Engr. Zeeshan Sabir Supervisor: Prof. Dr....
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PERFORMANCE ENHANCEMENT OF WIRELESS MOBILE ADHOC NETWORKS THROUGH IMPROVED ERROR CORRECTION AND CHANNEL ESTIMATION STRATEGY By: Engr. Zeeshan Sabir Supervisor: Prof. Dr. Mohammad Inayatullah Khan Babar Thesis submitted to the faculty of Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan in partial fulfillment of the requirements for the award of degree of Doctor of Philosophy in Electrical Engineering AUGUST 2013
Transcript of Engr. Zeeshan Sabir Supervisor: Prof. Dr....
PERFORMANCE ENHANCEMENT OF WIRELESS MOBILE ADHOC
NETWORKS THROUGH IMPROVED ERROR CORRECTION AND
CHANNEL ESTIMATION STRATEGY
By:
Engr. Zeeshan Sabir
Supervisor:
Prof. Dr. Mohammad Inayatullah Khan Babar
Thesis submitted to the faculty of Department of Electrical Engineering, University of
Engineering and Technology, Peshawar, Pakistan in partial fulfillment of the
requirements for the award of degree of Doctor of Philosophy in Electrical Engineering
AUGUST 2013
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ABSTRACT
PERFORMANCE ENHANCEMENT OF WIRELESS MOBILE ADHOC
NETWORKS THROUGH IMPROVED ERROR CORRECTION AND
CHANNEL ESTIMATION STRATEGY
By
Engr. Zeeshan Sabir
Mobile Adhoc Networks (MANET) refer to an arrangement of
autonomous wireless mobile nodes that show the tendency of freely and
dynamically self-organizing into arbitrary and temporary network
topologies. A variety of protocols have been implemented in MANET at the
Network layer which tend to show different performance in various
environments. Three of the most commonly used protocols at the Network
Layer in MANET are Destination Sequenced Distance Vector (DSDV)
Routing Protocol, Dynamic Source Routing (DSR) Protocol and Adhoc On-
Demand Distance Vector (AODV) Routing Protocol. A comprehensive
study on the performance evaluation of these three routing protocols have
been given in this thesis basing upon the TCP window size using Network
Simulator (NS-2.35) with two different types of network traffics. Tool
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Command Language (TCL) scripting is used to simulate the environment.
Orthogonal Frequency Division Multiplexing (OFDM) is the foremost
choice for MANET system designers at the Physical Layer due to its
inherent property of high data rate transmission that corresponds to its
spectral efficiency. One of the problems inherent in OFDM includes its
sensitivity to synchronization errors (frequency offsets and symbol time).
Most of the present day techniques employing OFDM for data transmission
support mobility as one of the primary feature. This mobility causes small
Channel Frequency Offsets (CFO) owing to the production of Doppler
frequencies. CFO tends to degrade the signal quality making the system
design unsuitable for many error sensitive applications. In this work two
efficient pilot-assisted channel estimation strategies have been implemented
in the proposed model of OFDM. The implemented solutions for channel
estimation include Zero Forcing algorithm and modified Least Square
channel estimation algorithm. Both these algorithms have been implemented
into the proposed environment of OFDM using two different types of pilot
insertion methods i.e. block-type and comb-type pilot insertion techniques.
Both these techniques have been compared amongst each other and with the
already published work as well.
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Another serious problem faced by the OFDM based transmission
systems is the sensitivity to the noise effects induced by the channel and
system. These noise effects tend to increase the BER of the system making it
unsuitable for many real-time applications. Turbo Codes have been
integrated with the proposed model of OFDM which have the tendency to
work in the Forward Error Correction (FEC) manner by not only identifying
the erroneous bit locations but also correcting them thus using simplex
control information link. The turbo codes have been implemented using
parallel concatenation of Recursive Systematic Convolutional (RSC) Codes
that tend to introduce redundant information into the user bits in order to
mitigate the effects of channel induced noise from the received OFDM
symbols. Results have been shown using MATLAB® simulation for
changing number of iterations of MAP decoder for five different modulation
schemes and are compared. The channel, through which the signal has been
passed, is simulated using Stanford University Interim Channel Model
parameters. These Channel models are six in number and depict three
different real outdoor environments including rural, urban and hilly terrains
having low, moderate and high tree densities.
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DEDICATION
Dedicated to my Parents, Wife and Sons
Zohair, Zain and Ahmad Abdullah
for their love, endless support and encouragement which enabled me to
complete this undertaking in time.
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ACKNOWLEDGEMENT
First and foremost, I would like to thank Allah who gave me the privilege
and grace to complete this study in-time in spite of many challenges faced.
The journey has been quite remarkable and is a unique stepping stone to
many exploits ahead.
Then I wish to express most sincere gratitude and appreciation to my
Supervisor Prof. Dr. Mohammad Inayatullah Khan Babar, Chairman
Department of Electrical Engineering, University of Engineering and
Technology, Peshawar, Pakistan for his continuous guidance, patience and
encouragement throughout this process. I‘ll always remain indebted for his
extraordinary and wholehearted support.
I would also like to wish deepest thanks to my Research Evaluation
Committee Members Prof. Dr. Shahid Khattak (COMSATS Abbottabad),
Prof. Dr. Syed Waqar Shah (U.E.T Peshawar), Dr. Riaz Ul Hasnain (U.E.T
Abbottabad Campus) and Dr. Mohammad Ali Shah (ICCC, Pakistan Atomic
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Energy Commission, Islamabad) for their visionary support, unwavering
guidance and all-time availability throughout the course of this work.
My parents, wife, sister and brother were always been a source of
encouragement for me. The three gifts that I received from God in the form
of Zohair, Zain and Ahmed Abdullah during the PhD period , were motives
of inspiration for me in moving ahead and overcome the obstacles in the
way. My family members, especially wife, sacrificed a lot and managed the
family matters during my absence from the home and late night sittings in
the Research Centre during the PhD phase for which I am extremely grateful
to all of them.
Finally, my thanks go to all the people who have supported me to
complete the research work directly or indirectly.
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TABLE OF CONTENTS
1 Introduction……………………………………………………….………………….1 1.1 General …………………………………………………………………………1 1.2 Problem Statement .............................................................................................. 3 1.3 Objectives and Scope .......................................................................................... 4 1.4 Review of Previous Work ................................................................................... 6
1.5 Original Contribution………………………………………………………...…9 1.6 Organization of Thesis…………………………………………………..……..10 2 Wireless Mobile Adhoc Networks-An Introduction ................................................. 13
2.1 Introduction ....................................................................................................... 13 2.2 Two Major Types of MANETs ......................................................................... 14
2.2.1 Infrastructure-based Mobile Adhoc Network……………………………14 2.2.2 Infrastructure less Mobile Adhoc Network………………………………16
2.3 A Review of MANET Routing Protocols ......................................................... 17 2.3.1 Adhoc On-Demand Distance Vector (AODV)Routing Algorithm……...18 2.3.2 Dynamic Source Routing (DSR) Routing Algorithm………………...….19 2.3.3 Destination Sequenced Distance Vector (DSDV) Routing Algorithm….19
2.4 A Few MANET Based Network Technologies ................................................ 20 2.4.1 IEEE 802.11 a/b/g/n ………………………………………………….….21 2.4.2 Bluetooth Wireless Technology …………………………………………24 2.4.3 HiperLAN/1 and HiperLAN/2 …………………………………………..27 2.4.4 IEEE 802.15.3 ………………………………………………………….. 28
2.5 TCP Window Size Evaluation using NS-2 Simulator ..……………………....28 2.6 Summary ........................................................................................................... 37
3 OFDM Introdution and System Modeling ................................................................ 39 3.1 Introduction ....................................................................................................... 39 3.2 Single Carrier vs Multicarrier Transmission ..................................................... 40
3.2.1 History....................................................................................................... 42 3.2.2 Basis of Orthogonality in OFDM ............................................................. 43
3.3 Block Diagram of OFDM ................................................................................. 44 3.4 Downsides of OFDM System ........................................................................... 49
3.4.1 Sensitivity to Doppler Spread ................................................................... 49 3.4.2 Sensitivity to Delay Spread ....................................................................... 51 3.4.3 Sensitivity to Noise Effects ....................................................................... 54
3.5 OFDM Merits and Demerits ............................................................................. 56 3.5.1 Merits ……………………………………………………………………55 3.5.2 Demerits …………………………………………………………………56 3.6 Summary ………………………………………………………………...……57 4 Simulating Behaviour of Wireless Channel using SUI Channel Parameters………59
4.1 Introduction ....................................................................................................... 59 4.2 Wireless Propagation Parameters ..................................................................... 61
4.2.1 Reflection .................................................................................................. 61 4.2.2 Refraction .................................................................................................. 61
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4.2.3 Diffraction ................................................................................................. 62 4.2.4 Scattering .................................................................................................. 62 4.2.5 Absorption................................................................................................. 62 4.2.6 Polarization ............................................................................................... 63
4.3 Types of Wireless Channels.............................................................................. 63 4.3.1 AWGN Channel Model ............................................................................ 64 4.3.2 Multipath Rician Fading Channel Model ................................................. 65 4.3.3 Multipath Rayleigh Fading Channel Model ............................................. 66
4.4 Simulating SUI Channel Models ...................................................................... 67 4.4.1 Parameters of SUI Channel Models .......................................................... 68
4.5 Summary ........................................................................................................... 72 5 Proposed Algorithms for Channel Estimation And Equalization ............................. 73
5.1 Introduction ....................................................................................................... 73 5.2 Proposed Algorithms for Channel Estimation/Equalization ............................. 74
5.2.1 Modified Least Square (LS) Channel Estimation Algorithm ................... 75 5.2.2 Modified Frequency Domain Zero Forcing (ZF) Channel Estimation Algorithm .................................................................................................. 78
5.3 Pilot Insertion Techniques and Their Effects .................................................... 81 5.3.1 Block-type Pilot Insertion Method............................................................ 81 5.3.2 Comb-type Pilot Insetion Method ............................................................. 83 5.3.3 Diagonal Pilot Insertion Method ............................................................... 85 5.3.4 Two Dimensional Pilot Insertion Method ................................................. 86
5.4 Summary ........................................................................................................... 87 6 Channel Coding ........................................................................................................ 88
6.1 Introduction ....................................................................................................... 88 6.2 Classification of Channel Coding ..................................................................... 90 6.3 Turbo Codes-Brief History ............................................................................... 91 6.4 Structure of Turbo Codes .................................................................................. 92
6.4.1 Implementation Details of Turbo Encoder ............................................... 93 6.4.1.1 Trellis Diagram for the Implemented Encoder Structure .................. 95 6.4.2 Turbo Decoding ........................................................................................ 97 6.4.2.1 Maximum A Posteriori Decoding Algorithm .................................... 98 6.4.2.2 Soft Output Viterbi Algorithm (SOVA) .......................................... 106
6.5 Summary ......................................................................................................... 108 7 Simulated Models and Results ................................................................................ 109
7.1 Introduction..………………………………………………………………...109 7.2 Simulated Model……………………………………………………………..108 7.3 Simulation Results for Proposed Model of Turbo-Coded/Un-coded OFDM with Frequency-Domain Pilot-Assisted Block-Type Zero-Forcing Channel Estimation Strategy Through Multipath Rayleigh Fading Channel ............... 113 7.4 Simulation Results for Proposed Model of Turbo-Coded/Un-coded OFDM with Frequency-Domain Pilot-Assisted Block-Type Zero-Forcing Channel Estimation Strategy ......................................................................................... 119 7.5 Simulation Results for Proposed Model of Turbo-Coded/Uncoded OFDM with Frequency-Domain Pilot-Assisted Block-Type Modified Least Square Channel Estimation Strategy ......................................................................................... 140
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7.6 Simulation Results for Proposed Model of Turbo-Coded/Uncoded OFDM with Frequency-Domain Pilot-Assisted Comb-Type Zero-Forcing Channel Estimation Strategy ......................................................................................... 157 7.7 Simulation Results for Proposed Model of Turbo-Coded/Uncoded OFDM with Frequency-Domain Pilot-Assisted Comb-Type Modified Least Square Channel Estimation Strategy ......................................................................................... 168 7.8 Summary ......................................................................................................... 189
8 Conclusions And Future Prospects ......................................................................... 190 8.1 Overview…...………………………………………………………………...190 8.2 Achievements….……………………………………………………………..191 8.3 Limitations ...................................................................................................... 191 8.4 Future Work .................................................................................................... 192
REFERENCES.…………………………………………………………………..……193 APPENDIX-A................................................................................................................. 202 PUBLICATIONS ............................................................................................................ 204
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LIST OF FIGURES
Figure 2.1 An Infrastructure Based Wireless Mobile Adhoc Network ............................ 14 Figure 2.2 Architecture of Infrastructure Less Wireless Mobile Adhoc Network ........... 16 Figure 2.3 Overview of Data Rate and Range for Different Wireless Technologies ....... 21 Figure 2.4 Comparison of OSI and IEEE 802 Reference Models ……………………....22 Figure 2.5 Comparison of OSI Reference Model and Bluetooth Protocol Stack.……….26 Figure 2.6 TCP Window Size Evaluation for DSDV Protocol ……...………………..…34 Figure 2.7 TCP Window Size Evaluation for DSR Protocol ............................................ 34 Figure 2.8 TCP Window Size Evaluation for AODV Protocol ........................................ 36 Figure 2.9 Number of TCP Packets Successfully Delivered to the Destination by the Three Reference Protocols ............................................................................... 37 Figure 3.1 An Envelop of OFDM Subcarriers. ................................................................. 40 Figure 3.2 Comparison Between Conventional Multicarrier And Orthogonal Multicarrier Transmission System ....................................................................................... 41 Figure 3.3 Block Diagram for OFDM based System ....................................................... 45 Figure 3.4 Channel Frequency Offset for a subcarrier of OFDM symbol Turbo Decoder ............................................................................................................ 50 Figure 3.5 101101 sequence that is to be sent. The dashed line shows actual transmitted shape ................................................................................................................ 52 Figure 3.6 Received sequence of the transmitted shown in figure 3.5 ............................. 52 Figure 3.7 Cyclic Prefix Insertion in an OFDM Symbol .................................................. 53 Figure 3.8 Constellation Map showing effect of noise on different modulation schemes for the proposed model. ................................................................................... 55 Figure 4.1 A Typical Example of Multipath Phenomenon ............................................... 60 Figure 5.1 A Broad Classification of Different Channel Estimation Techniques ............ 74 Figure 5.2 Modified LSE Channel Estimator ................................................................... 76 Figure 5.3 Modified Zero Forcing Channel Estimator ..................................................... 81 Figure 5.4 Pictorial Depiction of Block-Type Pilot Insertion Method ............................. 82 Figure 5.5 Pictorial Depiction of Comb-Type Pilot Insertion Method ............................. 84 Figure 5.6 Schematic View of Diagonal Pilot Insertion Method ......................................86 Figure 5.7 Schematic View of Two Dimensional Pilot Insertion Method .......................86 Figure 6.1 A rate 1/3 PCCC Turbo Encoder ..................................................................... 93 Figure 6.2 The eight (08) Possible Encoder States for the Constraint Length 3 RSC Encoder ............................................................................................................ 95 Figure 6.3 Trellis diagram for implemented RSC Encoder .............................................. 96 Figure 6.4 Trellis State Diagram for Information Bit Stream 10110101 Through First Encoder ............................................................................................................ 96 Figure 6.5 Coded bits Through First Component Encoder ............................................... 97 Figure 6.6 Structure of the implemented Turbo MAP Decoder based upon two Component Decoders ....................................................................................... 99 Figure 6.7 State Diagram for Turbo MAP Decoding Algorithm .................................... 100
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Figure 6.8 Step-by-Step Information Exchange Between The Two Component MAP Decoders ....................................................................................................... 103 Figure 7.1 Proposed Model of Turbo-Coded OFDM with modified Channel Estimation/Equalization Techniques ............................................................ 110 Figure 7.2 Performance of the Proposed Model with Uncoded OFDM and Proposed Zero-Forcing Channel Estimation Through Multipath Rayleigh Fading Channel Estimation/Equalization Techniques. ............................................. 114 Figure 7.3 Performance of the Proposed Turbo-Coded OFDM Model with BPSK Modulation Scheme and Modified Zero-Forcing Channel Estimation Through Multipath Rayleigh Fading Channel .............................................. 115 Figure 7.4 Performance of the Proposed Turbo-Coded OFDM Model with QPSK Modulation Scheme and Modified Zero-Forcing Channel Estimation Through Multipath Rayleigh Fading Channel .............................................. 116 Figure 7.5 Performance of the Proposed Turbo-Coded OFDM Model with 16-QAM Modulation Scheme and Modified Zero-Forcing Channel Estimation Through Multipath Rayleigh Fading Channel .............................................. 118 Figure 7.6 Performance of the Proposed Turbo-Coded OFDM Model with 64-QAM Modulation Scheme and Modified Zero-Forcing Channel Estimation Through Multipath Rayleigh Fading Channel .............................................. 118 Figure 7.7-7.12 Performance of the Proposed Model with Uncoded OFDM and Block-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Models ................................................................................................ ..119-122 Figure 7.13-7.18 Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And Block-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Model .................................... ................................. ..123-126 Figure 7.19-7.24 Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And Block-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Model.................................... ................................... ..127-129 Figure 7.25-7.30 Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And Block-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Model.................................... ................................... ..130-133 Figure 7.31-7.36 Performance of the Turbo-Coded OFDM With 32-QAM Modulation Scheme And Block-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Model.................................... ................................... ..134-136 Figure 7.37-7.42 Performance of the Turbo-Coded OFDM with 64-QAM Modulation Scheme And Block-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Model .................................... ................................. ..137-139 Figure 7.43 Performance of the Proposed Model with Uncoded OFDM and Presented Block-Type LSE Channel Estimation Through SUI-1 Channel Model ..... ..141 Figure 7.44-7.49 Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And Block-Type LSE Channel Estimation Through SUI-1 Channel Model .................................... ............................................................. ..141-144 Figure 7.50-7.55 Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And Block-Type LSE Channel Estimation Through SUI-1 Channel Model .................................... .............................................................. ..145-147 Figure 7.56-7.61 Performance of the Turbo-Coded OFDM with 16-QAM Modulation
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Scheme And Block-Type LSE Channel Estimation Through SUI-1 Channel Model .................................... .............................................................. ..148-150 Figure 7.62-7.67 Performance of the Turbo-Coded OFDM with 32-QAM Modulation Scheme And Block-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Model .................................... ................................. ..151-153 Figure 7.68-7.73 Performance of the Turbo-Coded OFDM with 64-QAM Modulation Scheme And Block-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Model .................................... ................................. ..154-156 Figure 7.74 Performance of the Uncoded OFDM with Comb-Type Zero-Forcing Channel Estimation Through SUI-1 Channel Model ................................................ ..157 Figure 7.75-7.80 Performance of the Turbo-Coded OFDM With BPSK Modulation Scheme Using Comb-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Models ..................................................................... ..158-161 Figure 7.81-7.86 Performance of the Turbo-Coded OFDM With QPSK Modulation Scheme Using Comb-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Models ..................................................................... ..162-165 Figure 7.87 Performance of the Turbo-coded OFDM with 16-QAM Modulation Scheme Using Comb-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Models ............................................................................ ..166 Figure 7.88 Performance of the Turbo-coded OFDM with 32-QAM Modulation Scheme Using Comb-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Models ............................................................................ ..167 Figure 7.89 Performance of the Turbo-coded OFDM with 64-QAM Modulation Scheme Using Comb-Type Zero-Forcing Channel Estimation Through SUI 1-6 Channel Models ............................................................................ ..167 Figure 7.90-7.95 Performance of the Proposed Model with Uncoded OFDM and Presented Comb-Type LSE Channel Estimation Through SUI 1-6 Channel Models.................................................................................................. ..169-172 Figure 7.96-7.101 Performance of the Turbo-coded OFDM with BPSK Modulation Scheme Using Comb-Type LSE Channel Estimation Through SUI 1-6 Channel Models ..................................................................... ..172-175 Figure 7.102-7.107 Performance of the Turbo-coded OFDM with QPSK Modulation Scheme Using Comb-Type LSE Channel Estimation Through SUI 1-6 Channel Models ..................................................................... ..176-179 Figure 7.108-7.113 Performance of the Turbo-coded OFDM with 16-QAM Modulation Scheme Using Comb-Type LSE Channel Estimation Through SUI 1-6 Channel Models.................................... ................................. ..179-182 Figure 7.114-7.119 Performance of the Turbo-coded OFDM with 32-QAM Modulation Scheme Using Comb-Type LSE Channel Estimation Through SUI 1-6 Channel Model................................... .................................... ..182-185 Figure 7.120-7.125 Performance of the Turbo-coded OFDM with 64-QAM Modulation Scheme Using Comb-Type LSE Channel Estimation Through SUI 1-6 Channel Model................................... .................................... ..185-188
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LIST OF TABLES
Table 2.1 Key Feature for different Industrial Standards based upon IEEE 802.11 Specifications .................................................................................................... 23 Table 2.2 PHY Specifications for IEEE 802.11 Standard. ............................................... 24 Table 2.3 Technical Specifications for BluetoothV2.1 ..................................................... 27 Table 2.4 Simulation Parameters for Evaluating Performance of Different Protocols in NS-2 Environment. ........................................................................................... 29 Table 4.1 SUI 1-6 Channel Model Parameters. ................................................................ 70 Table 4.2 Underlying Scenario for Calculating SUI Channel Model Parameters. ........... 70 Table 6.1 Calculating Computational Complexity of MAP Algorithm .......................... 104
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LIST OF ABBREVIATIONS
MANET Mobile Adhoc Networks
AP Access Point
MAC Medium Access Control
BSA Basic Service Areas
MD Mobile Devices
AODV Adhoc On-Demand Distance Vector
DSR Dynamic Source Routing
DSDV Destination Sequenced Distance Vector
RRep Route Reply
RReqs Route Request
RErr Route Error
TCP Transmission Control Protocol
OFDM Orthogonal Frequency Division Multiplexing
IP Internet Protocol
WLAN Wireless Local Area Network
OSI Open Systems Interconnection
LLC Logical Link Control
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CCK Complementary Code Keying
Wi-Fi Wide-Fidelity
CAN Campus Area Network
PAN Personal Area Network
SDP Service Discovery Protocol
GMSK Gaussian Minimum Shift Keying
ETSI European Telecommunications Standards Institute
BPSK Binary Phase Shift Keying
QPSK Quadrature Phase Shift Keying
QAM Quadrature Amplitude Modulation
QOS Quality Of Service
RSC Recursive Systematic Convolutional
MAP Maximum A Posteriori
SUI Stanford University Interim
AWGN Additive White Gaussian Noise
LTE Long Term Evolution
DAB Digital Audio Broadcasting
DVB Digital Video Broadcasting
ICI Inter Carrier Interference
IBI Inter Block Interference
WiMAX Wide Interoperability of Microwave Access
FFT Fast Fourier Transform
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Chapter-1
INTRODUCTION
1.1 General
In the recent years wireless communications has permeated every walk of life due
to its vast reach and ease of access. With the increasing usage of wireless systems,
the demand for better services on the part of the end user has increased. The
increasing demand of the wireless communications has seen a span starting from
voice connections and internet to military and warfield.
