength also allows for better penetration
capability.
It can be seen that channel 14 performs worse than channel 3 for the same bit rate and
mean PRF.
Figure 7-10: BER vs Distance – Chan 14, PRF 15.6MHz, Bit rate 850kbps
7.4.4
Effect of bandwidth
The 802.15.4a standard defines four channels with a bandwidth greater than 499.2MHz as
shown in Table 7-3.
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Table 7-3: 802.15.4a UWB PHY channels with increased bandwidth
Channel Bandwidth (MHz)
4
1331.2
7
1081.6
11
1331.2
15
1354.97
A larger bandwidth allows for greater channel capacity and also very high precision range
measurements. On the downside, it also implies that more noise will now be present on the
channel.
The results of an experiment run with channel 4 at 850kbps and PRF 15.60MHz depicts an
improvement in the BER for all channel models if compared to channel 3 with the same
settings. The BER for the LOS channel models are better than 1e-4 at a distance of 100m.
See Figure 7-11 for the graph.
Figure 7-11: BER vs Distance – Chan 4, PRF 15.6MHz, Bit rate 850kbps
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7.4.5
Simulation Results
Sub-Gigahertz band communication
In the sub-gigahertz band only one channel, channel 0, is specified. It sits at a center
frequency of 499.2MHz with a bandwidth of 499.2MHz. By taking the different effects
discussed so far into account, it can be seen that channel 0 with PRF of 3.90MHz and data
rate of 110kbps will result in the best BER performance over very large distances.
Figure 7-12 proves this and it is clear that even the for NLOS indoor channel propagation
models the achievable distances are very good – in the transmission of 64 000 bits, data bit
corruption only occurred at distances greater than 10 meters. The downside though is a
much lower data bit rate.
Figure 7-12: BER vs Distance – Chan 0, PRF 3.90MHz, Bit rate 110kbps
7.4.6
Effect of forward error correction
The modified 802.15.4a UWB model allows for the Reed-Solomon and Viterbi algorithms
to be run separately, together or not at all.
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After various simulation experiments with different channels, PRFs, bandwidths and
bitrates, it was interesting to find that the FEC does not add a considerable amount of gain
when the BER is already bad.
By definition the Reed-Solomon (63, 55) code are only capable of recovering from 4
symbol errors for each R-S encoded data block of 378 bits (330 data + 48 parity). For an
environment with high BER at large distances, this limitation causes the R-S decoder to
fail most of the times resulting in processing without gain. A possible improvement might
be a better code such as a (255, 223) with which 16 symbol errors can be corrected.
The performance of the Viterbi decoder on its own is also meager for such high BER
cases. In order to integrate the Viterbi algorithm in the UWB model, a coarse quantization
and normalization of the energy detector voltage values had to be done. This approach is
known to be sub-optimal. It was also proven through simulation that the convolutional
encoder polynomials (7, 5) delivers much better gain than the specified (2 ,5) polynomials.
The Viterbi algorithm is also known to work much better in communication scenarios
polluted by AWGN.
For very low BERs the single error that occurs here or there are easily fixed by the forward
error correction techniques adding the advantage of not having to retransmit the corrupted
data.
A graphical comparison between the different FEC combinations is provided in Figure
7-13 and Figure 7-14. The mandatory channel 3 was used with PRF 15.6MHz and data rate
at 850kbps. The LOS channels already perform excellent so these two figures depict the
results of the NLOS channel models, Ghassemzadeh-NLOS and CM2 for distances up to a
few meters.
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Figure 7-13: BER vs Distance – CM2 FEC compare
Figure 7-14: BER vs Distance – Ghassemzadeh-NLOS FEC compare
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As discussed these results show that for a high BER the FEC does not add substantial gain.
Table 7-4 and Table 7-5 show the FEC gains for the two NLOS channel models at some of
the distances points.
Table 7-4: 802.15.4a UWB PHY – Ghassemzadeh-NLOS FEC compare
Ghassemzadeh-NLOS at Ghassemzadeh-NLOS at
distance of 4m
distance of 6m
BER
Gain (dB)
BER
Gain (dB)
0.004625
0
0.0445
0
0.001
6.65
0.02475
2.55
0.003875
0.77
0.0355
0.98
RS and Viterbi 0.00175
4.22
0.021375
3.18
No FEC
RS
Viterbi
Table 7-5: 802.15.4a UWB PHY – CM2 FEC compare
CM2 at distance of 3m
CM2 at distance of 4m
BER
Gain (dB)
BER
Gain (dB)
0.05425
0
0.0445
0
0.032
2.29
0.02475
2.01
0.03575
1.81
0.0355
0.49
RS and Viterbi 0.010125
7.29
0.021375
3.1
No FEC
RS
Viterbi
The results indicate that at close range (up to 6 meters) some considerable gain for NLOS
channel models is achieved by employing the FEC techniques.