Wireless Mobile Adhoc Networks (MANET) are used to set up a
communication link between mobile nodes in an improvised environment without
the need of any dedicated administration [1]. In most of the cases MANETs are
deployed in the most adverse environments where a central base station, as
necessary in cellular network, is not possible to be installed. Thus a direct
communication between the nodes is necessary in which each node has obligation
to work as a router as well for forwarding the packets to the intended destination
thus using its battery power and shortening its overall life. OFDM is applied at the
physical layer for MANETs which can utilize the available frequency spectrum in
the most efficient manner. But there are some problems associated with OFDM
which include its sensitivity to Channel Induced Noise effects and fading
phenomenon which disturbs the orthogonality of the subcarriers of the OFDM
symbol.
Two time-dependent waveforms Am(t) and Bn(t) are deemed to be
orthogonal if these fulfill the orthogonality principle i.e.
2
nmfordttBtAT
T
T
nm
0)()(2
1 (1.1)
over the interval 2T which is the common repetition period[2]. If these two
waveforms are taken as the two subcarriers of OFDM symbol then graphically this
condition is satisfied if the peak of subcarrier is aligned with nulls of other
subcarriers of same OFDM symbol.
The noise generated into the channel due to different means including
AWGN noises, galactic noises, electric transient noises, thermal noises etc can
severely disturb the received signal’s quality thus affecting the system’s
performance.
The basic motive of the thesis is to propose such a model of OFDM which
should have the capability of mitigating the effects of fading and channel induced
noise effects in the most efficient manner. In this regard Forward Error Correcting
(FEC) turbo codes are integrated into the proposed OFDM model which works in a
forward error correction manner by not only identifying the erroneous bit locations
but also correct them thus requiring simplex control information link. The
improved turbo codes increases the immunity of the system against the channel
induced noise effects improving the system’s BER performance.
Similarly for increasing the capability of the proposed model against the
fading effects, two different modified channel estimation algorithms have been
integrated into the model which makes the proposed model capable to perform
well in fading conditions as well. The proposed channel estimation algorithms
works with the help of the information gathered from the pilot signals sent prior to
the user data into the channel. Based upon the information gathered from the pilot
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data, channel is estimated which is later used to equalize the upcoming data of the
user on symbol to symbol basis. Stanford University Interim channel models (1-6)
are used to check the system’s performance.
1.2 Problem Statement
Orthogonal Frequency Division Multiplexing (OFDM) is used at the physical layer
for OFDM due to its inherent property of high data rate transmission which
emanates from its lofty spectral efficiency. But first of the two most serious
problem associated with OFDM is its sensitivity to the channel induced fading
effects which disturbs the orthogonality of the OFDM subcarriers. This loss of
orthogonality adversely effects the overall performance of the system. Efficient
channel estimation strategy is proposed in this thesis, when integrated into the
OFDM model mitigates the effect of fading from the received signal. The channel
is estimated using two modified channel estimation algorithms namely Zero
Forcing channel estimation algorithm and modified Least Square channel
estimation algorithm. Both these algorithms are tested in the proposed environment
using different pilot insertion techniques which cast effects on the performance of
the system.
The second problem associated with the OFDM based transmission systems
is its sensitivity to the channel induced noise effects which deteriorates the BER
performance. In order to cater for channel induced noise effects, an improved
forward error correcting turbo codes are integrated with the proposed model which
identify and corrects the erroneous bit. Both the turbo encoder and decoder are
designed in the most improved form and then are integrated into the proposed
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model to improve its error rate performance.
1.3 Objectives and Scope
OFDM is a transmission scheme, which splits up a large data stream, by sending
the data symbols simultaneously over a set of parallel data sub-carriers. OFDM
based transmission systems are prone to some adverse channel effects two of these
effects are channel fading and noise effects. Both these phenomenon tend to
negatively effect the quality of the finally received signal.
The basic objective of the thesis is to address these two problems of OFDM.
In this regard, the first problem i.e. fading phenomenon is compensated at the
receiver side by introducing efficient channel estimation strategy into the proposed
model of OFDM using two proposed channel estimation algorithms. Both these
algorithms are implemented using pilot aided method in which channel estimation
matrix is used for equalizing the channel impulse response effects. Two different
types of pilot insertion methods, which are inherently used in many standards, are
used to test the performance of the proposed model which is tested with a number
of digital modulation schemes.
The second problem, noise effect, is addressed in the thesis, by the
integration of turbo codes. Turbo codes show an improved performance over the
competitors[3]. Decoding is performed via a pair of two component decoders
connected with each other via interleaver/deinterleaver set. Maximum a posteriori
algorithm is implemented at each of the component decoder to calculate the Log
Likilihood ratio of the a priori probability. The working of the component
decoders is carried out in the form of iterations. The final estimate regarding the
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decoded bit keeps on improving until a hard decision is carried out at the output
generated by the second component decoder. The basic scope of the thesis is to
work on the physical and data-link layer of the wireless mobile adhoc networks
and to look into improving the performance of the network at these lowest two
layers of the TCP/IP protocol suite.
1.4 Review of Previous Work
Some work has already been done in the proposed area. A number of different
approaches have been adopted for mitigating effects of ICI and error correction in
the OFDM model in various environments. In one of such approaches [4], a new
idea regarding self-cancellation scheme for ICI is proposed in which the diverse
weights have been given to the same data symbol and then modulating the alike
data symbols on different neighbouring subcarriers. At the receiving side, maximal
ratio combining algorithm is used to combine these multiple copies of the OFDM
symbol. The basic problem with this technique was the bandwidth wastage due to
the transmission of redundant data symbols on different subcarriers.
In [5] and [6] a time domain Lease Square DFT based algorithm is proposed.
Similarly in [7], the authors have proposed a Recursive Least Square (RLS)
channel estimation approach in which the same estimation matrix is used for
equalization. The computational complexity analysis show that the computations
involved in carrying out this equalization for the received symbols can results in
such a latency in the system that it can drag the performance of the system away
for its implementation for many real-time applications.
Another famous model has been proposed by Jeon et. al. [8] in which an ICI
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Cancellation algorithm has been proposed for OFDM system in which channel
impulse response variations were considered linear inside a symbol. And these
variations were considered varying on OFDM symbol-to-symbol basis. The results
shown in the paper reveals that the presented algorithm works well under the
assumption of static channel conditions for an OFDM symbol. But in the practical
environments, the supposition made in the paper might not work well since the
channel impulse response variations may or may not remains static or linear. This
condition is not addressed or discussed in this work. So there is a need of a channel
estimation algorithms which works well in fast fading environments as well.
In [9] authors have presented a channel estimation algorithm that works
inside the iterations of the turbo decoder for the Wireless LAN (802.11a) standard.
Results have been shown for a number of iterations of the decoder and it has been
proved that the proposed system works well for static parameters. But the
performance of the proposed algorithm degrades when mobility is introduced into
the system. This is because due to the mobility generation, doppler frequencies are
generated which degrades the system performance to a level at which the proposed
algorithm is unable to perform well due to the production of severe Inter-carrier-
interference between the subcarriers of OFDM symbol.
Similarly in [10] and [11], Sequential Interference Cancellation (SIC)
algorithm and Minimum Mean Square Estimation (MMSE) algorithms have been
applied as a part of multirate sampling theory in which the channel fading effects
are mitigated from the received OFDM symbols. Similarly the MMSE proposed in
[11] tends to investigate the correlation of a dispersive channel frequency response
over time-domain and frequency-domain. In [10] a BER performance of 10e-2 is
reported in the results at affordable SNR. In both these similar algorithms, the
7
computational complexity has been the basic issue which has been resulted in a
latency which suppresses its practical implementation for a number of applications
including the real-time services.
Another improved iterative receiver model has been proposed in [12] which
can jointly compensates the non-linearities in the channel coefficient estimates and
Channel frequency offsets (CFO) using the iterations of the turbo decoder. But the
results portion shows that the anticipated algorithm is practical only for the M-ary
modulaton schemes due to the complexity issue involved. Thus there is a need for
such an optimized receiver which should be capable enough for dealing with the
QAM (of different varieties) and other digital modulation schemes which are used
in different standards now-a-days.
Looking at the above facts about the already presented schemes for the
mitigation of channel impulse response effects and the noise effects from the
OFDM system, there was a need for such a robust system which should
incorporate the solutions for the problems faced by the already presented schemes.
In this thesis, we have presented such a scheme which is able to show good
performance with most of the digital modulation schemes with a much less system
overhead. Results have been shown for a number of digital modulation schemes in
various channels which depicts different practical environments.
1.5 Original Contribution
The thesis is suppose to contribute to the field of the multicarrier transmission
strategy in a number of ways. The contributions expected by the proposed
8
methodology into the environment of OFDM are given below.
a. Proposal of an efficient Channel estimation strategy namely frequency-
domain pilot-assisted modified Least Square channel estimation algorithm which
can mitigate the effects of channel induced fading with the help of pilot-aided
channel estimation approach.
b. Proposal of a frequency-domain pilot-assisted zero forcing channel
estimation scheme and its integration into the proposed model. Since pilot insertion
method has a great effect on the system’s performance therefore both the above
mentioned schemes has been implemented using two different pilot insertion
methods. Results have been shown for each of this pilot insertion method using six
different channel model which are based upon the parameters of Stanford
University Interim (SUI) Channel models. Similarly a comparison of the results of
both these channel estimation schemes have also been shown and discussed.
c. In order to mitigate the effect of channel induced noise, forward error
correcting turbo codes have been combined into the proposed model. The
combination of turbo codes with the above mentioned channel estimators have
been used for the first time for investigating the performance of the OFDM.
Comparison have been shown via BER vs SNR graphs for five different digital
modulation schemes. Comparisons have also been given for coded vs uncoded
OFDM system using the above mentioned two channel estimation algorithms.
Comparison of the proposed model performance through different SUI channel
models is also discussed in the results portion.
9
1.6 Organization of Thesis
The thesis is organized as follows:
Chapter-1 contains an introduction to the work being carried out in the thesis.
Starting from the introduction to the work, it covers the basic statement of the
problem addressed in the thesis including the scope/objectives of the work and the
original contributions to the area made by the work presented in the thesis.
Chapter-2 introduces the basis of Wireless Mobile Adhoc Networks (MANET)
and then gives introduction to some of the algorithms which are standardized in the
environment of MANET. The basic organization of MANET is discussed
alongwith some results of the simulated environment of MANET using three
routing protocols.
Chapter-3 gives an introduction to the Orthogonal Frequency Division
Multiplexing. It starts with a brief introduction and history of OFDM. Then the
basic block diagram of OFDM is explained. The basic problem associated with
OFDM are discussed which includes Inter-Carrier-Interference and Noise effects.
Chapter concludes with the advantages and disadvantages associated with this
multicarrier transmission technique.
Chapter-4 gives an insight to the simulated channel environment. The channel is
simulated based upon the parameters of Stanford University Interim (SUI) Channel
Model Parameters. These are six channel models and their pros and cons and
different parameters are discussed in this chapter with sufficient details.
Chapter-5 gives details of the proposed. Two pilot-aided algorithms have been
proposed for estimating the effects of the channel from the received OFDM
10
symbols. The pilot insertion technique, which cast a major impact on the overall
performance of the system is also discussed with an introduction to the different
pilot insertion techniques used in various standards have also been discussed. The
pros and cons of the two pilot insertion methods used with the proposed model
have also been discussed.
Chapter-6 deals with the channel coding used in this thesis. Forward error
correcting turbo codes have been thoroughly explained in this chapter. Sufficient
details have been given regarding the proposed modified turbo encoder and
decoder. Simulation details of the proposed modified encoder design which is
comprising of concatenated recursive convolutional codes have been given with
sufficient details. Turbo decoder which is comprising of component Maximum a
posteriori decoder is also mentioned with explanation regarding the decoding
algorithm. Contemporary decoding algorithm of MAP are also discussed with brief
explanation in this chapter.
Chapter-7 is dedicated for the discussion on the simulation results for the proposed
model. First of all the block diagram of the proposed model is discussed. Then the
results of proposed model have been shown in different environments with and
with out the aid of the channel coding and through different channel. Different
digital modulation schemes are tested with the proposed environment and the
results are shown and discussed with a comparison with the already published
work in this field.
Chapter-8 concludes the thesis by making a concise debate on the findings of the
thesis and then the future direction in which the research can be further extended to
produce valuable progress in the field.
11
13
Chapter-2
WIRELESS MOBILE ADHOC NETWORKS - AN INTRODUCTION
2.1 Introduction
Great advances have been witnessed since the last decade in the field of distributed
communication and computing technologies. The basic platform for advances in
these technologies have been formulated owing to the technological progress being
made in the field of miniaturization of low-power and low-cost system designs.
With these technological advancements a new kind of networks have been
emerged, termed as Mobile Adhoc Networks (MANET) [13].
Wireless Mobile Adhoc Networks, refers to an arrangement of wireless
mobile nodes that show tendency of freely and dynamically self-organizing into
arbitrary and temporary network topologies. In MANET environment,
communication between two nodes is directly possible only if they are in
eachother’s transmission range. If the two nodes are out of eachother transmission
range then the message has to traverse a number of hops amid reaching the
destination. During the travel of the packet to the destination, each hop, which
represents a node, have to work as a router as well by forwarding the packets in the
direction of the destination, thus utilizing its own battery and network resources.
The way packets are routed in the network depends primarily on the routing
protocol implemented into the system at the Network Layer. Due to the suitable
routing protocol implemented, MANETs show much auto-configurability and
adaptivity in different environments which facilitates flexible deployments in
14
various scenarios including military insurgencies, rescue services, industrial
applications, emergency situations and campus-wide networks[14][15]
2.2 Two Major Types of MANETs
Broadly speaking, MANETs are classified into two primary categories. Namely
Infrastructure-based and Infrastructureless MANETs. A brief introduction of both
these types of MANET categories in chronological order is given below.
2.2.1 Infrastructure-based Mobile Adhoc Network
Infrastructure-based wireless mobile adhoc networks can be considered as a
midway of the transaction from fully infrastructured network to totally adhoc
network in which each node acts independently as a router by forwarding the
packets to the intended destinations. A schematic representation of Infrastructure-
based wireless mobile adhoc network is shown in Fig. 2.1.
15
Fig 2.1: An Infrastructure Based Wireless Mobile Adhoc Network
The infrastructure-based wireless mobile adhoc networks are semi adhoc networks
in which the routing of the packets is controlled by a central entity called “Access
Point (AP)”. It is the access point through which all the nodes are connected
wirelessly and it controls the routing of packets between the different nodes of the
network[16]. Access Point is then connected to rest of the network cloud where the
packets get routed out of the network.