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7.5
Simulation Results
MULTI-NODE 802.15.4A UWB WSN
Figure 7-15 shows an example of a typical WSN with multiple nodes at random locations.
Nodes can communicate with each other at the same time introducing multiple access
interference. In this scenario all nodes can communicate with each other. If the 802.15.4a
MAC protocol is used, a node can be declared as a RFD or FFD which limits the
connections between nodes because an RFD can only communicate with the PAN
coordinator FFD.
Figure 7-15: 802.15.4a UWB model – Multiple node WSN scenario
Although MAI results are not presented, the Mixim model was designed to take it into
account. When starting to receive, a node’s receiver will typically take into account all
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Simulation Results
interfering airframes and attenuate the received signal accordingly. Figure 7-16
demonstrates this concept.
Interfering Airframe 1
Received Airframe
Interfering Airframe 2
Interfering Airframe 3
Figure 7-16: Interfering airframes causing MAI
7.5.2
802.15.4a UWB PHY throughput
Some curious results are ascertained when comparing the different data bit rates possible
with no FEC, with only R-S encoding, with only convolutional decoding and with both
encoding mechanisms activated (see Table 7-6).
Without any FEC, data rates of up to 62.4Mbps can be achieved. Even the bit rate of
channel 0, which proved to be very robust over long communication distances, can be
more than doubled.
This imparts something to consider – how can a node achieve the best throughput while
still maintaining its mobility functionality?
If a node knows its goals, operating environment, channel characteristics, power
constraints, maximum allowable BER and required minimum throughput, it can be
programmed beforehand with a “best performance” configuration allowing it to change
certain communication parameters to best achieve its mission.
The “best performance” configuration does not even have to be loaded beforehand. If
power constraints permits, a node can be made intelligent and it can train itself to “learn”
about its operating environment and channel. With this self obtained knowledge the node
can take into consideration all variables and set up the communication parameters in such a
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Table 7-6: 802.15.4a UWB PHY – Data bit rates for different FEC combinations
Bit Rate (Viterbi Bit Rate (RS
Bit Rate
Applicable mean
Only)
Only)
(Viterbi & RS)
PRF
Tdsym
(ns)
Viterbi
Rate
RS Rate
Overall FEC
Rate
Bit Rate (No
FEC)
32.05
0.50
0.87
0.44
62 402 496
31 201 248
54 290 172
27 145 086
64M
64.10
1.00
0.87
0.87
31 201 248
31 201 248
27 145 086
27 145 086
16M
128.21
0.50
0.87
0.44
15 599 407
7 799 704
13 571 484
6 785 742
16M & 64M
256.41
1.00
0.87
0.87
7 800 008
7 800 008
6 786 007
6 786 007
4M
512.82
0.50
0.87
0.44
3 900 004
1 950 002
3 393 003
1 696 502
4M
1025.64
0.50
0.87
0.44
1 950 002
975 001
1 696 502
848 251
4M & 16M & 64M
8205.13
0.50
0.87
0.44
243 750
121 875
212 062
106 031
4M & 16M & 64M
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manner as to optimally achieve its goals. Even FEC can be turned on and off as required. If
stationary, the node can optimize for speed. If mobile, the node can optimize for distance.
Figure 7-17 to Figure 7-19 shows the BER for the four data bit rates as defined by the
standard. Only distances that guarantee a BER < 1e-4 is shown.
110kbps
850kbps
6.8Mbps
27Mbps
1.00E-06
1.00E-05
BER
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1m
2m
3m
4m
5m
6m
7m
Figure 7-17: Throughputs and distances with BER < 1e-4 (CM1)
In a communication environment that follows a CM1 channel model, Figure 7-17 shows
that transmitting at 27Mbps will not give a BER < 1e-4 even if the distance between the
transmitting nodes is 1m. Communicating at 6.8Mbps will give a BER < 1e-4 up to a
distance of 4m. With 850kbps the distance limit is 5m and with 110kbps up to 7m can be
achieved.
From Figure 7-18 it is clear that in a CM2 type environment the 6.8Mbps and 27Mbps
data bit rates can be achieved for nodes that are close together (1m or 2m), while still
maintaining a BER < 1e-4.
The Ghassemzadeh-NLOS channel model is more lenient and a bit rate of 27Mbps can be
achieved up to 9m in such an environment. The Ghassemzadeh-LOS case is not shown
because the model does not provide practical results for throughput profiling.
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110kbps
850kbps
6.8Mbps
27Mbps
1.00E-06
1.00E-05
BER
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1m
2m
3m
4m
Figure 7-18: Throughputs and distances with BER < 1e-4 (CM2)
850kbps
110kbps
6.8Mbps
27Mbps
1.00E-06
1.00E-05
BER
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1m
2m
3m
4m
5m
6m
7m
8m
9m
10m
Figure 7-19: Throughputs and distances with BER < 1e-4 (Ghazzemzadeh-NLOS)
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From these throughput graphs it is simple to see how a mobile node can store this
knowledge and then optimize its communication parameters accordingly. If the node is
intelligent it can also learn about its environment and share this knowledge with new nodes
to allow the whole sensor network to operate in an optimized fashion.
7.6
PROPOSAL FOR A COGNITIVE AND ADAPTIVE TECHNIQUE
Consider the example network portrayed in Figure 7-20. It consists of a single PAN
coordinator node and five sensor nodes classified as Reduced Function Devices (RFDs) by
the 802.15.4 MAC. Each sensor node can either be mobile or stationary.
Figure 7-20: Example topology for demonstrating cognitive and adaptive technique
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Assume that the radios of all nodes support all the channels and mean PRFs specified by
the 802.15.4a UWB physical layer standard. The PAN coordinator node is also equipped
with more storage space and higher processing capability.
A PAN coordinator node can be programmed to be aware and intelligent by following a
“best performance” scheme according to a preset configuration defining the Quality of
Service (QoS) that needed to be maintained.
Such a coordinator node can use its cognitive ability to measure and maintain the
performance of each link to each node registered with it. Various performance
measurement criteria can be used to specify the desired level of quality for all nodes or
each node individually. In the example provided the stationary nodes might have a
different function to fulfill than the mobile nodes.
The PAN coordinator node can keep two tables with information about the link to each
node. Table 7-7 gives an example.
Table 7-7: Example best performance tables of an intelligent coordinator node
Stationary Nodes Table
Node Id Distance BER RSS SNR
1
18m
..
..
..
3
20m
..
..
..
5
4m
..
..
..
Mobile Nodes Table
Node Id Distance BER RSS SNR
2
5m
..
..
..
4
1m
..
..
..
Various performance criteria can be used to measure against. Limits to maintain QoS are
specified beforehand. Actual configuration parameters to employ are defined by these
limits. Previous information about the communication environment and links to each node
can be used as well. During the course of communication the specific performance
measurement criteria are kept up to date to ensure either optimal throughput or optimal
range.
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A node registers itself with the PAN coordinator with certain requirements. The
coordinator can then make use of the ranging functionality in an 802.15.4a UWB frame to
determine the exact distance to the newly registered node and apply the following simple
rules as an example:
-
If node is stationary, set communication link up for best speed while maintaining
QoS requirements.
-
Mobile node table must be updated at frequent intervals to ensure the distance to
the mobile node is up to date.
-
If node is mobile then check if distance to node changed during the previous τ
seconds and update the rate of distance change value accordingly.
-
If the rate of distance change of the node is greater than some limit α and the node
is moving away, then the node link should be set up for optimal range. If the rate
of change did not pass the α limit or did and is moving towards the coordinator,
then the link should be set up for best speed.
The bit error rate (BER) performance measurement can be used. From the previous results
obtained in this chapter a lookup table on the coordinator can be used to ensure the link is
set up with the “best performance” configuration parameters. It would even be more
intelligent if the coordinator could create such a lookup table itself using a neural network
to train itself about its communication environment.
Received signal strength (RSS) measurements can also be used to give an indication about
the quality of the link to each node and to determine latency.
Another useful measurement is the signal-to-noise ratio (SNR) which can be obtained by
dividing the known transmit power with the current noise power.
If serious data bit rates are required and the nodes are close by, the coordinator can set the
link up with FEC disabled, thereby allowing higher throughput because at close distances
the BER is so good that FEC is not necessary.
Furthermore, if a coordinator node does not have power constraints it can even talk to each
node or different groups of nodes on a different channel. The viability of such hopping
between channels will have to be carefully investigated but it does offer some advantages.