One of the biggest advantage of infrastructure-based routing is the savage of
power at the end of nodes since each node deals with its own data traffic and has
nothing to do with the rest of the network. Thus it saves its battery power which is
one of the major concerns in the MANET based architectures. Secondly, since the
routing of the packets is centralized, therefore the computational delay at the end
of each node is minimum as the routing table updates have to be managed by only
one entity i.e. Access point, while rest of the network is free from any such record
of routing table updates. Thus the computational delay on the part of each node is
minimized. The major disadvantage of infrastructure-based MANET is the “single
point of failure”. If the access point fails, whole of the network fails as the routing
of the packets is basing upon a single entity only. In order to solve this problem,
infrastructure less mobile adhoc networks concept got materialized.
IEEE 802.11 MAC provides several characteristics for the AP based adhoc
networks. These characteristics include roaming, channel synchronization, link
setup, power management, authentication, clear channel assessment just to mention
a few.
2.2.2 Infrastructure less Mobile Adhoc Network
16
In Infrastructure less mobile adhoc networks, there is no centralized entity to
control the routing of packets, instead each node acts independently to work as a
router by forwarding the packets to the intended destination. An architectural view
of Infrastructure less Wireless Mobile Adhoc Networks is shown in Fig. 2.2. The
Mobile Devices (MD) in Fig. 2.2 are connected directly to eachother without the
support of any fixed entity for controlling the packets routing. In the type of
architecture shown in Fig. 2.2, each node forward packets by acting as router and
forwards packet to the intended destination. In order to do so, every node has to
keep the record of routing table updates in its cache memory. This record of
routing table entries and their accession at the times of selection of appropriate
route for a packet puts some extra load on the system in terms of computational
complexity and thus latency. This reduces the overall battery life since the life of a
route in a network is a concave constraint of the shortest battery life of any node
present on the route and taking part in packet forwarding.
Fig 2.2: Architecture of Infrastructure Less Wireless Mobile Adhoc Network
The biggest advantage of this type of systems is its fully distributed approach for
17
packet handling. Means due to absence of a single point-of-failure, the network
overall packet routing efficiency increases tremendously. Packets can choose,
amongst a number of routes, their intended destination for going towards
destination. If any of the node dies out, routing table updates are sent to rest of the
network mentioning the unavailability of the particular route for packet transaction.
An alternate route is selected and the network manages to withstand the loss of the
dying node. Most of the present day standards facilitating fully adhoc mode of data
transfer, use this type of network infrastructure for implementation.
2.3 A Review of MANET Routing Protocols
The way a routing path is selected for the packets to traverse from source to
destination depends upon the routing protocol implemented into the network.
Broadly speaking, the MANET routing protocols can be divided into two
categories, active protocols and reactive protocols.
Active protocols are the one which use periodical routing updates for getting
an information about the route availability into the network. The basic advantage
of active routing protocols is the readily availability of the fresh routes for data
transfer. Downside of active routing protocols is the extra bandwidth which is
utilized uselessly for keeping the routing tables information up-to-date even if it is
of no use.
Second class of protocols is Reactive protocols which get the information
about the fresh and available routes in reactive manner i.e. on-demand. In reactive
protocols, the node sends routing updates whenever it has data to send. In this way
the network saves the extra bandwidth, at the cost of latency due to route selection,
18
which is required for unnecessary route updates control packets at the time node is
the idle state.
There are a number of different routing protocols which are used for routing
packets from source to destination utilizing different metrics for route selection.
Some of the protocols which are matured to most of the extent in the MANET
environment involves Adhoc On-Demand Distance-Vector (AODV) Routing
Algorithm, Dynamic Source Routing (DSR) Algorithm and Destination Sequenced
Distance Vector (DSDV) Routing Algorithm. In the following lines, a brief
introduction to all these protocols have been given with a little discussion on the
route selection criterion for route selection in each case.
2.3.1 Adhoc On-Demand Distance Vector (AODV ) Routing Algorithm
Being a reactive protocol, AODV algorithm uses on-demand approach for
constructing routing paths from sending node to receiving node [17]. The link
maintenance and discovery is carried out using three control messages Route Reply
(RRep), Route Request (RReqs) and Route Error (RErr). Later, a special
destination sequence number is used for storing each discovered route in the
routing table [18]. Information about one-hop neighbours is obtained by floating a
HELLO message into the network. Whenever the destination node is not accessible
via the current intermediate node or has been died-out or is out of the network,
RErr message is send to the initiating node. A RReq message is initiated by the
source node whenever it has data to send, and a particular route is selected for this
communication session which is expired as soon as the communication session is
complete.
19
2.3.2 Dynamic Source Routing (DSR) Algorithm
Being designed for upto 200 nodes, DSR algorithm is able to work in a reactive
manner. DSR algorithm sends Route-Request message to all the immediate
neighbours whenever any node has data to send [19][20]. Whenever a node
receives a route-query message, it scans its cache memory for the route
information. If it finds the information, it piggy-back the route information on the
route query message. The route maintenance is carried out by confirming the link
availability for carrying out the data by the receiving node. An acknowledgement
packet is generated by the receiving node which is used for link availability in the
network which is an essential step for route maintenance in DSR algorithm.
2.3.3 Destination Sequenced Distance Vector (DSDV) Routing Algorithm
DSDV routing algorithm works in a active manner by constantly updating its
routing path by periodically sending routing update messages and keeping its
routing table up-to-date with the changing network scenario. Network designers
choose DSDV to route packets in those network which contains comparatively
lesser number of nodes [21]. Earlier version of DSDV suffered with a routing loop
problem which is solved in the newer versions of this protocol by applying a
sequence number to the each routing update. The priority of the route is then
selected basing upon the highest sequence number of all the competing routes.
Concept of this sequence number has been inspired from the Bellman-Ford
algorithm, in which each routing table entry has a sequence number to measure the
freshness of the route.
20
2.4 A Few MANET Based Network Technologies
A number of networking technologies have been implemented based upon the
principles of MANET. All these technologies tend to consider a component device
as a node which is capable of sending the data as well as forwarding it to the
neighbouring nodes thus acting as a router as well. Each of these technologies has
been developed to work in a particular environment and scenario. One of the key
feature in most of these technologies is the use of Orthogonal Frequency Division
Multiplexing (OFDM) at the physical layer which is meant for sending data on
parallel subcarriers thus subjected to high data rates. The relationship of OFDM
and MANET is very strong and most of the present day deployed MANET based
architectures are using OFDM as the physical layer implementation [22]. This is
the reason that in our work we have improved the performance of OFDM at the
lowest two layers of TCP/IP protocol suite.
In the following lines a brief introduction of the networking technologies
that are based upon the principles of MANET is given. Fig. 2.3 is showing the
Data Rate vs Range of different Wireless Networking Techniques associated with
MANET.
21
Fig 2.3: Overview of Data Rate and Range Associated With Different Wireless
Technologies[23]
2.4.1 IEEE 802.11 a/b/g/n
IEEE 802.11 standard suite presents specifications for a Physical (PHY) Layer and
Medium Access Control (MAC) Layer. IEEE 802.11 focus on High speed Wireless
Local Area Network (WLAN) scenario and has been emerged as the pioneer
WLAN standard that has penetrated the commercial market. IEEE 802.11 supports
both the ad hoc networking approach as well as connections using AP, mentioned
in Sec. 2.2.1. Comparing to the Open Systems Interconnection (OSI) model which
divides the communication system into seven layers, IEEE 802.11 sets the
specifications for the lower two layers of the model and divides the second layer of
OSI model into two namely Medium Access Control (MAC) and Logical Link
Control (LLC) layers [24].
22
A schematic comparison between the two reference models is given in Fig. 2.4.
Application
Physical
Data Link
Network
Transport
Session
Presentation
Physical
Medium Access Control
Logical Link Control
OSI ReferenceModel
IEEE 802 Model
Fig 2.4: Comparison of OSI and IEEE 802 Reference Models
Fig. 2.4 shows that the IEEE 80.11 reference model deals with the lowest two
layers of the OSI model irrespective of the upper five layers.
IEEE 802.11 standard was constituted in a need to foster the compatibility
between the WLAN industrial product vendors and this idea led to the approval of
the WLAN standard in June, 1997. Later-on, in order to meet the challenges of
high data rate requirements of the newly emerging applications market, a few
newer versions of the IEEE 802.11 standard were launched by tailoring the PHY
layer specifications of the original IEEE 802.11 standard in order to accommodate
23
maximum data rate transmission. Two of these newer specifications are ratified in
1999 and are labelled as IEEE 802.11a and IEEE 802.11b standards. At the MAC
layer both these newly born standards shared the same specifications as the
original IEEE 802.11 but at the PHY layer, their properties differ from the parent
protocol. 802.11a uses Orthogonal Frequency Division Multiplexing technique at
the PHY specifications with 5GHz UNII band, while the 80.11b uses
Complementary Code Keying (CCK). The output of 802.11a can go upto 54 Mbps
while 802.11b can provide throughput upto 11 Mbps [25].
Another feature of IEEE 802.11 standard is 802.11n which is termed as
Wide Fidelity (Wi-Fi) system. This is another popular industrial standard which
provides adhoc wireless connectivity between mobile nodes at the Campus Area
Network (CAN) level. Tables mentioning the key features of the popular 802.11
industrial standard and its PHY specifications are given below.
Table 2.1: Key Feature for different Industrial Standards based upon IEEE
802.11 Specifications [22]
Industry
Standards
Roaming
Support
Supported
PHY Tech.
Data Rate
(Mbps)
ISM
Band
(GHz)
UNII
Band
(GHz)
Network
IEEE 802.11 Yes
DSSS, FHSS,
Diffuse Ir 1, 2 2.4-2.48 N/A WLAN
IEEE 802.11a
Yes OFDM
6, 9, 12, 18,
24, 36, 48
54
N/A
5.15-5.25
5.25-5.35
5.72-5.87
WLAN
IEEE 802.11b Yes DSSS 1, 2, 5.5, 11 2.4-2.48 N/A WLAN
Bluetooth No FHSS 1 2.4-2.48 N/A WPAN
24
Table 2.2: PHY Specifications for IEEE 802.11 Standard [26]
PHY Frequency
Band
Data Rates Modulation Comments
Frequency Hopping
Spread Spectrum
2.4-GHz
ISM Band
1, 2 Mbps 2-Level
Gaussian
FSK,
4-Level
Gaussian FSK
50 Hops Per
Second
79 Channels
Direct Sequence
Spread Spectrum
2.4-GHz
ISM Band
1, 2 Mbps Differential
Binary FSK,
Differential
Quadrature
PSK
11-Chip Barker
Sequence
Spreading
Baseband IR Diffuse
Infrared
1, 2 Mbps 16-Pulse
Position
Modulation
Uses Pulse
Position
Modulation
The IEEE 802.11a PHY is similar to the HiperLAN/2 PHY. Both these standards
use OFDM at the PHY and operates at 5 GHz UNII band. Additional to this, a data
rate starting from 6 Mbps to 54 Mbps can be supported by IEEE 802.11a. Similarly
802.11a is least subjected to the Radio Frequency (RF) interference effects. These
all features make IEEE 802.11a one of the foremost choice for today’s system
implementers to use this standard as a platform for their proposed models. In our
proposed model we have also used OFDM as a baseline standard for data
transmission.
25
2.4.2 Bluetooth Wireless Technology
Bluetooth technology concept was put forth in early 1994 as a replacement for
cable connections to connect cellphone with headsets and other accessories. The
first version Bluetooth v1.0, was launched in July 1999. Since then different
versions of Bluetooth have been launched and have penetrated into the market.
Currently Bluetooth v4.0 is available into the market. Similar to IEEE 802.11b
standard, Bluetooth also works in the global 2.4 GHz ISM band. Today Wireless
Personal Area Networks (PAN) has been deployed using Bluetooth at the premises
of homes and offices.
Bluetooth offers three different classes in terms of range of operations.
These includes 10m, 20m and 100m termed respectively as lowest, moderate and
highest power range with a respective transmit power as 1mW, 2.5mW and
100mW. Bluetooth works on the concept of piconet which is actually an AP based
MANET in which one master device is controlling two or more slave devices.
Piconet is further subdivided into scatternets. One slave device can be part of a
number of piconets. Service Discovery Protocol is used by Bluetooth technology to
discover the Bluetooth enabled devices in the range of the current master device.
Different applications profiles are provided by Bluetooth specifications which fine-
tunes implementation of various applications.
Compared to OSI reference model, Bluetooth divides its stack into eight
layers as shown in Fig. 2.5. The first layer, Applications manages the
communication between the host computers. Since Bluetooth involves many point-
to-point links, thus for having a common representation of the input data,
RFCOMM/Service Discovery Protocol (SDP) emulates serial connections similar
to RS232 serial ports. Similar to the Session Layer, L2CAP layer manages the data
26
flow control by multiplexing the data coming from the upper layers and converting
them into different packet sizes. HCI layer controls communication between the
Bluetooth device and the separate host. In the OSI model the Transport layer
maintains the multiplexing and reliability of data communication across the
network. So LM and HCI layers overlap with the Transport Layer.
Application
Physical
Data Link
Network
Transport
Session
Presentation
OSI ReferenceModel
Applications
RFCOMM/SDP
Logical Link Control and Adaptation(L2CAP)
Host Controller Interface(HCI)
Link Manager (LM)
Link Control
Baseband
Radio
Bluetooth
Fig 2.5: Comparison of OSI Reference Model and Bluetooth Protocol Stack
The basic role of the LM layer is to configure and control the links of the
Bluetooth device with the other Bluetooth devices. It is also responsible for
connecting slave devices with the piconet and their address generation. The
27
number of slaves are limited to seven in the Bluetooth specifications. If other
active devices are present in a piconet they ‘ll be treated as parked[27]. Link
Controller and Baseband Layers overlap with the Datalink Layer of OSI model and
are responsible for controlling the Physical Links by performing error checking
and correction, packet assembling and frequency hopping control. Radio Layer
performs the same task as the Physical Layer of OSI model.
Table 2.3: Technical Specifications for BluetoothV2.1
Technical Specifications BluetoothV2.1+EDR
Radio Frequency 2.4 GHz
Distance Range 10 meters
Over the air data rate 1-3 Mbps
Application throughput 0.7-2.1 Mbps
Nodes/Active Slaves 7/16,777,184
Security 64b/128b and application layer user defined
Robustness Adaptive fast frequency hopping, FEC, fast ACK
Latency (from a non-connected
state)
100ms
Government Regulation Worldwide
Certification Body Bluetooth SIG
Voice Capability Yes
Network Topology Scatternet
Service Discovery Yes
Profile Concept Yes
Primary Uses Mobile Phones, WPAN: Cable replacement
Every Bluetooth network operates in one of the two network configurations,
Master or Slave. Slaves tune into the frequency hopping sequence set by the master
28
device. Bluetooth offers a connectivity range of 10m. Within a piconet, full duplex
point-to-point communication is used between master and slave devices. A chart
showing the technical specifications of the BluetoothV2.1 is given in Table 2.3.
2.4.3 HiperLAN/1 and HiperLAN/2
HiperLAN/1 has been developed as a wireless equivalent of Ethernet while
HiperLAN/2 is a WLAN standard based upon Wireless Asynchronous Transfer
Mode (ATM). The gross data rate of HiperLAN/1 can go upto 23.5 Mbps and its
net data rate can go upto a maximum 18 Mbps. On the other hand, the HiperLAN/2
can provide a gross data rate of upto 6, 16, 36, and 54 Mbps and its net data rate
can go upto 50 Mbps. European Telecommunications Standards Institute (ETSI)
have deployed both these standards. HiperLAN/1 uses Gaussian Minimum Shift
Keying (GMSK) while HiperLAN/2 uses OFDM modulation scheme with any
suitable digital modulation like Binary Phase Shift Keying (BPSK), Quadrature
Phase Shift Keying (QPSK) or Quadrature Amplitude Modulation (QAM). The
maximum range for both the standards is 50 m and these standards use three
different power levels 10, 100 and 1000mW for signal transmission.
2.4.4 IEEE 802.15.3
IEEE 802.15.3, termed as Wireless Personal Area Network (WPAN) is an IEEE
standard which is based upon the concept of piconet and uses a 10m Personal
Operating Space (POS) around the device for connection. Every piconet is
controlled by a PicoNet Coordinator (PNC). Every PNC has a lot many functions
to perform which include Quality of Service (QOS) issues, management of power
save modes, authentication, security and the basic timings of the piconet using
beacons. The uncoded piconet data rate at the PHY is 22 Mbps. It can supports five
29
data rates from 11 to 55 Mbps. IEEE 802.15.3 is designed to be operated at 2.4 to
2.4835 GHz.
2.5 TCP Window Size Evaluation using NS-2 Simulator
As already discussed in Sec. 2.3, the performance of the MANET depends greatly
on the routing protocol implemented at the Network layer for routing the packets
amongst the different nodes of the network. In this regard we have carried-out a
study [28] regarding the performance evaluation of the different routing protocols
in the MANET basing upon the simulated environment in NS-2. We simulated the
proposed environment in the Open Source Network Simulator (NS-2) over the
Fedora-14 platform and tested the performance of the protocols in the light of
different parameters including tcp window size vs time and packet delivered at the
destination.
In this regards, the environment that we simulated was based upon three
nodes moving in a geographical area of 800m x 500m. Rest of the simulation
parameters are given in Table 2.4.
When the simulation starts, there is no connection between the nodes since
they are far-apart and are out-of-reach. Node 0,1 and 2 starts their movement at
times 10, 15 and 20 secs respectively towards their destination. Node 2 starts
movement to its down-left with 1 m/s, node 1 starts moving towards its right with
3 m/s while Node 0 towards down-right with 1 m/s. The Tool Command Language
(TCL) script governing the initial positions and movement of the nodes is given in
Appendix-A.
Table 2.4: Simulation Parameters for Evaluating Performance of Different
30
Protocols in NS-2 Environment
Routing Protocols Used AODV, DSDV, DSR
Geographical Terrain 800m x 500m
Mobility Model Random Waypoint Mobility Model
Queue Type Queue/DropTail/PriQueue, CMUPriQueue
No. of Nodes 3
Simulation Time 280 secs
Data Traffic FTP (over TCP)
Nodes Placement As Explained in Following Text
Bandwidth 11 Mbps
MAC Layer IEEE 802.11 (WLAN)
In the simulation scenario, a TCP agent is attached with the Node 0. A File
Transfer Protocol (FTP) application is attached with the TCP agent for data
transmission. At 10 seconds since the start of simulation, the first TCP packet is
transmitted from Node 0 towards Node 1, but there is no connection established
between the two nodes. The two nodes start moving towards their destination.
After the failure of the first transmission attempt that occurred at 10 sec, second
transmission attempt is carried-out at 16th seconds. But still the two nodes are out
of range of each other so no connection can be established between them and the
packets get lost. The two nodes continue to move closer to eachother and the next
retransmission attempts occur at 28th sec, 52nd sec and then at 100th sec. In the
meanwhile, Node 3 also starts its movement towards the destination. During the
random course of its movement, it has to pass in between the path of the Node 0
and 1. In the last retransmission attempt, the Node 0 & 1 have come so closer to
eachother that a two-hop connection is established between the two nodes via
Node 2. Node 2 is acting as a router by routing the data packets from Node 0 to 1
while Ack packets (as being expected for TCP traffic) have been routed from Node
31
1 to Node 0. This process goes on till at 110 seconds a direct connection is
established between the two and a one hop communication starts. During the phase
of switch-over from 2-hop to 1-hop communication, there is a slight phase of
packet drop which is shown by the drop in the TCP window size after which it
again starts rising till 200 seconds. At 200 seconds, the two nodes get out of range
of each other and thus a communication is not possible between the two thus the
TCP window size drops down to 0.
The nodes continue their random course of movement which is dictated by
the TCL script given in Appendix-A, during which they again come in eachother’s
vicinity at around 240 secs. This proximity is via the
Fig 2.6: TCP Window Size Evaluation for DSDV Protocol
Node 2 which facilitates a 2-hop communication between Node 0 & 1. But this 2-
hop communication lasts very short for about 9 secs after which Node 1 again goes
out of range of Node 2 and the communication session is broken which is shown
by the rapid shrink in the TCP window size. The variation of the TCP window size
is depicted in the graph of Fig. 2.6 which is drawn using the following command
32
of the TCL script at time resolution of 0.01 secs.
$ns at 10.1 “plotWindow $tcp $windowVsTime”
NS-2 has the ability of not only showing us the happenings of the network in the
form of Network AniMator build-in application, but it also gives us an opportunity
to analyze the different parameters of the network in the form of American
Standard Codes for Information Interchange (ASCII) data. This data is generated
in the form of a trace file which is a log file storing the different events of the
network. The trace file which contains .tr extension is generated using the
following command in the TCL script.
# set tracefd filename1.tr
NAM trace file stores the NAM traces using the following piece of TCL script.
# set namtrace filename.nam
Execution of NAM traces is carried-out in the Bourne-Again Shell (BASH) using
the following TCL script.
# exec nam filename.nam
The NAM traces and ASCII traces are get recognized on the basis of extensions of
the filenames.
In order to get the data of our choice extracted from the ASCII trace
generated during the happening of events in the network, we need to first study the
structure of the ASCII trace so as to understand the information which is found in
the ASCII trace.
The structure of the ASCII trace generated in our simulated network is given
as.
33
r 162.804394634 _1_ AGT --- 9715 tcp 1060 [13a 1 0 800] ------- [0:0 1:0 32 1] [4834 0] 1 0
Different fields of the ASCII trace contains various information. The entry in the
first field can contain one of the five alphabets, namely r, s, D, f, or M. These
entries represents received, sent, Dropped, forwarded and Movement Indication
respectively. For the Movement Indication ASCII trace, the overall format of the
trace is entirely different and includes an indication of the movement speed as
well. Time of the event is depicted in the second field. Third field represents the
node number which has taken part in this event. In our simulation scenario this
field can be 0,1 or 2. The type of packet to which this trace belongs, is indicated in
the fourth field. There are four types of packets which are generated during the
execution of NS-2. These include AGT, IFQ, RTR or MAC type of packets. These
represents TCP (Transport Layer) packet, dropped packet event (interference
priority queue), routed packet and MAC layer packet respectively. Fifth field
represents the global sequence number of the packet. Sixth field shows us the
packet type. This field can be udp, tcp or ack to represent UDP packet, TCP packet
or ACK packet. Packet size is mentioned in the seventh field which is in bytes.
Next field contains information about the MAC layer. Within the square bracket,
the first number is the time (sec) in which the data is expected to travel across the
wireless medium. The second and third number mentions the MAC id of the
sending and receiving node. This number can be 1 or 0. The last number in the
square bracket is a particular number which is related to the specific MAC type. In
the above mentioned trace file entry, this number is shown as 800 which is related
to ETHERTYPE_IP. The IP address of the sender and receiver node is mentioned
at the first and second place of the next square bracket. The third number in the
same bracket, 32 in our case, indicates the time-to-live of the packet on the
network. At the expiry of this time, the packet dies-out automatically. Next square
34
bracket indicates the sequence number and acknowledgement number of the tcp
traffic.
Once the entries in the traces of the events of the network are generated and
recognized, next step is to extract the information of our choice from the trace file.
For carrying out this process, the language we used is termed as GNU AWK
(GAWK) language [29]. GAWK is a powerful pattern-recognition, text-
manipulation, data-driven open-source language which is used to extract and
isolate particular alpha-numeric patterns in the given text [29].
First of all we used DSDV protocol in the network and the number of tcp
packets successfully delivered by the DSDV protocol to the receiving node. The
following piece of script was used at the BASH terminal to carry-out this task.
#awk '$7~/tcp/ {print}' tracefile.tr> "tcp_out_packets.tr"
#awk '/_1_ AGT/ {print}' tcp_out_packets.tr>"final_tcp_DSDV.tr"
In the above script, the first line isolates those rows of the trace file which contains
the keyword tcp in seventh field. This line isolates all those rows and saves them in
a separate file with name tcp_out_packets.tr. Next command line, reclassify the
newly created file, by isolating only those rows which contains _1_AGT at any
field position of the row. The newly isolating rows are saved in the file
final_tcp_DSDV.tr. Application of the above commands indicates 8489 TCP
packets successfully delivered by DSDV protocol to the destination in 280 secs.
Next we applied the same parameters to the DSR protocol. The reactive
DSR protocol produced the graph shown in Fig. 2.7.
35
Fig 2.7: TCP Window Size Evaluation for DSR Protocol
This graph shows that the communication between the nodes was started a bit
earlier in the DSR protocol as compared to the DSDV protocol. The first packet
was successfully transmitted to the destination at around 80 secs. This shows an
early start of communication as compared to the case of DSDV. The tcp window
size continue to rise until at around 110 secs when the communication is shifted
from 2-hop to 1-hop, there is a sudden drop in the tcp window size showing a
slight packet drop at the time of communication shift from 2-hop to 1-hop. The
window size continue to rise until at around 204 secs when the communication re-
shift from 1-hop to 2-hop. The shift of communication shown by the change in the
slope of the window size increase at around 204 secs.
There is a sudden drop in the size of the tcp window to 0 at around 204 secs
when the two nodes go out of range of eachother. Then there is no further
connection between the two nodes. While simulating the model for DSR protocol
36
we have used Carnegie Mellon University (CMU's) wireless extension to NS-2
(incorporated in the release NS-2.1b9a). This extension is implemented at the
Interface Queue type for DSR. A necessary feature of CMUPriQueue type is that it
classifies the packets into four categories namely control packet, audio, video and
rest of traffic. Remaining parameters including the movement scenario were the
same as for DSR. Using the same GNU AWK script as described in the start of the
same session, the number of packets successfully delivered to the destination using
DSR protocol is 10470. It should be remembered that the proactive protocol
DSDV utilizes a significant number of control packets just to keep the routes fresh
in its cache, even at the times when the nodes are in idle state and has nothing to
send.
When we applied the same parameters to the AODV protocol and evaluated
the window size, we see that there is no drop in the window
Fig 2.8: TCP Window Size Evaluation for AODV Protocol
37
size as was encountered in the case of DSR or DSDV protocols. The window size
vs time graph for the AODV protocol using the same parameters and movement
scenario is given in Fig. 2.8.
Analysis of the graph reveals the fact that the tcp window size continue to
grow in size till at around 247 secs the two nodes go out of range of eachother and
a communication is not possible between them. In the whole course of movement
scenario a 2-hop path is established between the two nodes. The Reno/Tahoe
algorithm implemented at the tcp level increases the window size in the integer
values of MSS(Maximum Size Segment), since there is no loss event occurs which
is depicted by the reception of three duplicate acks or by a time-out event after
which the window size would have dropped down to 1 MSS or half of its peak
value depending upon the algorithm implemented. The no. of packets successfully
delivered to the destination using the AODV protocol is 5995. This show a least
number of packets successfully
38
Fig 2.9: Number of TCP Packets Successfully Delivered to the Destination by the Three
Reference Protocols.
delivered to the destination when compared to the other two routing algorithms as
shown in Fig. 2.9.
The basic reason for the least reception of the tcp packets by the AODV
protocol compared to the other two protocols is the fact that there is a continuous
2-hop communication between the two nodes during the whole communication
session. Due to the large round-trip time required for traversing the packets
between the two nodes on a 2-hop link and same for the acks on the return path,
makes the overall reception of packets less. This makes the count of the
successfully delivered packets to the destination the least for the case of the AODV
protocol compared to the other two protocols.
2.6 Summary
This chapter starts with a basic introduction of Wireless Mobile Adhoc Network
which is then followed by the classification of the wireless MANET into the two
major categories. Next a basic level introduction is given to the three famous
routing algorithms namely AODV, DSDV and DSR. The route selection criterion
of each of these protocols is discussed in brief. Then the major networking
technologies based upon the principles of MANET are discussed with sufficient
details. These technologies include IEEE 802.11 a/b/g/n (WLAN), Bluetooth,
HiperLAN/1,2 and IEEE 802.15.3 (WPAN). These technologies have been
discussed with putting a light on their nitty-gritty of the PHY specifications. Next
the simulation results have been discussed for the three widely implemented
routing algorithm based upon the tcp window size evaluation and the number of
39
successfully transmitted packets using NS-2 simulation.
Chapter-3
OROTHOGONAL FREQUENCY DIVISION MULTIPLEXING (OFDM) -
INTRODUCTION AND SYSTEM MODELING
3.1 Introduction
Orthogonal Frequency Division Multiplexing (OFDM) is a communication
technique which enables a number of narrowband subchannels to be transmitted
over a large bandwidth in parallel. This division of the bandwidth into subchannels
allow efficient usage of the available frequency spectrum. OFDM is chosen as the
radio access technique for the LTE at the downlink while it is chosen for the
WiMAX standard at both the uplink and downlink[31]. Being one of the most
widely used communication technique, OFDM is used in a number of legacy
standards, examples include Asymmetric Digital Subscriber Line(ADSL) [32],
wireless Metropolitan Area Network(WMAN) [33,34] and wireless Local Area
Networks(WLAN) [35-37] standards. Similarly Institute of Electrical and
Electronics Engineers (IEEE) have declared OFDM a potential candidate for the
different wireless access standards evolved in the recent past or are about to be
evolved in the near future, examples include wireless World Initiative New Radio
(WINNER) [38] standard, the first cognitive radio standard known as Wireless
Regional Area Network (WRAN) [39], the two standards proposed by 3rd
Generation Partnership Project (3GPP) known as LTE-Advanced and Long Term
Evaluation (LTE) Standards [40]. The two European standards Digital Video
40
Broadcasting (DVB)[42] and Terrestrial Digital Audio Broadcasting (DAB-T)[41]
uses OFDM technique.
The basic concept of OFDM lies in the division of available allocated
spectrum into subbands which are then modulated with orthogonal subcarriers.
Over frequency-selective channels, the coherence bandwidth of the channel is
smaller than the subcarrier bandwidth. Coherence bandwidth refers to the
frequency spectrum in which the channel has a linear phase response and constant
gain or simply the frequency spectrum in which the channel parameters have a
strong correlation. A schematic representation of an OFDM symbol comprising of
orthogonal subcarriers is given in Fig. 3.1.
Fig 3.1: An Envelop of OFDM Subcarriers
3.2 Single Carrier vs Multicarrier Transmission Systems:
Prior to OFDM, the communication system in use was single carrier modulated
transmission systems termed as Frequency Division Multiplexing (FDM). In the
41
single carrier modulated transmission systems, the data was modulated on a single
carrier and then was transmitted on to the channel. In case data from a number of
users have to be transmitted then the data is modulated on different carriers having
sufficient guard interval to avoid aliasing effect and then it was sent on to the
channel. The biggest advantage of this type of system was the use of relatively
simple channel estimation schemes for mitigating the effects of the channel
impulse response from the system. On the other hand, there were two inherent
problems associated with the single carrier transmission systems. First was the fact
that due to the modulation of a data on to a single carrier, if a channel fade comes,
it can destroy the whole link. Secondly the low data rate associated with FDM due
to the modulation of the data onto a single carrier prevents the use of the system
for many latest applications which needs high data rate to operate. Thus there was
a need of such a technique which can utilize the available bandwidth in the most
efficient manner. This concept gave rise to OFDM.
In the orthogonal frequency division multiplexing technique, data is send at
a time for different users on different subcarriers of same symbol. The schematic
comparison of the single carrier based multi-channel modulated data transmission
and orthogonal frequency division multiplexing based transmission is given in Fig.
3.2
42
Fig 3.2: Comparison Between Conventional Multicarrier And Orthogonal Multicarrier
Transmission System
Due to the modulation of data symbols on orthogonal subarriers which overlap in
time domain, the overall data rate of the system increases many folds. And this is
the main advantage of OFDM which convince the digital transmission system
designers to include OFDM as the backbone transmission system in a number of
standards.
3.2.1 History
The concept of parallel data transfer began materializing in early 60's[43] when
relatively wide bandwidth channel was used to carry the low rate signals of
telegraph. A separate carrier frequency was used for each signal in that system. An
application which implemented parallel transmission was introduced in 1957 for
the military purposes. The application name was Kinplex system [44]. This
application was designed for data transmission over high frequency wireless
43
channel in the most adverse environment. The next step was the proposal of multi-
tone code-multiplexing scheme in 1961, which used 9-pt QAM constellation
scheme at each carrier [45]. This code-multiplexing scheme is termed as the first
step towards attaining spectral efficiency using the concept of orthogonal waves.
The concept of OFDM was proposed by Robert W. Chang in 1966 [46]. He
was the first to give the concept of band-limited orthogonal signals[2]. Since then,
OFDM data transmission technique was used in several military applications
comprising of high frequency [47,48]. In 1971, Weinstein and Ebert gave the
concept of Discrete Fourier Transform (DFT) for the first time for parallel data
transfer [49]. Trellis codes were implemented for the first time in the OFDM in
1980's. Since then OFDM is used for high density recording, digital mobile
communications and high speed modems. By looking at the patent of Chang.,
OFDM can be considered as the optimum case of Multi-carrier modulation (MCM)
due to the fact the subcarriers are treated orthogonal. As compared to FDM, an
approximately 50% bandwidth savage is observed while using OFDM [50]. And
this is the basic reason behind the use of OFDM in a number of different standards
meant for high data transfer applications as mentioned earlier in the introduction
section.
3.2.2 Basis of Orthogonality in OFDM
The basic concept of OFDM lies in the orthogonality of the subcarriers which
constitute an OFDM symbol. This orthogonality is maintained in the Inverse Fast
Fourier Transform (IFFT) block at the transmitter end. Two frequency waves
forms Am(t) and Bn(t) which are time-dependent, will be treated as orthogonal if
44
they fulfil the orthogonality principle.
nmfordttBtAT
T
T
nm
0)()(2
1 (3.1)
where 2T is the common repetition interval for the two waves. If these two waves
are considered as the two subcarriers of the OFDM, then if the above condition is
satisfied then the data will be transmitted through the OFDM system without
suffering from Inter-carrier-interference. This condition is depicted in Fig. 3.1.
In the real-world, two waves are orthogonal to eachother whenever their
frequencies are integral multiples of eachother. This condition is satisfied in the
IFFT block of the OFDM which modulates each of the digitally modulated data
symbol with a frequency which is integral multiple of any other frequency in the
same envelop. The mathematical equation for IFFT of a waveform is given as
under:
1
0
2)()(
N
n
N
nkj
enxkX (3.2)
The value of n in eq. 3.2 keeps on changing with the taps of the IFFT, generating
sinusoids of different frequencies each of which is an integral multiple of
eachother as depicted from eq. 3.2. Thus all the generated subcarriers are
orthogonal to each other by the analytical definition of the orthogonality of
different waveforms. This orthogonality is the nub of OFDM and dictates the ICI
free transmission of the data symbols from transmitter to receiver.
3.3 Block Diagram of OFDM
45
The block diagram of OFDM is given in Fig. 3.3. In the first stage, the message bit
stream is passed through the Source Encoder bock which also acts as a
compression block and converts the analogue input message symbols into bit
stream. Any suitable source encoder can be used for this purpose. One of the most
commonly used source encoder is Huffman Encoder which converts the input
message symbols into bit stream with
Data Source
Digital Modulation
S/PCP
InsertionP/SIFFT
FFTSource
DecoderDigital
DemodultionP/S S/P
CP Removal
Multipath Fading Channel
AWGNW(n)
Source Encoder
Recovered Data
Fig 3.3: Block Diagram for OFDM based System
minimum redundancy depending upon the probability of occurrence of the
different symbols. The most frequently occurring symbols are assigned the least
bits and the least frequent symbols are assigned the most number of bits. Next the
bits are passed through the channel coding block which is not shown in Fig. 3.3 to
conform it to the basic OFDM standard. So depending upon the channel coding
used in the system, redundant bits are added into the OFDM model. In our
proposed model we have used Turbo Codes for mitigating the effects of noise from
46
the signal.
Next step is the passage of the bits to the digital modulation block. This
block converts the channel coded message bits into the digital modulated symbols
which are depicted as constellation points on the constellation map. Five different
types of digital modulation schemes can be used for this purpose. After being
conversion into digitally modulated symbols, the signal is converted from serial to
parallel format and is fed to Inverse Fast Fourier Transform (IFFT) block. This
step is termed as OFDM modulation. In this step the digitally modulated data is
remodulated on a number of frequency subcarriers depending upon the size of the
IFFT. For an N-pt IFFT, at the output it produces Xp(k) symbol at the output having
N subcarriers given by[51]:
Tppppp NXXXXkX )]1(),...2(),1(),0([)( (3.3)
IFFT is the block which is responsible for the generation of orthogonal subcarriers
which form the basis for the high data rate of the OFDM through parallel
transmission of data symbols. For n subcarriers (V n=0,1,2 .....N-1) the OFDM
modulated symbol generated at the output of N-pt IFFT at time instant p is given
by:
1
0
2
)(1
)(N
n
N
j
p enxN
kX
(3.4)
Where Xp(k) is given by (3.3). x(n) shows the signal at the output of the nth tap of
IFFT block.
Next step is the re-conversion of the data from parallel to serial after which
cyclic prefix is added at the end of the OFDM symbol in order to increase the
47
immunity of the system against the delay spread of the channel. This delay spread
causes inter-symbol interference into the OFDM system which is also sometimes
termed as inter-block interference. The cyclic prefix which is also called Guard
Interval, is affixed with each OFDM symbol. i.e. last L symbols of )(kX p are
appended to the start of each transmitted OFDM block pX~
,and is shown as:
)();1)((~
kXLNkXX pcppp (3.5)
In Eq. 3.5 N represents size of the IFFT block and Lcp refers to the cyclic prefix
length. As per the convention used in MATLAB®, a semicolon is used between
the two terms to show that the first portion of the symbol is appended to the second
symbol at the start. The cyclic prefix elongates the signal in time domain by
appending the subcarrier from the start to the end, to ensure that the aliasing
portion of the symbol owing to delay spread of the channel is the one which has
already been replicated at the start. A detailed discussion on the repercussion and
causes of the cyclic prefix is given in the Sec. 3.4.2 which is dedicated for the
discussion of inter-symbol/block interference.