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An obstacle, as illustrated in the link to Node1, might pose a problem when QoS
requirements are very stringent. Take the previous experiments with a GhassemzadehNLOS channel model as an example – it showed that the only way a high BER such as 1e6 can be guaranteed is when channel 0 is used for communication with PRF of 3.90MHz
and bit rate of 110kbps. Clearly if the coordinator could talk to Node1 using these
configuration parameters it would be beneficial especially if Node1 were to become mobile
and move even further away.
Some nodes might have very precise ranging requirements for which hopping to one of the
channels with the higher bandwidth is necessary.
The proposed approach does have its setbacks, particularly with the fact that wireless
sensor nodes are deployed in large quantities, have very tight power requirements and
processing overheads are usually avoided. Still, it allows for an interesting discourse and
something to keep in mind as sensor nodes become more and more powerful.
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Chapter 8
8.Conclusion
This final chapter presents the conclusions drawn from the knowledge obtained out of the
various literature studies, software implementations and network simulation experiments
performed over the course of this project. The chapter ends with future work contributions
that will benefit the research community.
8.1
CONCLUSION
As part of this project an in-depth study into the inner workings of the IEEE 802.15.4a
UWB physical layer were done. The resulting work should provide an excellent stepping
stone for anyone who requires an introduction into the concepts of UWB and especially the
low-rate UWB physical layer specified by the IEEE 802.15.4a standard.
The Mixim-UWB framework proved an excellent choice for simulating the IEEE
802.15.4a UWB physical layer.
Using the modifications made to the Mixim-UWB framework, several simulation results
were obtained. From these results the following discoveries were made:
-
A higher bit rate results in a higher BER.
-
The longer the communication range the higher the BER.
-
A high PRF performs better than a lower PRF given certain conditions.
-
The channel center frequency effects performance because a higher center
frequency are more susceptible to noise and other attenuations.
-
A large bandwidth allows more noise to be introduced. It also spreads the power
more making interference with other wireless technologies less of a problem. The
extra bandwidth also allows for more precise ranging measurements to be done.
-
For communication over large distances the FEC does not help much in improving
the BER due to the fact that too many errors exist to correct. At lower BERs the
FEC are useful and avoids having to retransmit.
Chapter 8
-
Conclusion
An “intelligent node” approach, where a node has cognitive abilities allowing it to
learn about its environment and channel can be very beneficial allowing for
communication parameters to be dynamically adapted to best suit current
requirements. If data throughput is not a concern, the “intelligent node” can
automatically adapt and change its communication parameters to achieve very large
communication distances with a low BER. If distance is not a concern and the node
is not mobile, the link to the node can be set for optimal data throughput.
8.2
8.2.1
FUTURE WORK\CONTRIBUTIONS
Compliant UWB pulse
The triangular waveform used is not according to specification. More accurate waveforms
that meet the FCC power requirements can be investigated.
8.2.2
802.15.4 MAC
The Mixim-UWB model does not implement the MAC as specified by the IEEE 802.15.4
standard. Because Zigbee makes use of the same MAC, a lot of useful research has been
done on the 802.15.4 MAC.
Feng Chen [77] implemented an 802.15.4 MAC for OMNET++ using the INET
framework. It will be very useful if this can be integrated into the Mixim-UWB model.
8.2.3
PHR
The physical header part can be added to the UWB symbol and the receiver updated to
support it. The PHR information will aid an 802.15.4a MAC implementation.
8.2.4
Correlation receiver
Implementation of a correlation receiver for Mixim-UWB simulation purposes will prove
very beneficial to research and will make for a good research topic.
8.2.5
Ranging
Ranging as specified by the 802.15.4a standard can also be incorporated into the MiximUWB model.
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Chapter 8
8.2.6
Conclusion
Cognitive UWB Impulse Radio
A very interesting topic if the “intelligent node” employing the “best performance”
adaptive techniques mentioned in this document is carefully considered and tested for its
viability in wireless sensor networks.
8.2.7
TOSSIM implementation
It would be of great value if the IEEE 802.15.4a UWB PHY layer simulation work
discussed and presented in this paper is ported to the TOSSIM environment. This will
allow for real time testing of the concepts and algorithms on real hardware motes.
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Addendum A
A.Source code
This section is intended to provide help on the navigation and arrangement of the software
source and additional documentation on the DVD provided with this master dissertation.