Next the signal is passed through the channel. In order to imitate the
behaviour of the real environment, channel has been simulated as multipath
Stanford University Interim (SUI) channel model. After the incorporation of fading
effects into the signal following its passage through a multipath rayleigh fading
channel, next step is the addition of Additive White Gaussian Noise (AWGN) into
the signal. The noise tend to further deteriorate the quality of the signal by
increasing its BER.
48
)()(),()(
1
0
nwlnxlnhnyL
l
(3.6)
Where L represents the total number of discrete multiple paths for the Rayleigh
fading channel, h(n,l) shows the fading channel impulse response and w(n) depicts
the receiver added AWGN noise.
Now the signal is converted from serial to parallel format for passage through the
Fast Fourier Transform (FFT) block. The FFT block demodulates the digital
symbols from the orthogonal subcarriers and the signal at the output of this block
is given by [52]:
m
N
kjjN
k
L
l
kmlk WeHXmY
21
0
1
0
)()( (3.7)
Here mW is the FFT of the AWGN noise )(nw and )( km
lH
shows FFT of the
Rayleigh fading channel impulse response given by:
N
kmj
lnkm
l ehN
H)(2
,)( 1
(3.8)
After passage through the FFT, the received symbols are converted from parallel to
serial and then fed to the digital demodulation block. But before feeding them to
the digital demodulation block, the channel estimation/equalization can also be
performed on the signal to remove the effects on the received signal due to the
Doppler spread of the channel. The proposed channel estimation/equalization
algorithm has been explained in due details in the Ch. no. 5. After passage through
the channel estimation/equalization and demodulation of the signal, the decoding
of the bits is carried-out by passing them through iterative decoding portion of the
49
receiver. The decoder is based on Maximum A Posteriori algorithm which
efficiently decodes the bits using the two component MAP decoders by exchanging
soft information between them. This step helps improve the decoding efficiency by
using the previous estimates of the decoded bit in each iteration. Finally BER is
computed using these two values in the signal comparator. The proposed OFDM
Model has been tested by changing the number of iterations and evaluating and
comparing its performance under five different digital modulation schemes.
3.4 Downsides of OFDM System
Some downsides of OFDM system also exists. These downside includes its
sensitivity to different parameters like delay spread, doppler spread and noise
effects etc. These parameters have been explained with due details in the following
text.
3.4.1 Sensitivity to Doppler Spread
As discussed earlier, the OFDM symbol is composed of a number of subcarriers
which are used for modulation of the digital symbols. These subcarriers need to be
orthogonal in order for data to be received correctly at the receiver end. In sec.
3.2.2 it has been mentioned that the orthogonality of the subcarrier is generated in
the IFFT block which produces a subcarrier of different frequency at each of its tap
position. These generated frequencies are integral multiples of eachother and thus
are orthogonal by the analytical definition of orthogonality. But whenever there is
a comparative shift between the receiver and transmitter, doppler frequencies are
50
produced due to which the number of cycles at the transmitted frequency are
different than the number of cycles in the receiving frequency. Generation of
Doppler frequency follows the following formula:
)cos(v
fd (3.9)
In Eq. 3.9, fd is the Doppler frequency, v is the relative velocity between the
transmitter and receiver and λ is the wavelength of the carrier. θ is distributed
between 0 to 2π and is referred to as the angle between communication link and the
velocity. Due to Doppler frequency, there is a small drift in the peak of the
subcarrier from their original location because the frequencies are now no more
integral multiple of each other. This causes Inter-carrier interference (ICI) between
the subcarriers [53]. The second cause of the ICI is the phase distortion caused by
the fading phenomenon in the channel [54]. ICI is also caused due to the
synchronization issues between the transmitter and receiver. All these effects tend
to produce Channel Frequency Offsets (CFO) in the subcarrier forming an OFDM
symbol. Pictorial depiction of CFO is given in Fig. 3.4.
51
Fig 3.4: Channel Frequency Offset for a subcarrier of OFDM symbol
CFO is mitigated from the received OFDM symbol using the best method which is
addition of an efficient channel estimation technique into the system[55]. In this
thesis we have proposed two efficient channel estimation/equalization strategies
with the OFDM system which can mitigate the effects of CFO using pilots which
are sent prior to the data symbol into the channel. These pilots tend to incorporate
in them the channel impulse response, which is the major cause of CFO and then
using the proposed algorithms, the channel impulse response matrix is later used
for equalization of the upcoming data symbols. Ch. No. 5 provides the details of
the proposed channel estimation/equalization algorithm.
3.4.2 Sensitivity to Delay Spread
The time lapse at the receiver between the first and last multipath component refers
to delay spread. OFDM based communication systems are very sensitive to the
delay spread of the channel due to the multipath phenomenon. The OFDM symbols
during their passage through the multipath channel gets elongated in the time-
52
domain. This elongation of the OFDM symbol in the time-domain causes an
aliasing effect in the adjacent symbols which loses some subcarriers causing a loss
of data. This effect is termed as Inter-symbol interference or inter-block
interference in some text. This phenomenon puts an adverse effect on the system
performance. The basic effect of inter-symbol interference is the spilling of energy
at the adjacent symbols as shown in the example Fig. 3.5 and 3.6[51].
Fig 3.5: 101101 sequence that is to be sent. The dashed line shows actual transmitted shape
Fig 3.6: Received sequence of the transmitted shown in figure 3.5
53
Let we have to transmit the sequence 101101 on a channel prone to intersymbol
interference. The transmitted sequence is shown in Fig. 3.5 where the symbols 1
and 0 are clearly delimited and there is no spilling of energy before they are being
subjected to the channel prone to intersymbol interference.
After the sequence is being subjected to the multipath channel which suffers
with intersymbol interference, the shape of the received sequence is given in Fig.
3.6. The figure shows that each of the symbol is
Fig 3.7: Cyclic Prefix insertion in OFDM symbol
elongated in time-domain due to the multipath effect occurring at the receiver end.
Due to this multipath effect, the energy of each of the symbol is spilling into the
adjacent symbol as a result of which there is a possibility of loss of the subcarriers
found at the edge of the symbol.
In order to cater-for the effects of inter-symbol/bock interference, the
54
method used in the proposed model is the insertion of cyclic prefix. Cyclic prefix is
a portion of the symbol which is copied from the end and is appended at the start or
vice-versa. Due to the cyclic prefix, the aliasing portion of the symbol is the one
which is already been copied and thus the system retains a copy of the lost
subcarriers. Thus at the expense of extra bandwidth, system is saved from the
effects of intersymbol interference. Schematic view of the cyclic prefix insertion is
given in Fig. 3.7.
Fig. 3.7 shows the way cyclic prefix is inserted in an OFDM symbol. In
Fig. 3.7, TG refers to the guard interval period, Td to data symbol time and TS to the
overall symbol period. The overall symbol period is an addition of the Guard
interval time and data symbol period. Fig. 3.7 shows that a portion of the overall
symbol is replicated by copying from the end to the start. This has elongated the
data symbol in time-domain increasing its capability to deal with the delay spread
of the channel at the expense of extra bandwidth utilization. ¼ size cyclic prefix is
used in our proposed model to cater-for the effects of the inter-symbol interference
occurring in the channel.
3.4.3 Sensitivity to Noise Effects
Whenever a telecommunication signal passed through a channel and reaches
receiver, there are a number of noise effects which are incorporated into the signal
both at the channel and the receiver. The combined effect of all these different
types of noises is the deterioration of the signal which tend to degrade the overall
performance of the system. There are a number of noise effects which tend to
adversely tailor the system’s performance. These noise effects include galactic
noises, electric transient noises, thermal noises etc. Similarly the effect of all the
55
Gaussian distributed noises have been combined under one category called
Additive White Gaussian Noise (AWGN). All these noise effects severely disturb
the quality of the received signal thus affecting the BER performance of the
system. These noise effects tend to scatter the constellation points on the overall
constellation diagram thus causing the angular separation between them to
minimize. A pictorial representation of the scatter plots of the signal points, for the
proposed model, before and after their passage through the noisy channel, is shown
for four different modulation schemes in Fig. 3.8.
Fig 3.8: Constellation Map showing effect of noise on different modulation schemes for the
proposed model.
Depending upon the angular separation between the constellation points of
various digital modulation schemes, the noise effect is pertinent differently for
56
different modulation schemes. For the lower modulation scheme e.g. BPSK or
QPSK, the effect of noise is less significant. Reason being the angular separation
between the constellation points which is much large giving a cushion to the
system to identify the digital symbols properly even if the effect of noise effect is
large. Contrary to it, for higher modulation schemes, where the angular separation
between the constellation points is small, the effect of noise is more pertinent since
it is difficult at the receiving side for the decoding algorithm to get the symbols
identified properly due to very small angular separation between them.
In order to cater-for the noise effects induced in the received signal, channel
coding is used into the proposed system. Channel coding has been carried-out in
the presented scheme using Forward Error Correcting (FEC) turbo codes. Turbo
codes works in the forward error correction manner by not only identifying the
erroneous bit location but also corrects them, thus requires simplex control
information link. The channel coding scheme tend to add redundant information
into the message bit sequence which is used later on to produce a posteriori
probability, for making a decision regarding the decoded bit to be a 0 or 1. Details
of the channel coding scheme integrated into the proposed model is given in Ch.
no. 6.
3.5 OFDM-Merits and Demerits
Some of the inherent merits/demerits of OFDM system have been discussed at the
appropriate place during the chapter. But a summary of the overall merits and
demerits of using multicarrier transmission scheme for signal communication is
given in the following lines:
57
3.5.1 Merits
a. Efficient use of available spectrum by allowing overlap of subcarriers
resulting in high data rates.
b. Division of the channel into narrowband flat fading subchannels increases
resistance of the transmitted signal against flat-fading phenomenon compared to
single carrier systems.
c. The symbol loss occurring due to frequency-selectivity of the channel can be
reversed using channel coding and interleaving.
d. FFT/IFFT pair increases the computational efficiency of the system by
implementing modulation/demodulation blocks.
e. Less sensitivity to the sampling time offsets compared to FDM based
systems.
f. Adaptive equalization techniques make possible the channel equalization
more simpler than for FDM system.
g. Single Frequency Networks (SFN) facilitation using transmitter macro-
diversity.
h. A broad range of deployment in different frequency bands with a little
modification for the air interface.
i. High performance gain possible by integration of a suitable channel coding
scheme as turbo codes.
58
3.5.2 Demerits
a. Problem of high Peak-to-average Power ratio.
b. Sensitivity to carrier frequency offsets, requiring a suitable channel
estimation/equalization
c. Sensitivity to noise effects requiring channel coding at the expense of extra
bandwidth usage.
d. Sensitivity to Delay spread of the channel requiring the insertion of cyclic
prefix at the expense of more bandwidth usage.
3.6 Summary
In this chapter an introduction to OFDM system has been given alongwith putting
a light on the different aspects associated with it. Chapter starts with a brief
introduction to the multicarrier OFDM system and its historical background. Then
the mathematical modeling of the OFDM system is discussed with the side-by-side
explanation of the block diagram of the model. Next, the three major problems
associated with OFDM and addressed in the presented work have been discussed
alongwith their remedy. The chapter ends with the summary of the advantages and
disadvantages associated with using an OFDM system as a backbone for data
transmission.
59
Chapter-4
SIMULATING BEHAVIOUR OF WIRELESS CHANNEL USING SUI
CHANNEL PARAMETERS
4.1 Introduction
In the practical scenario, the wireless channel acts as a filter by suppressing some
frequencies and allowing others. The wireless signal propagation phenomenon in
both the indoor and outdoor environments represents a complex mechanism which
is influenced by a number of propagation parameters such as scattering, diffraction,
refraction and reflection [52]. All these parameters will be a topic under discussion
60
in this chapter. Adding to it, the mobility of the communicators has added more
challenges into the communication system design. The wireless communication
has gone through many major changes in the past few decades. The applications of
wireless communication were limited to the terrestrial links, broadcasting, space
communication etc till the last four decades or so, but in recent past mobile
telephony, wireless networking and Personal Communication Systems (PCS) are
dominating the market of the modern wireless communication systems.
Additive White Gaussian Noise (AWGN) is not able to present the channel
for the modern applications. It is because the presence of the Line-Of-Sight (LOS)
or non-LOS path in this type of channel is not obvious. Another important
parameter of consideration in Wireless channels is the multipath phenomenon. It
refers to the multiple copies of the signal being arriving at the receiver after
reflection, refraction, diffraction and scattering from different entities present in
the surrounding environment. This effect is shown in the Fig. 4.1.
Due to the phenomenon of multipath, a number of different copies of the
signal are received at the receiver. In the wired channel, also known as the
Gaussian channel, such phenomenon is not present. These channels are free from
such kind of effects. This is the reason that the amount of transmit power needed to
get the same BER performance in case of wired channel is much smaller compared
to the case of wireless channels.
61
Fig 5.1: A Typical Example of Multipath Phenomenon
4.2 Wireless Propagation Parameters
Here we will define some parameters about the wireless channel which quantify its
performance. A brief introduction to these common propagation parameters is
given below:
62
4.2.1 Reflection
Signal travels in the form of Electromagnetic (EM) waves. During propagation it
impinges upon different objects. If the physical size of the object is much greater
than the wavelength (λ) of the signal, then the EM waves are reflected from the
surface of the object. This phenomenon is termed as reflection.
4.2.2 Refraction
It refers to the change in the direction of the wave due to the change in the medium
of propagation. Refraction is a surface phenomenon and due to this effect the
frequency of the signal remains the same but its phase velocity changes. Snell’s
law governs the refraction phenomenon and is given by.
1
1
2
1
2
1
Sin
Sin (4.1)
Where θ1 and θ2 represents the incident and reflected angle, η1 and η2 represents
the indices of refraction of the two surfaces and ν1 and ν2 are the phase velocities of
the signal in the two media.
4.2.3 Diffraction
Whenever the EM wave strikes a sharp edge, there comes a sharp change in the
propagation path of the EM wave. This phenomenon is termed as diffraction.
4.2.4 Scattering
It refers to the bending of the wave from its defined trajectory when it encounters a
63
cluster of objects smaller in size than its wavelength (λ), such as foliage or water
vapors. Scattering causes multiple copies of the same EM wave to travel in various
directions.
4.2.5 Absorption
Absorption refers to the phenomenon of taking up of the energy of the EM wave
by the matter. This results in the shortening of the reachable range and attenuation
of the EM wave. Every medium has a certain absorption coefficient which depends
upon the attenuation coefficient, mass attenuation coefficient also sometimes
termed as mass extinction coefficient, penetration depth (skin effect), propagation
constant, complex dielectric constant etc. All these effects combine to generate the
absorption coefficient of the medium which has to act as the wireless channel for
the EM signal.
4.2.6 Polarization
Speaking generally, polarization refers to the orientation of wave in the space. Or
precisely, Polarization refers to the path followed by the tip of the electric field
component vector in the free space. Polarization of wave have a number of
applications in different areas e.g. particle size measurement, seismology,
electronics and antenna design for efficient radiation capture properties etc.
64
4.3 Types of Wireless Channels
Depending upon different parameters, wireless channels have been divided into a
number of types to represent different environments. Broadly speaking, depending
upon whether the communication between the transmitter and receiver is
influenced by the presence or absence of the Line-Of-Sight component, the
wireless channels are divided into two basic groups namely Rayleigh fading and
Rician fading. Discussion about both these type of fading models will be
performed in the following lines. Alongwith this, an introductory detail about the
theoretical model which is also sometimes used to check the performance of the
communication systems, the AWGN model, will also be a topic under
consideration in the following lines. In the terrestrial wireless communication
links, the signal travel from transmitter to receiver via multipaths which add to the
distortions occurring in the signal on top of the noise effect by different means. For
better modeling the combined effect of the multipath phenomenon and the
reflections taking place, the channel is modeled as wireless multipath fading
channel or microscopic fading channel. The dynamics which play a pivotal role in
the wireless fading channel are relative motion between transmitter and receiver,
multipath phenomenon, channel bandwidth, symbol duration, signal attenuation
etc.
4.3.1 AWGN Channel Model
Simplest of all, Additive White Gaussian Noise (AWGN) channel model is the one
which is modeled considering the combined noise effect taking place at the
receiver and channel. It is termed as additive since it gives us the additive effect or
65
wholesum effect of all the noises taking place, it is white since its effect is same for
all the frequency band, Gaussian refers to distribution of the noise. Inspite of the
fact that AWGN serves as an important reference for evaluating the performance
of the communication system, only applying AWGN parameters to the
communication model can drag the result far from the reality.
AWGN model can be considered accurate in the deep space communication
and the communication between the Earth and the satellite stations [53], but yet it
is far from real while considering the wireless terrestrial links.
Considering the transmitted signal x(t) influenced by a White Gaussian
Noise z(t), the received signal y(t) after its passage through the AWGN channel is
given by the following equation:
)()()( tztxLty (4.2)
In Eq. 4.2, L represents the power attenuation from the transmitter to the
receiver.
4.3.2 Multipath Rician Fading Channel Model
Whenever there is a strong stationary (non-fading) line-of-sight path present, the
small-scale fading envelope follows a rician distribution. Thus the rician
distribution models the received signal amplitude variation over the wireless
transmission links whenever the propagation environment contains a strong direct
line-of-sight component alongwith scatter components. It causes a non-zero mean
for the complex Gaussian distributed channel fading coefficients, which generates
a basic property of the rician distributed fading Since both the AWGN and
66
Rayleigh distributed fading can be considered as the limiting case of Rician
distributed fading, therefore the choice of rician fading is a typical general
approach for start of the analysis of the wireless communication channel [54].
The rician distribution is termed as a special case of the joint Gaussian PDF [55].
For the statistically independent random Gaussian variables X1 and X2, their joint
Gaussian PDF is given by :
22
21
2 XXR (4.3)
Similarly the PDF of the random fluctuations of the amplitude yields a non-central
PDF which has a two degree of freedom and is given by the following relation :
22
22
2 2exp
2
1)(
rs
Isr
rp (4.4)
Here 22 is the power in the scattered component, s2 shows us the scattered power
component and vI is the modified Bessel function of the vth order.
There is another parameter called K factor. This is termed as the ratio of the
specular power, s2 to the scattered power, 22 given by :
2
2
2s
K (4.5)
It is the value of the K-factor which indicates the tilt of the channel towards rician
distribution or otherwise. For the higher values of K-factor, the fading distribution
of the channel shows more tilt towards rician. This is the case in Stanford
University Interim Channel Models-1,2. Similarly if the value of the K-factor is
less than 1, the distribution of the fading is suppose to have more tilt towards
67
rayleigh.
Inspite of the fact that the rician distribution do not represent most of the
present day practical scenarios, but yet micro-cellular links and satellite links are
typical examples of rician distributed envelopes [56].
4.3.3 Multipath Rayleigh Fading Channel Model
Rayleigh fading model assumes that the LOS factor the signal is very weak or
faded away. Thus the communication between the transmitter and receiver is
dependent upon the multipaths being received at the receiver end after passing
through different paths and striking different objects in the environment. The
statistical time varying nature of the received envelope of the signal is described
mostly using Rayleigh distribution in the mobile radio channels.