IMPORTANT NOTE
All scripts, programs, and scenario files provided in the software DVD
have undergone innumerable alterations to suite the simulation
requirements. The software is provided “as is”. Use at own risk.
Licensing information about the different source components is provided
on the disc. Mostly the license terms and conditions are for
noncommercial settings – at academic institutions for teaching and
research use, and at non-profit research organizations.
A.1 FOLDER STRUCTURE
/doc
/dissertation
/endnote
/figures
/reference_material
/omnetpp-4.0
/win_install
/linux_src
/mixim-uwb-original
/mixim-uwb-modified
/matlab
/results
/2nodes
/10nodes
A.1.1 Documentation
/dissertation
A copy of this dissertation, its figures and reference table.
/reference_material Electronic copies of some research and conference papers
used during literature studies.
A.1.2 OMNET++
win_install
Windows installation for OMNET++ v4.0.
linux_src
Compressed OMNET++ v4.0 source archive.
mixim-uwboriginal
Original Mixim-UWB framework before changes was made.
mixim-uwboriginal
Modified Mixim-UWB with changes discussed in this
document.
A.1.3 Matlab
/.
Matlab scripts used to generate UWB pulses, pulse trains and
frequency response graphs used in this document.
A.1.4 Results
/2nodes
Results of 2 nodes scenario.
/10nodes
Results of 10 nodes scenario.
Addendum B
Service primitives
Addendum B
B.Service primitives
This addendum provides a brief overview of the concept of service primitives. Refer to
ISO/IEC 8802.2 [78] for more detailed information.
B.1 SERVICES AND PRIMITIVES
A service is a set of functions, known as primitives that a layer provides to the layer above
it. To specify a service the information flow between the layers (service users and service
providers) are described. The information flow is modeled by discrete, instantaneous
events consisting of a service primitive that is passed from one layer to the next through a
Service Access Point (SAP). Hence, to describe the service one describe the service
primitives and parameters that characterize the service. A service may have one or more
primitives and each primitive may have zero or more parameters to convey information
required for its service.
Primitives are calling functions that manage communication between adjacent protocol
layers within a communication node. Primitives perform various actions and typical
examples of primitive names include: Get, Set, Connect, Disconnect, Send, Receive,
Listen, Data, and Scan. There are four types of primitives listed in Table B-1.
Table B-1: Four service primitive types
Type
Description
request
Sent by layer (N + 1) to layer N.
Sent to request a service and passes any required parameters.
indication
Sent by layer N to layer (N + 1)
Sent in return to a (N + 1) request or to indicate an internally N-layer
initiated action.
response
Sent by layer (N + 1) to layer N.
Sent in reply to an indication. It may acknowledge with the results of an
Electrical, Electronic and Computer Engineering
160
Addendum B
Service primitives
action previously invoked by an indication primitive from layer N.
confirm
Sent by layer N to layer (N + 1).
Sent to acknowledge with the results of a previous layer (N + 1) request.
The concept of service primitives is illustrated in Figure B-1.
Figure B-1: Communication of primitives between peer protocol entities
A protocol is set of rules determining the format and transmission of data in the form of
frames, packets, or messages within a layer. The implementation of the service is defined
by the protocol and is only visible to the provider of the service. Service primitives specify
only the provided service to the service user and not the implementation thereof.
Electrical, Electronic and Computer Engineering
161
Addendum B
Service primitives
B.2 DATA UNITS
B.2.1 Service Data Unit
A Service Data Unit (SDU) is the set of data that is sent by a layer to the layer below it. In
other words, it is the set of data passed to a layer from the user who makes use of the
services provided by that layer. The lower layer does not understand the data in the SDU
and treats it as payload.
B.2.2 Protocol Data Unit
Each protocol layer adds to the SDU certain data and additional information that is
required for the layer to perform its function, a process known as encapsulation. The SDU
received from the higher layer together with this additional information the layer adds to it,
constitutes the protocol data unit (PDU) at this layer.
Again, if it is not the lowest layer, this PDU will be passed as a SDU to the next lower
layer who does not understand the structured information of the SDU and only delivers it
onward. Only the peer layer at the destination will understand the data. The peer layer will
reverse the process by decapsulating the data unit it gets from the layer below to extract the
information that was added. The information might be a port number, network address,
error checking information, etc. the layer needs to carry out its function.
To summarize, the PDU at layer N is the SDU of layer N-1. This SDU at layer N-1 is the
payload of the PDU for layer N-1.
Electrical, Electronic and Computer Engineering
162
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