Most of the present day communication channels exhibit rayleigh fading
distribution. Especially in the urban areas and hilly terrains where the direct LOS
path between the transmitter and receiver is not possible due to the sky scrappers
and high hills, these are only multipaths which make possible the communications
between the transmitter and receiver. The rayleigh fading distribution is formed by
the sum of two quadratic Gaussian noise signals. The same PDF as shown in eq.
4.4 is used for Rayleigh distribution as well. The difference is the value of eq. 4.5,
the rayleigh factor, K. Whenever the value of the K factor is less than 1, it means
that the specular power is less than the scattered power. Or in other words, the non-
LOS factors are much stronger than the LOS factors. This gives rise to a rayleigh
distribution fading parameters for the received signal. Rayleigh fading channel is
simulated in the MATLAB® as a combination of two quadratic Gaussian envelops
68
as follow:
)),(),((2
1)( nljrandnnlrandntr (4.6)
For evaluating the performance of the proposed model, SUI channel have
been used. Out of the six SUI channel model, SUI-4, 5, 6 are Rayleigh fading
channel models are depicted by the value of K parameter associated with these
channel models. The value of K for SUI-3 channel model is 1, thus its status is
ambiguous. The fading distribution contains both the qualities of the Rayleigh and
Rician distribution since the power of the LOS component and non-LOS
component is equal. It is believed that the Rayleigh fading distribution model is
reasonable for ionospheric and topospheric signal propagation and for heavily built
urban environments and hilly terrains for radio signals [57].
4.4 Simulating SUI Channel Models
Stanford University Interim (SUI) Channel Models [58], are six in number and
depict different environments starting from low populated rural environment to
thickly populated urban environments and high rising hilly terrains typical of the
United States continental. These channel models are considered as standards for
evaluating the performance of the communication systems in different
environments. In all these channel models the multipath fading is modeled as 3-
taps delay line with non-uniform delays. Each of this model has a K-factor the
value of which characterizes the fading distribution to be rayleigh or rician. Before
going into the implementation details of the model, the parameters of these channel
models are discussed first.
69
4.4.1 Parameters of SUI Channel Models
Simulation of SUI Channel models is based upon the work of the IEEE Broadband
Wireless Access Working Group [39] which was a dedicated and official forum
working on the simulating the SUI multipath channel models. As discussed
before, one of the key parameter in the explanation of these models is the K factor
which is measure of the tilt of the channel towards the Rician distribution.
Depending upon the value of the K, it is evident from Table 4.1 that in the first
three channel models namely SUI 1-3, the first multipath exhibits a Rician
behaviour with a dominant LOS factor. This LOS factor continue to fade till in the
SUI-3 model this factor is equivalent to the non LOS factor shown by the value of
K=1. These six channel models are divided into three groups basing upon the
outdoor terrains which these are representing. These three terrains are given below
alongwith a minor introduction to the environment they are representing.
Terrain-A = Hilly Terrains with thick tree density.
Terrain-B = Urban Environment with moderate tree density and
tall buildings.
Terrain-C = Rural Environment with light tree density and
buildings with low height.
Multipath fading in these channel models is modelled as 3-tap delay line with non-
uniform delays of the multipaths. K-factor, as discussed before, is the measure of
the Rayleigh component of the channel and equals ratio between Line-Of-Sight
(LOS) to the Non Line-Of-Sight component of the signal on linear scale. In the
Table-4.1, the K-Factor for these models have been given in linear scale for 90%
cell coverage. Greater the value of k , larger is the tilt of the channel coefficients
towards Rician distribution.
70
These three terrains A, B and C have further been subdivided into two
categories each. The first category in each of the subclass belongs to a relatively
transmission friendly environment while the second class depicts an environment
in which either LOS component is totally absent or is very weak. The scenario
under which the experiments performed which resulted in the figures given in
Table 4.1 are given in Table 4.2 [59].
Table 4.1: SUI 1-6 Channel Model Parameters
Channel
Model
Terrain
Type Parameter
Channel Taps
Antenna
Correlation
Gain
Reduction
Factor
(GRF) (dB)
Normalization
Factor (dB) Tap-1 Tap-2 Tap-3
SUI-1 C
Delay (µs) 0 0.4 0.9
0.7 0 -0.1771 Power (dB) 0 -15 -20
K-Factor 4 0 0
Doppler 0.4 0.3 0.5
SUI-2 C
Delay (µs) 0 0.4 1.1
0.5 2 -0.393 Power (dB) 0 -12 -15
K-Factor 2 0 0
Doppler 0.2 0.15 0.25
SUI-3 B
Delay (µs) 0 0.4 0.9
0.4 3 -1.5113 Power (dB) 0 -5 -10
K-Factor 1 0 0
Doppler 0.4 0.3 0.5
SUI-4 B
Delay (µs) 0 1.5 4
0.3 4 -1.9218 Power (dB) 0 -4 -8
K-Factor 0 0 0
71
Table 4.2: Underlying Scenario for Calculating SUI Channel Model Parameters
Cell Size 7 Km
BTS Antenna Height 30 m
BTS Antenna Beamwidth 120°
Receive Antenna Height 6 m
Receive Antenna Beamwidth 360° (Omni dir.)
Polarization Vertical Dir. only
Cell coverage is 90% with 99.9%
reliability at each covered location
Doppler 0.2 0.15 0.25
SUI-5 A
Delay (µs) 0 4 10
0.3 4 -1.5113 Power (dB) 0 -5 10
K-Factor 0 0 0
Doppler 2 1.5 2.5
SUI-6 A
Delay (µs) 0 14 20
0.3 4 -0.5683 Power (dB) 0 -10 -14
K-Factor 0 0 0
Doppler 0.4 0.3 0.5
72
The scenario in Table-4.2 considers an Omni-directional antenna in which the
receive antenna beamwidth is 360°. The cell coverage is considered as 90%.
Polarization is considered in the vertical direction only. Polarization refers to the
path followed by the electric field component vector in the free space. Antenna
correlation factor is considered in the case of MIMO channels. GRF refers to the
total mean power reduction for a 30° antenna compared to an omni directional
antenna. For a 30° antenna the GRF is included into the each path loss so that all
the paths are effected equally by the effect of local scattering. Since the cumulative
channel gain is not normalized therefore the normalization factor is to be added to
each tap in order to achieve 0 dB mean power.
There are a number of ways in which the SUI channel models can be
generated in the different simulators. The document in [60] is one of the key source
for simulating the SUI channels for the proposed system model. In this document,
the SUI channels have been simulated as a three tap multipath channels with the
particular power delay profile as mentioned and release by the group working on
these channel models. This document starts by calculating the power in the
constant and random components of the Rician/Rayleigh channel for each of the
three taps. Next step is to calculate the wholesum effect of the fading by combining
the individual effect of these taps, keeping the variance of the distribution equal to
one in order to avoid the coefficients of the Linear Constant Coefficient Different
Equation equal to zero. This process is performed for each of the data block till
whole of the user data is passed through the channel filter.
73
4.5 Summary
In this chapter an introduction to the wireless channel has been given. The chapter
starts with a brief introduction of the wireless channels. It then proceeds with an
introduction to the different propagation parameters related to the wireless
channels. Next the three commonly used wireless channels namely AWGN,
Rayleigh and Rician are discussed with due details and introductory level
implementation issues. Next section of this chapter deals with the introduction of
the SUI channel models. It explains the parameter of the SUI channels and gives
some details of the implementation issues related to these channels in MATLAB®.
74
Chapter-5
PROPOSED ALGORITHMS FOR CHANNEL ESTIMTION AND
EQUALIZATION
5.1 Introduction
Channel estimation refers to the process of estimating the channel impulse
response incorporated into the transmitted signal bits using the received signal bits.
Similarly equalization refers to the procedure of nullifying the effects of the
estimated impulse response of the channel from the received signal bits [61]. Thus
the channel estimation and equalization procedures are usually referred in the form
of a pair. In the present day communication systems, channel estimation and
equalization algorithms play a key role in the overall performance of the system
and its ability to perform under specific conditions/applications. One of the key
feature of the channel estimation is its ability of how accurately it can estimate the
impulse response of the channel from the received signal bits. The conditions of
the channel also allows the system designers to choose a particular algorithm for
estimating and equalization channel effects from the received signals. One point to
remember is that the basic purpose of channel estimation and equalization process
is to tackle with the impulse response effects induced by the channel into the
received signal bits, in order to neutralize the effects of the noise incorporated into
the received signal due to different means, channel coding is the strategy adopted
75
in the present days communication systems. Channel coding process has been
explained with due details in the Chapter-6.
5.2 Proposed Algorithms for Channel Estimation/Equalization
Broadly speaking, the channel estimation and equalization algorithms have been
divided into two types, pilot assisted and non-pilot assisted. The non-pilot assisted
channel estimation is also termed as blind estimation since depending upon the
channel estimation response calculated in the start of the communication session,
the same channel parameters are considered throughout the session for equalization
purposes.
Pilot-assisted channel estimation uses pilot tones for estimating the channel
impulse response from the received signal bits. The pilot bits serve as an extra
overhead on the system which is only used for making the system adaptable to the
changing conditions of the channel. There are different algorithms which are used
for estimating and equalizing the channel impulse response. A tree diagram
showing a broader classification of the different types of channel estimation
algorithms is given in Fig. 5.1.
76
Figure 5.1: A Broad Classification of Different Channel Estimation Techniques
In the classification shown in Fig. 5.1, the pilot assisted channel estimation is
broadly classified into two types, namely comb-type and block-type, depending
upon the ways pilot tones are inserted into the OFDM symbol. Explanation of
these pilot insertion techniques is given in next section. In our work we have used
two different algorithms for estimating the channel impulse response and then have
checked the performance of both these algorithms with two different pilot insertion
methods. Both these proposed algorithms are explained with sufficient details in
the lines given below.
5.2.1 Modified Lease Square (LS) Channel Estimation Algorithm
The basic principle of this algorithm is that is minimizes the square of the error
between the estimated value and the detected value to its least. No prior
probabilistic assumptions are required for LSE channel estimation algorithm [62].
77
A schematic view of the proposed modified LSE channel estimator is given
in Fig. 4. In the figure, the square of the calculated value E[n] has been minimized.
E[n] refers to the difference between the detection and estimation value. Equally
spaced pilot tones for channel estimation has been used in our proposed approach.
Two different types of pilot insertions are used and the performance using both
these estimation approaches have been quantified. Both these pilot insertion have
been explained with due details in the next section.
Figure 5.2: Modified LSE Channel Estimator
The equally spaced pilot data is given by:
78
1,...3,2,1.
0)()()(
LldataInf
lnXlnLXkX p
(5.1)
where L=N/Np and Xp(n) represents the value of the pilot at nth subcarrier.
First of all, in the proposed method the Fourier matrix F is calculated which
has the size NxN:
)1)(1()1(21
.
.
.
)1(2
.
.
.
.
.
.
4
.
.
.
2
.
.
.
)1(21
.......1
.......1
.......1
1.......111
1
NNNN
N
N
WWW
WWW
WWW
NF
(5.2)
Here W represents the Wronskian determinant [63] and its value is given by
Nj
eW
2
.
Next step is to define another matrix Fg of the order NpxN which contains Np
rows and N columns of unitary Fourier matrix F. This matrix is given by :
)1)(1()1(21
.
.
.
)1(2
.
.
.
.
.
.
4
.
.
.
2
.
.
.
)1(21
.......1
.......1
.......1
1.......111
1
NNNN
N
N
g
ppp WWW
WWW
WWW
NF
(5.3)
And ].........,....................,[ 21 npnnp fffF (5.4)
Where Fn represents the nth column of Fg which shows the pilot symbol
subcarrier. Finally, the LSE estimate of the channel at the pilot frequencies is given
79
by the following equation:
PH
PTH
TP
HTP
ep YFPPF
PF
h
(5.5)
Proof of this equation has been given in Appendix-B at the end of this chapter. In
this equation, Yp is the vector of received pilot symbols, H represents the Hermitian
transposition, and PT is the diagonal matrix containing the pilot values at the
diagonal positions, which is given by :
np
n
n
n
T
x
x
x
x
P
.......000
0.......00
0.......00
0.......00
.
.
.
.
.
.
.
.
.3
.
.
.
.
.
.
2
1
(5.6)
In the last phase, Hce , the final estimated channel impulse response matrix in the
frequency domain, is obtained using the following relation:
epH
gce hFH (5.7)
In order to estimate the channel impulse response at the positions which are
missing with the pilot tones, a suitable interpolation technique is applied. We have
used pilot tone at every 8th subcarrier position in our proposed model for comb
type case. A number of interpolation techniques can be applied in this regard in
order to get these channel impulse response. We have used low pass interpolation
technique in our work which has an improved performance over the
contemporaries.
After estimating the channel impulse response, next step is to do
80
equalization for the upcoming data symbols using the channel estimates which we
have received using the pilot data symbols. This is done by neutralizing the
channel impulse response effects by dividing he received OFDM symbol by the
channel impulse response matrix.
s
cee IK
kH
kYX
)(
)(
(5.8)
5.2.2 Modified Frequency-Domain Zero Forcing (ZF) Channel Estimation
Algorithm
The second channel estimation that we have used with our proposed model is the
modified frequency-domain zero forcing channel estimation algorithm. This
channel estimation algorithm is relatively more computational friendly compared
to the modified LSE channel estimation, but at the same time, in terms of the
performance, it shows some degradations in the higher modulation environments
when tested in the same environments as LSE.
Modified Frequency-domain Zero Forcing algorithms is called zero forcing
since it forces the ICI to zero value by using an upto infinite length filter for
channel estimation[64].
In order to mitigate the effect of the ICI completely, the frequency response
of the equalizer and channel should follow the following reciprocity formula:
tffHfH eqch 2
11)()(
(5.9)
where Heq(f) is the channel estimation matrix calculated by the equalizer at each
81
tap value of the channel while Hch(f) represents the folded frequency response of
the channel. Thus the proposed zero forcing equalizer works in the form of an
inverse filter by inversing the channel impulse response effecting the input data
symbols causing the ICI. This inverted channel impulse response matrix is then
used to nullify the ICI from the received OFDM symbol.
First step in the proposed algorithm is the introduction of the channel
impulse response matrix which is obtained from the received and send pilot data.
The equation governing the generation of channel impulse response matrix is
shown below [65]:
)(
)()(
fX
fYfH
pt
prce
(5.10)
where Ypr and Xpt are depicted in the figure.
Once the channel estimation matrix is calculated, the upcoming data is
equalized on symbol-to-symbol basis by dividing the received data symbol by the
channel impulse response matrix calculated in the first step. This is shown in the
equation given below:
)(
)()(
fH
fXfH
ce
dreq
(5.11)
In this equation, Xdr(f) represents the data sequence that is received at the reciever.
One of the major disadvantage of zero forcing equalizer is its inherent
property of noise enhancement. This property add to the fact that the performance
enhancement in the system due to the channel estimation is diluted due to the noise
enhancement factor of the inverse filter. This is the reason that in the proposed
82
model we have suggested the use of a suitable error correcting Turbo codes. Thus
the combination of a suitable error correcting codes like Turbo codes can improve
the performance many folds and this property has been proved in the simulation
results portion in which the performance of the proposed model has been compared
with and with-out the addition of turbo codes. The proposed Frequency domain
pilot-assisted Zero Forcing Channel Estimation algorithm is shown in Fig. 5.3.
Figure 5.3: Modified Zero Forcing Channel Estimator
5.3 Pilots Insertion Techniques and Their Effects
There are a number of ways in which pilot tones can be inserted into the OFDM
symbol. The placement of pilot tones has a great effect on the system’s
83
performance. Every method of pilot tones insertion has its own pros and cons in
the different scenarios. A few of the commonly used Pilot tones insertion
techniques are discussed below.
5.3.1 Block-type Pilot Insertion Method
In Block-type pilot insertion method, the pilot tones are concentrated in a single
OFDM symbol. In other words, in block-type pilot insertion method, some OFDM
symbols of all the data blocks are dedicated for sending pilot data. All subcarriers
of these OFDM symbols modulates pilot data which is used for estimating the
impulse response of the channel. Rest of the OFDM symbols of this data block
contains user data. Pictorial depiction of block type pilot insertion scheme is shown
in the Fig. 5.4.
Fig. 5.4 shows the schematic representation of the Block-type pilot insertion
method on a Frequency vs Time graph. This figure shows that every block of data
consists of five OFDM symbols. Each OFDM symbol contains a total of
hypothetically 10 number of subcarrier which have been modulated with the
digitally modulated data. In every data block, the first OFDM symbol is dedicated
for sending pilot data. All the subcarriers of this OFDM symbol contains pilot
tones. Rest of the OFDM symbols of this data block contain the user data.
84
Figure 5.4: Pictorial Depiction of Block-Type Pilot Insertion Method
The advantage of block type pilot insertion method is its robustness against
the slow fading in the static or quasi-static environments. The block-type pilot
insertion method practically calculates the channel estimates. Due to this property
it outperforms other pilot insertion methods being discussed in the next lines. But
this performance improvement is in particular environments. In our proposed
method, we have used a ratio 1:7 pilot tones insertion methodology for inserting
the pilots in the OFDM data block. This methodology is adopted due to its
improved performance [52]. So depending upon the channel estimates calculated
by the first pilot OFDM symbol, the data is equalized for the upcoming six user
data symbols. This methodology is further explained in the Chapter-7 which is
dedicated for discussion on the simulated model and the results of the proposed
model in different channel environments.
85
5.3.2 Comb-type Pilot Insertion Method
Comb-type pilot insertion method is applied whenever the channel is fast fading or
whenever the channel changes inside the OFDM symbol. This method is adopted
keeping in view the interpolation error which is generated in the result due to the
interpolation necessary at the positions which are missing with the pilot tones.
Graphic representation of the comb-type pilot insertion technique is given in the
Fig. 5.5.
Figure
86
5.5: Pictorial Depiction of Comb-Type Pilot Insertion Method
Figure 5.5 shows the schematic view of the comb-type pilot insertion method in an
OFDM symbol. In this method some subcarriers of all the OFDM symbols are
dedicated for sending pilot data. In this way all the OFDM symbols contain pilot
data for estimating the channel impulse response. This is the reason that the comb-
type pilot insertion method is more robust to the changing channel conditions
compared to the other pilot insertion techniques. But on the other hand, the
downside is the non-availability of the pilot tones for all the subcarrier positions as
it was the case for the block-type pilot insertion method. Due to this reason, the
comb type pilot insertion method is more prone to the errors which are caused due
to the process of interpolation at the positions which are missing with the pilot
tones. In the Fig. 5.3, the scheme shows that every 8th symbol is dedicated for
sending pilot data while rest of the seven OFDM symbols are dedicated for sending
user data. This is the scheme that we have followed in the proposed model for
checking the performance of the system with the comb-type pilot insertion method
with the proposed channel estimation schemes in the SUI multipath fading channel
environments. We have simulated the proposed model with both the anticipated
channel estimation schemes using both the block-type and comb-type pilot
insertion schemes and the performance of both these schemes is compared. Equally
space pilots have been used in our work because it enhances the overall
performance of the communication system [66].
5.3.3 Diagonal Pilot Insertion Method
87
Due to constraint of both the comb and block type pilot insertion methods,
Diagonal pilot insertion method is also been standardized and in use in some of the
standards e.g. 802.16e (WiMax) [67]. In this technique, the pilots are inserted in
the form of a diagonal in the OFDM symbol as shown in Fig. 5.6. The advantage
of diagonal pilot insertion method is the combination of the good properties of both
comb and block type methods, but the downside of this approach is the complex
two dimensional interpolation which is needed in order to estimate the channel
impulse response at the subcarrier position which are missing with the pilot tones.
This generates a two fold computational complexity in the system which prevents
its use for many real-time applications.
Figure 5.6:
Schematic View of Diagonal Pilot Insertion Method
5.3.4 Two Dimensional Pilot Insertion Method
88
Another method which combines the effect of both the comb type and block type
pilot insertion methods is the two dimensional pilot insertion scheme. In this
scheme, the pilots are inserted in the OFDM both in the comb-type manner and in
the block-type manner as shown in Fig. 5.7.
Figure 5.7:
Schematic View of Two Dimensional Pilot Insertion Method
Fig. 5.7 shows the pilot tones inserted in a two dimensional manner into the
OFDM symbol. This method shows a good performance in terms of accurately
estimating the channel impulse response but shows a worse performance in terms
of the bandwidth utilization. Since the pilots have to be inserted into the OFDM
symbol both in the horizontal and in vertical direction therefore the bandwidth
required for the control information needed into the system for the
estimation/equalization of channel impulse response is much higher compared to
the case of block or comb pilot insertion methods. Different versions of two
dimensional channel estimation scheme are used in different standards. The basic
aim of these variations of two dimensional channel estimation scheme is to
89
minimize the system overhead for estimation of the varying channel impulse
response parameters.
5.4 Summary
Both the channel estimation and equalization algorithms which have been used in
the proposed model for nullifying the effect of channel impulse response have been
explained with due details in the chapter. Alongwith this, the different types of
pilot insertion methods that can be used with the proposed schemes of the channel
estimation implemented in the anticipated model have also been discussed. Pros
and cons of each type of pilot insertion technique is also explained with more
details of the two types of channel estimation schemes that have been practically
implemented with the proposed model. Chapter ends by putting a light on the two
dimensional pilot insertion method.
90
88
Chapter-6
CHANNEL CODING
6.1 Introduction
During its passage through the noisy channel and the receiver, different types of
noises are incorporated into the signal which tend to degrade its quality. Thus there
is a need for such a mechanism which can nullify the effects of noise from the
received signal. This is the basic reason behind the concept of channel coding.
Channel coding represents the type of signal transformation techniques
which are used to improve the overall performance of the communication system
by enhancing the capability of the received signal to better survive various channel
and receiver impairments such as noise and interference effects [68]. Broadly
speaking, the coding is divided into two major types.
Source Coding: It refers to the Analogue to Digital conversion process taking
place in a communication system. This process converts the user generated
symbols into binary digits with minimum redundancy. This makes one of the
purpose of the source coding as compression. One of the famous source coder is
Huffman coder which converts the user generated symbols into bits depending
upon the probability of occurrence with minimum redundancy. Maximum bits are
assigned to least occurring symbols while minimum bits are assigned to the highest
occurring bits.
Channel Coding: It refers to the process of adding redundant information into the
message bits in order to cater-for the noise effects induced into the signal at
89
different levels. The noise effects are removed at the decoding stage using the
parity bits which were added into the message signal at the time of channel
encoding. These extra parity bits, which apparently serves as an extra bandwidth
usage, gathers channel information at the receiver side which is then used for the
efficient decoding of the bits.
Depending upon whether the channel codes have the capability of only
detecting the erroneous bits or correcting them as well, the channel coding is
divided respectively into two types, namely, Backward Error Correction (BEC)
codes and Forward Error Correction (FEC) Codes. FEC coding has been used in
our work with the proposed model of OFDM.
The history of channel coding goes back to the pioneering work of Claude E.
Shannon in 1948 [69]. In this work, Shannon developed a mathematical basis for
quantifying the noise prone communication channels. He developed the maximum
theoretical channel capacity for an error free transmission over noise
communication channel. But he refrained from giving any clue regarding the
channel codes which will make possible the attainment of this benchmark for error
free transmission over the noisy channel.
The equation developed by Shannon for calculating the channel capacity for
a band-limited channel erupted by channel noise effects is given below;
)1(log2 SNRBC (6.1)
Here, Channel Capacity, C is specified in bits/sec. channel capacity refers to the
maximum theoretical limit for the data rate which can be transmitted over a band-
limited channel prone to Additive White Gaussian Noise (AWGN).
90
A shortcoming of the Shannon work was noticed owing to the fact that a
corresponding increase in the information delay is observed as the redundancy is
increased in the system. But Shannon didn’t specify the tolerable amount of delay
in the system in order to make possible the communication near the Shannon limits
[70].
6.2 Classification of Channel Coding
As discussed earlier the channel coding is divided into two types, Forward Error
Correction (FEC) Coding and Backward Error Correction (BEC) Coding. The FEC
coding is further broadly classified into two types, both mentioned below:
i. Block Coding:
Block Coding refers to the FEC coding in which the present state of encoder
doesn’t depend upon the previous states. Rather the encoder is memoryless i.e.
there is no concept of Memory delay registers in block coding thus there is no
record of the history of the past encodings. Block codes are essentially useful for
setting boundary values and for studying the limitation of the codes in a unified
manner. In most of the present day systems, block codes have been replaced with
the more adaptive and decoding efficient convolutional codes. Examples of block
codes includes Golay codes, Hadamard codes, Reed-Solomon codes, Hamming
codes, Expander codes, Reed-Muller codes etc.
ii. Convolutional Coding:
Convolutional codes refer to the class of codes in which there is a concept of
91
memory of the encoder i.e. it is the class of codes in which the present state of
encoder depends upon the previous states as well. This dependency of coding on
the previous states emerge from the presence of right shift memory delay registers
which are one bit memory register that stores the previous state of encoder in the
form of binary digit. Convolutional codes usually refer to a modern class of
channel coding which has shown improved performance over the predecessors. A
very famous example of Convolutional codes is Turbo Codes which have captured
a place in most of the present day wireless standards. We have integrated Turbo
Codes in our proposed model of OFDM.
6.3 Turbo Codes- Brief History
As discussed earlier, the history of Turbo codes starts from the work of Shannon
presented in 1948[69]. Since the proposal of Shannon limit, research was going-on
to reach as near to the limit as possible. The research has given birth to a number
of coding schemes. The first of such FEC coding scheme was proposed in the form
of Hamming codes[71] in 1950 which was designed to detect and correct one bit
errors. The memoryfull Convolutional codes, were proposed in 1955 [72] and its
idea was put forth by Wozencraft , Elias and Reiffen [73, 74]. The major work in
the decoding algorithm of the convolutional codes was performed by Fano[75] and
Massey[76]. Similarly, Viterbi in 1967 [77] introduced the idea of Maximum
likelihood (ML) sequence estimation algorithm which is considered as a big
achievement in the convolutional coding scheme. Another implementation of
Viterbi algorithm was given by Forney[78] in 1973. The first application based
upon the convolutional codes was proposed by Heller and Jacobs in 1970s [79].
92
In 1993, Berrou, Glavieux and Thitimajshima [80] proposed a new class of
memoryfull Convolutional codes, which they termed as Turbo codes, whose Bit
Error Rate (BER) performance was close to Shannon limits. In the seven pages
history-making paper, they showed with the help of supporting analysis that it is
possible to work within 0.7 dB range of Shannon limits. The word “Turbo codes”
was borrowed by the authors from the famous Turbo engine, which feeds the
exhaust back into the input. Turbo codes also works in the same manner by feeding
the output back into the input to improve the overall performance of the coding.
Since their proposal, Turbo codes have gained tremendous attention by the
academic researchers and industrial implementers. A lot of work is carried-out in
the implementation of Turbo codes in a number of existing standards in order to
improve their performance.
6.4 Structure of Turbo Codes
The concept of Turbo codes is implemented at the decoding side. At the transmitter
end, the turbo encoder works in almost the same manner as its parents codes, the
convolutional codes. At the decoding end the Turbo
93
Fig. 6.1: A rate 1/3 PCCC Turbo Encoder
codes performs in an entirely different manner than the predecessors or
contemporaries codes. The structure of the Turbo codes, at transmitting and
receiving end, implemented in our proposed model is discussed in the following
lines.
6.4.1 Implementation Details of Turbo Encoder
Turbo Encoder has been implemented in the form of two or more concatenated
Recursive Systematic Convolutional Codes with an interleaver in between. The
structure of the implemented Turbo Encoder is given in Fig. 6.1.
Fig. 6.1 shows the structure of Turbo Encoder. Each of the component RSC
encoder is a systematic encoder of the type (n,k,v)=(2,1,3) where n shows the no.
of output bits, k is the number of input bits and v is the constraint length of the
encoder. This analogy shows that for every single input bit the encoder generates
94
two output bits with the constraint length of the encoder as 3. Similarly, when two
such encoders are concatenated in parallel via an interleaver, with the systematic
lines of both the encoders is same, they form a PCCC turbo encoder of the type
(3,1,3) which is depicted in Fig. 6.1.
Parallel concatenation is done primarily to facilitate the decoding of the bits
due to the fact that the presence of an interleaver in between the path of the parallel
concatenated component encoders will scramble the bits. This will allow the output
parity bit sequence to be uncorrelated. The hamming distance between the
uncorrelated bit sequence will be more. This will facilitate the decoder in
recognizing the bit on the basis of the probabilistic model and thus the overall
decoding efficiency of the system will increase.
Performance of the encoder depends greatly upon the interleaver used in the
system. The interleaver is there in the system to spread the burst error uniformly
over the whole bit sequence in order to save a single user or a group of user from
the effects of the noise generated by different means into the bit sequence. The
second use of the interleaver, as discussed in the last para, is to decorrelate the
outputs generated by the two component RSC encoders since it increases the
decoding efficiency. A detailed discussion on the design of different interleavers
and their performance is given in the following sections.
Generator matrix used in the feed forward and feed backward paths of the
identical component encoder also effects the overall performance of the system. In
our proposed model, we have used 1+D+D3 and 1+D+D2+D3 generator matrices at
the feed-forward and feed-backward paths due to their improved performance [81].
At the output, puncturing technique is optional to be applied in order to variate the
rate of the coding as per the choice or the system requirements. We have used 1/3
95
rate coding in our proposed model. D in Fig. 6.1 represents the Right Shift
Memory Delay Registers which are actually one bit memory which saves the
previous state of encoder and uses it as a seed value for the next state to generate
parity bits.
6.4.1.1 Trellis Diagram for the implemented Encoder structure
Trellis diagram for the implemented component RSC encoder is given in Fig. 6.3.
This diagram shows all the possible transitions in the encoder due to an input 1 or
0 for all the six encoder states. Fig. 6.2 shows the eight possible states in which the
encoder can be at the time there is an input bit for the encoder. Similarly the trellis
diagram for the implemented encoder showing the transitions for both the input 1
and 0 is shown in Fig. 6.3.
Fig. 6.2: The eight (08) Possible Encoder States for the Constraint Length 3 RSC Encoder
96
S1
S2
S3
S4
S5
S6
S7
S8
S1
S2
S3
S4
S5
S6
S7
S8
00
11
11
00
10
01
01
10
11
00
11
00
10
01
10
01
Fig. 6.3: Trellis diagram for implemented RSC Encoder (blue line represents transition due
to 0, red line represents transition due to 1)
Fig. 6.4: Trellis State Diagram for Information Bit Stream 10110101 Through First
Encoder
97
K=0 K=1 K=2 K=3 K=4 K=5 K=6 K=7
Systematic Bits 1 -1 1 1 -1 1 -1 1 Parity Bits(Enc-
1) 1 -1 -1 -1 1 1 -1 1
Fig. 6.5: Coded bits Through First Component Encoder
Fig. 6.4 and 6.5 shows the trellis diagram for an example input bit stream
10110101. In both these figures, the blue lines show state transition due to an input
0 while the red lines show state transition due to an input 1. In fig. 6.4, the output
bits due to each transition are shown on top of the transition arrow.
6.4.2 Turbo Decoding
The concept of Turbo codes lies at the decoding side. The basic concept
implemented at the decoding side of the turbo codes is the iterative mechanism
which is based on the probabilistic decoding method.
The decoding algorithms can be classified into two major categories,
Algebraic decoding and Probabilistic Decoding. Algebraic decoding is used
normally for relatively reliable channels which are less prone to noise effects[82].
These decoding algorithms promise virtually an error-free communication over
such channels. On the other hand, probabilistic decoding algorithms manages to
approach an optimal performance by utilizing the channel output in the best
98
possible manner. Probabilistic decoding methods are suited for relatively
unreliable channels which are exposed to noise effects. Probabilistic decoding
algorithms are generally based upon soft decoding of the bits in a number of steps
instead of taking hard decision in the very first step. The decoding algorithm that
we have implemented in our proposed model and which is explained in the
following lines is based upon the probabilistic decoding algorithm.
Brief explanation of the two famous probabilistic decoding algorithm is
given below.
6.4.2.1 Maximum A Posteriori (MAP) Decoding Algorithm:
We have used MAP decoding algorithm in our proposed model. Structure of the
implemented decoder is shown in Fig. 6.6. MAP algorithm is based upon
probabilistic decoding approach and utilizes two component decoders. The two
component MAP decoders, thereafter called MAP Dec-1 and MAP Dec-2,
calculates the Log Likelihood Ratio (LLR) of the a posteriori probability of the
generated by the other component decoder and then feeds this value to the other
component MAP decoder as it’s a priori probability. The LLR equation for each of
the component MAP decoder is given as :
)/1(
)/1(ln)(
1
1N
k
Nk
k yuP
yuPuLLR
(6.2)
There are three inputs to each of the component MAP decoder. The three inputs to
the first component MAP decoder are the systematic information, parity bits
generated by the first component RSC encoder at the transmitter side which have
99
been separated from the input message bit stream by dividing the input bit stream
by three and looking at the remainder via mod operator.
Cmpt Dec-1
Cmpt Dec-2
Ex-1
Ex-2
^2
Uk
Up1k
Up2k
Fig. 6.6: Structure of the implemented Turbo MAP Decoder based upon two component
Decoders
The value of the remainder determines whether the input bit is a systematic bit,
parity generated by the first component RSC encoder or second component RSC
encoder. The third input to the first component MAP decoder is the extrinsic
information generated by the second component MAP decoder fed to the first
component MAP decoder as it’s a priori information. Using these three inputs, the
first component MAP decoder generates its own extrinsic information as it’s a
posteriori probability which acts as a priori probability for the second component
MAP decoder and is fed to it via an interleaver.
The other two inputs to the second MAP decoder are the interleaved version
of the systematic information from the channel and the parity bits generated by the
second component RSC Convolutional encoder. Using these three inputs the
100
second component MAP decoder calculates its own extrinsic information which
acts as the a priori probability for the first component MAP decoder and is fed to it
via a deinterleaver. This process is repeated a number of times in the form of
iterations. After the defined number of iterations, which are preset by the system
designers keeping in view the conditions of the channel and a number of other
parameters, a hard decision is taken at the output of the component MAP decoder
no. 2. The hard decision is taken basing upon the sign of the LLR. Since log is an
operator which indicates power of a number, therefore a negative value of mantissa
specifies a decoded bit 0 while a positive value of mantissa shows that the decoded
bit is 1. These steps are shown in a step by step manner in the state diagram of the
MAP decoding algorithm given in Fig. 6.7. For a rate 1/n turbo codes, let )0(1tp and
)1(1tp be the a priori probability of the bit 0 and 1. These a priori probability has
been taken as ½ in the first iteration since it is unknown a priori. The Log
Likelihood ratio produced by the first component encoder is given by[83] :
Makes new estimate based on
information
Transfers estimate to
another decoder
Transfers estimate to
another decoder
Makes new estimate based on
information
Receives information from channel and second decoder
Receives information from channel and first
decoder
Data Input
Data Output
101
Fig. 6.7: State Diagram for Turbo MAP Decoding Algorithm
1
0,2
1
0
20,,
1'1
1
0,2
1
0
21,,
1'1
1
'
'
)()2
))((
exp()0()(
)()2
))((
exp()1()(
log)(
s
s
M
llt
n
jjtjt
tt
M
llt
n
jjtjt
tt
t
l
lxr
pl
l
lxr
pl
C
(6.3)
In this equation, the value α, β and γ is given as follows,
},{)( 1t
trt lSPl r
1
01
'1
'
},,{)(sM
l
tttrt lSlSPl r
after doing some mathematical manipulations, it can be written as
),().()( '
)1,0(
'1
01
'
lllli
it
M
ltt
s
(6.4) for
t=1,2,3,4,……….τ
for t=0 we have the boundary conditions αo(0)=1 and αo(l)=0 for l≠0
Next, βt(l) can be written as }|{)( 1 lSPl ttrt r
1
01
'1
'
}|,{sM
ltttr lSlSP r
102
after few mathematical manipulations it can be written as
)1,0(
'1
'1
01 ),()()(
' i
it
M
ltt llll
s
(6.5)
for t=τ-1, …………..2,1,0.
The boundary conditions are βτ(0)=1 and βτ(l)=0 for l ≠ 0
Similarly γ can be written as
'1
' |},,{),( lSlSiCPll ttttrit r
}{
},,,{'
1
'1
lSP
lSlSicP
tr
ttttr
r
after some mathematical simplifications the above equation can be written as :
otherwise
Bllfor
lxr
ipll it
n
j
ijt
ij
tit
0
),()2
))((
exp()(),( '2
1
0
2,,1
'
(6.6)
Now we get rewriting equation 6.3
1
0,2
1
0
20,,
200,0,
'1
1
0,2
1
0
21,,
210,0,
'1
1
1
1
'
'
)()2
))(()(
exp()(
)()2
))(()(
exp()(
log)0(
)1(log)(
s
s
M
llt
n
jjtjttt
t
M
llt
n
jjtjttt
t
t
tt
l
lxrxr
l
l
lxrxr
l
p
pc
(6.7)
For the systematic code in which 100, tx and 11
0, tx . Thus )(1 tC could be further
103
decomposed into
)(
2
)0(
)1(log)( 10,21
1
1 tett
tt cr
p
pc
(6.8)
where
1
0,2
1
0
21,,
'1
1
0,2
1
0
21,,
'1
1
'
'
)()2
))((
exp()(
)()2
))((
exp()(
log)(
s
s
M
llt
n
jjtjt
t
M
llt
n
jjtjt
t
te
l
lxr
l
l
lxr
l
c
(6.9)
In the above equation, )(1 te C is termed as the extrinsic information produced by
the first component MAP decoder and it is fed to the second component MAP
decoder as it’s a priori information. Using this a priori information, the second
component MAP decoder produces its own extrinsic information using equation
no. 6.9 and feeds it as a priori probability back to component MAP decoder no. 1.
In this way both the component MAP decoders calculates the extrinsic information
and feeds it to the other decoder improving the final estimate regarding the
decoded bit to be a 0 or 1. In this way after a predefined k no. of iterations a hard
decision is taken at the output of the component MAP decoder no. 2 and depending
upon sign of the LLR the bit is decoded as a 0 or 1. Fig. 6.8 shows the step-by-step
procedure in decoding of the bits using MAP decoding algorithm.
104
Fig. 6.8: Step-by-Step Information Exchange Between The Two Component MAP Decoders
Computational Complexity:
The computational complexity of Turbo decoder is given by the table 6.1[84]:
The table shows the number of operations for only a single iterations of the
MAP decoder. In this table, M = Total number of states of decoder. For constraint
length=3 the total number of states , M=8. Thus, for a single bit, total number of
operations are, 30 x 8 – 1 =239 operations.
Now we will calculate the number of operations for the different digital
modulation schemes used in the proposed model.
BPSK:
The data rate of the proposed model discussed in Chap. 7 using BPSK modulation
scheme is calculated as 11.57 Mbps. Thus, the total number of operations for
BPSK modulation scheme per second is given by
11.57 Mbps x 239 = 2.765 Gops
105
Table 6.1: Calculating Computational Complexity of MAP Algorithm
Operation Maximum a posteriori
(MAP) Algorithm
Maximization 2M-1
Addition 4M
Multiplication 10M
Table Look Up 0
Total Operation 14M
Total No. of Operations 30M-1
QPSK:
The data rate of the proposed model which is discussed in Chapter-7 using QPSK
modulation scheme is 23.15 Mbps. Thus the total number of operations for QPSK
modulation scheme per second is given by
23.15 Mbps x 239 = 5.536 Gops
16-QAM:
The data rate of the proposed model given in Chapter-7 with 16-QAM modulation
scheme is 46.25 Mbps. Thus the total number of operations for 16-QAM
modulation scheme per second are calculated as:
46.25 Mbps x 239 = 11.053 Gops
32-QAM:
The data rate of the proposed model given in Chapter-7 with 32-QAM modulation
scheme is 57.85 Mbps. Thus the total number of operations for 32-QAM
106
modulation scheme per second are calculated as:
57.85 Mbps x 239 = 13.826 Gops
64-QAM:
The data rate of the proposed model (Figure 5.7) for 64-QAM modulation scheme
is 69.37 Mbps.
Therefore the total number of operations for BPSK modulation scheme per second
is given by
69.37 x 239 = 16.579 Gops
6.4.2.2 Soft Output Viterbi Algorithm (SOVA):
Being a comparatively early algorithm for decoding the bit stream, used mainly for
decoding Convolutional codes, the Soft Output Viterbi Algorithm (SOVA) has
been derived from the Viterbi algorithm. The SOVA algorithm has incorporated
two basic changes from the conventional Viterbi algorithm [85]. The first
modification is the introduction of a soft output for the decoded bits and second is
the modification of the maximum likelihood path through the trellis in order to take
account of the a priori information regarding the decoded bits. The increased
degraded performance and the computational complexity of the SOVA algorithm
has put it in the back seat for use since the proposal of MAP algorithm. But SOVA
is briefly explained here to complete the discussion regarding the decoding
algorithms.
The first step is SOVA algorithm is selecting the survivor path for each time
instance t while passage through a particular node. Survivor path is one of the two
branches converging at the node. The survivor path is found using the famous
107
Baye’s rule given as :
)(
)()/()/(
yP
xPxyPyxP (6.10)
In the above equation, )(yP and )(xP represents the a priori probability for the
received and transmitted symbol respectively. If we assume that the a priori
probabilities are independent then we can replace the a priori of the transmitted bit
with the a priori probability of the sign vector u which is represented as )(uP , thus
rewriting the above equation we get,
K
kk
N
iii upxyp
yPyxP
11
)()/()(
1)/( (6.11)
If we consider the signals as antipodal, i.e. }1{ix , then the a priori LLR vectors
can be written as:
)1/(
)1/(log
ii
iici xyp
xypL ;
)1(
)1(log
k
kck up
upL (6.12)
Similarly LLR of the channel is written as
yc AL
2
2
(6.13)
Next, the conditional probability of the transmitted sequence is given as,
))(1
exp()/(2
xuCyxP
(6.14)
In the above equation, C is constant and u(x) is defined by
108
)..(2
)(2
ac LuLxxu (6.15)
Since if there are only two antipodal results, x and
x , possible for a given event,
then from the basic knowledge of probability theory we can write
)/(1)/( yxPyxP
(6.16)
and so the LLR is given by
))()((1
)/(
)/(log)(
2xuxu
yxP
yxPxL
(6.17)
Eq. 6.17 gives the final value of the LLR which is exchanged between the SOVA
based component decoders for improving the final estimate regarding the decoded
bits.
6.5 Summary
In this chapter, channel coding which has been integrated with the proposed model
of OFDM is explained with sufficient details. The contents of the chapter gives
details regarding the Forward Error Correcting Turbo Codes which have been
integrated into the proposed environment. Sufficient details have been given for
the modified Turbo Encoder which has been constructed using parallel
concatenation of RSC encoders with an interleaver in between. Similarly, the
Maximum a posteriori decoding algorithm, which is used in the proposed model
for decoding the received signal bits is also explained by putting a light on its pros
and cons. The predecessor of MAP algorithm, which is termed as SOVA algorithm
109
is also explained briefly and a comparison between the two is given in terms of
computational complexity and performance.
Chapter-7
SIMULATED MODEL AND RESULTS
7.1 Introduction
In this chapter the model of uncoded and turbo-coded OFDM which is used for
evaluating the performance of the proposed algorithm for channel estimation and
equalization has been discussed. First of all the block diagram of the model which
is simulated in the MATLAB® has been discussed. Then the results of the
proposed model in the uncoded and turbo coded environment has been discussed.
Each of this portion deals with the uncoded and turbo-coded OFDM results in the
environment of the two proposed channel estimation/equalization schemes. So the
combination of two different pilot insertion methods and two proposed channel
110
estimation/equalization techniques form four sets of results which are explicitly
discussed in detail in the following lines.
7.2 Simulated Model
The simulated model of OFDM which have been used to analyze the performance
of the proposed model is shown in the Fig. 7.1 [86]. This model shows a small sets
of modification to the basic model of OFDM shown in Fig. 3.3. The modifications
are done in terms of addition of the two blocks namely Turbo Encoder and Turbo
Decoder. Alongwith this, the channel Estimation/Equalization block has also been
added into the basic model of OFDM shown in Chapter-3. This block refers to the
two proposed channel
Figure 7.1: Proposed Model of Turbo-Coded OFDM with modified Channel
Estimation/Equalization Techniques
111
estimation/equalization techniques which have been integrated with the proposed
model and are already discussed in detail in Chapter-5. Similarly the Source
Encoder block has also been added in order to increase the tilt of the presented
model towards real implementation.
In the block diagram shown in Fig. 7.1, first of all the user data which is
labeled as data source is passed through Source Encoder. The source encoder
works as an A/D converter by converting the user analogue symbols into binary
digits. Any suitable encoder can be used here. One of the widely used source
encoder is Huffman encoder which converts the input analog symbols stream into
bits depending upon their probability of occurrence with minimum redundancy.
Next is to pass the bits through the Channel encoder. The channel encoder has been
simulated as a parallel concatenated convolutional encoder with two convolutional
encoders concatenated in parallel. The turbo encoder is a rate 1/3 encoder with
puncturing applied at the output. The structural details of the turbo encoder are
given in Chanter-6. The turbo coded bits which are now containing the parity
information as well are passed through the digital modulation block. This block
converts the binary digits into digital modulated symbols. We have used five
different digital modulation schemes in our work namely BPSK, QPSK, 16-QAM,
32-QAM and 64-QAM. These digital modulated symbols are mapped on the
constellation diagram.
The digitally modulated symbols are then passed through IFFT block.
Before doing this the symbols are converted from serial to parallel according to the
size of the IFFT. This step is termed as “OFDM modulation”. In IFFT block
digital symbols are modulated on the orthogonal subcarriers which are combined in
one envelope to form an OFDM symbol. During this process the pilot symbols are
112
inserted in parallel in order to make possible the channel estimation/equalization
process at the receiver.
Next step is to increase the immunity of the system against the channel delay
spread. Cyclic Prefix, also sometimes called guard interval, is inserted at the end of
every OFDM symbol in order to elongate the symbol in time-domain. This is done
by replicating the subcarriers from start to the end or vice-versa. Next is to pass the
symbol through multipath rayleigh/rician fading AWGN channel. The channel has
been simulated in two ways, firstly as a multipath Rayleigh fading channel and
then using the parameter of SUI channel models. Results have been shown for both
the cases. The details of the simulated channel and its parameters are discussed
with due details in Chapter 4.
At the receiving side, first of all the cyclic prefix is removed from the
received symbol. It is then passed through the serial to parallel block in order to
make it compatible with the size of the IFFT block. IFFT block tend to demodulate
the digital symbols from the orthogonal subcarriers and pass these symbols to the
parallel to serial block which reconverts the data from parallel to serial. Next, the
digital symbols are put to the frequency-domain pilot-assisted channel estimation.
If the data symbol is containing pilot subcarrier then estimation process is done at
this stage and if the data contains the user generated symbols then equalization is
performed here. After the removal of CFO, next process is to demodulate the
binary digits from the received OFDM symbols. This is done by passing the
symbols through the digital demodulation block.
Subsequently, the bits are passed through the turbo decoder. The decoder has
been constructed using two serially concatenated component decoders. These
decoders tend to gain from eachother’s information interchange. Detailed structure
113
of the turbo decoder is given in chapter-6. After removing the parity information
from the data bits and decoding the received bits in the optimum manner, next step
is the calculation of the BER from the received bits by passing it through the signal
comparator block and comparing the received and decoded bits with the sent bits.
The BER has been calculated for changing number of iterations of the turbo
decoder for the five different modulation schemes and the results have been
discussed.
First of all the results of the proposed model are discussed using a multipath
Rayleigh fading channel model as shown.
7.3 Simulation Results for Proposed model of Turbo-Coded/uncoded
OFDM with Frequency-Domain Pilot-Assisted Block-type Zero-
Forcing Channel Estimation Strategy Through Multipath Rayleigh
Fading Channel:
The channel has been simulated as a sum of two quadratic Gaussian sinusoids with
the variance equal to 1. In the next section, results have been shown for the
proposed model using the parameters of the six SUI channel models.
In the first step, the performance of the proposed model will be seen without
the aid of the FEC Turbo Codes. The corresponding Bit Error Rate (BER) curve is
shown in Fig. 7.2.
114
5 10 15 20 25 30 3510
-3
10-2
10-1
100
SNR (dB)
Ave
rage
BE
R
Comparative curve for OFDM with PACE for different modulation schemes
--OFDM-64QAM
--OFDM-16QAM--OFDM-QPSK
--OFDM-BPSK
Fig. 7.2: Performance of the Proposed Model with Uncoded OFDM and Proposed Zero-
Forcing Channel Estimation Through Multipath Rayleigh Fading Channel
The constellation diagram showing the constellation points for the proposed model
before and after their passage through the multipath Rayleigh fading channel have
already been shown in Fig. 3.8. The pilot-to-data symbol ratio has been taken as
1:6 in the proposed model with block-type pilot insertion method.
Next the performance of the proposed model has been shown with the turbo-
coded OFDM system with the proposed modified zero forcing channel estimation
using BPSK modulation scheme. The results for the proposed model are shown in
Fig. 7.3. Fig. 7.3 shows the performance of the proposed model with changing
number of iterations of the MAP decoder using BPSK modulation scheme through
the multipath Rayleigh fading channel. The number of iterations has a major
impact on the overall system’s performance.
When comparing the curves in the graph of the Fig. 7.3, it is eminent that the
performance of the system keeps on improving as the no. of iterations of the MAP
decoder are increased. It is obvious from the fact that the increasing no. of
115
iterations of the MAP decoder tend to deviate the result of LLR from the mean
zero which is calculated at the output of each of the component decoder of the
implemented MAP decoder. Due to this diversion of the value of the LLR from the
mean zero, it becomes easy for the decoder to decode the input bit with certainty as
a 1 or 0.
When compared with the uncoded BPSK curve of Fig. 7.2, the turbo-coded
curve for 20 iterations of MAP decoder shown in Fig. 7.3 shows a performance
improvement of 4.9 dB at an SNR of 10e-3 which goes to the credit of using turbo
codes with the proposed model. As discussed earlier, turbo codes tend to improve
the performance of the proposed model by exchanging a soft information between
the component decoders before making a final decision regarding the decoded bit
to be a 1 or 0.
The performance curve for the proposed model with the same turbo-coded
OFDM and proposed modified zero-forcing channel estimation strategy using
QPSK modulation scheme is shown in Fig. 7.4 below.
Performance curves for QPSK again reveals the same trend of curves by improving
the performance of the system as the no. of iterations of the MAP decoder are
increased. Considering the 4 iterations curve, when we compare the performance
of the proposed model with QPSK modulation scheme to the system model of [87],
our model shows a performance gain of 3.1 dB SNR at 10e-3 BER. This
improvement in performance goes to the credit of using block-type pilot insertion
method compared to the comb-type pilot insertion technique used in the system
proposed in [87]. As already discussed in Sec. 5.3, pilot insertion technique cast a
116
major effect on the overall performance of the system. The block-type pilot
insertion method that we have used in our proposed channel estimation algorithm,
calculates pilots practically for all the subcarrier positions of the OFDM system.
On the other hand, in [87], the comb-type pilot insertion method has been used
which suffers with an inherent interpolation error at the positions which are
missing with the pilot tones. At these positions an interpolation mechanism has to
estimate the channel estimates. Interpolation is by itself an approximation process
which suffers with the interpolation error. This interpolation error is prominent in
the results portion of [87] with a coding loss of 3.1 dB SNR at the BER of 10e-3.
Similarly the BER curves for the proposed model using the 16-QAM and
64-QAM modulation schemes is shown in Fig. 7.5 and Fig. 7.6 below. Both these
curves show the same general trend as the curve for the BPSK and QPSK
modulation schemes.
5 10 15 20 25
10-3
10-2
10-1
100
SNR (dB)
Ave
rage
BE
R
Comparison curve for different number of iterations of MAP Decoder for 16-QAM
1-iter
2-iter
4-iter8-iter
20-iter
Fig. 7.5: Performance of the Proposed Turbo-Coded OFDM Model with 16-QAM
117
Modulation Scheme and Modified Zero-Forcing Channel Estimation Through Multipath
Rayleigh Fading Channel
5 10 15 20 25 30
10-2
10-1
100
SNR (dB)
Ave
rage
BE
R
Comparison curve for different number of iterations of MAP Decoder for 64-QAM
1-iter
2-iter
4-iter8-iter
20-iter
Fig. 7.6: Performance of the Proposed Turbo-Coded OFDM Model with 64-QAM
Modulation Scheme and Modified Zero-Forcing Channel Estimation Through Multipath
Rayleigh Fading Channel
7.4 Simulation Results for Proposed model of Turbo-Coded/uncoded
OFDM with Frequency-Domain Pilot-Assisted Block-type Zero-
Forcing Channel Estimation Strategy:
In the next lines the performance of the proposed model has been shown with
uncoded and turbo-coded OFDM through SUI Channel Models. The frequency-
domain pilot-assisted block-type zero-forcing channel estimation strategy has been
used in the proposed model. As mentioned by the name, the pilots have been
inserted using the block-type insertion method in the OFDM symbol. The OFDM
symbol has been modulated using five different digital modulation schemes and is
passed through the six SUI channel models. The performance comparison of the
118
proposed model using the six channel models for the uncoded OFDM system is
given in the following lines.
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
Uncoded OFDM with different modulation schemes through SUI-1 Channel
SNR(dB)
BE
R
BPSK with ZFQPSK with ZF16-QAM with ZF32-QAM with ZF64-QAM with ZF
Fig. 7.7: Performance of the Proposed Model with Uncoded OFDM and Block-Type Zero-
Forcing Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
Uncoded OFDM with different modulation schemes through SUI-2 Channel
SNR(dB)
BE
R
BPSK with ZFQPSK with ZF16-QAM with ZF32-QAM with ZF64-QAM with ZF
Fig. 7.8: Performance of the Proposed Model with Uncoded OFDM and Block-Type Zero-
119
Forcing Channel Estimation Through SUI-2 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
Uncoded OFDM with different modulation schemes through SUI-3 Channel
SNR(dB)
BE
R
BPSK with ZFQPSK with ZF16-QAM with ZF32-QAM with ZF64-QAM with ZF
Fig. 7.9: Performance of the Proposed Model with Uncoded OFDM and Block-Type Zero-
Forcing Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
Uncoded OFDM with different modulation schemes through SUI-4 Channel
SNR(dB)
BE
R
BPSK with ZFQPSK with ZF16-QAM with ZF32-QAM with ZF64-QAM with ZF
Fig. 7.10: Performance of the Proposed Model with Uncoded OFDM and Block-Type Zero-
120
Forcing Channel Estimation Through SUI-4 Channel Model
0 5 10 15 20 25 30 35 40
10-4
10-3
10-2
10-1
100
Uncoded OFDM with different modulation schemes through SUI-5 Channel
SNR(dB)
BE
R
BPSK with ZFQPSK with ZF16-QAM with ZF32-QAM with ZF64-QAM with ZF
Fig. 7.11: Performance of the Proposed Model with Uncoded OFDM and Block-Type Zero-
Forcing Channel Estimation Through SUI-5 Channel Model
121
0 5 10 15 20 25 30 35 40
10-4
10-3
10-2
10-1
100
Uncoded OFDM with different modulation schemes through SUI-6 Channel
SNR(dB)
BE
R
BPSK with ZFQPSK with ZF16-QAM with ZF32-QAM with ZF64-QAM with ZF
Fig. 7.12: Performance of the Proposed Model with Uncoded OFDM and Block-Type Zero-
Forcing Channel Estimation Through SUI-6 Channel Model
By closely observing the six figures (Fig. 7.7-7.12), one can see that the basic trend
followed by the five modulation schemes is the same. In all these graphs, the BER
is showing a decreasing trend as the transmit power is increased. In all these
graphs, BPSK outperforms rest of the four modulation schemes. The reason lies in
the close placement of the constellation points in the higher modulation schemes.
Due to these close placements of the constellation points in these modulation
schemes, the constellation points are more prone to be got corrupted during their
passage through the fading AWGN channel. In the case of BPSK, the two
constellation points are 180° apart, while in QPSK, the angular separation between
any two consecutive points out of the four is 90°. This is the reason that in BPSK
there are less chances of corruption of data as compared to the other modulation
schemes. This fact is also obvious from the simulation results given in the above
122
graphs.
Another notable parameter in these curves is performance degradation of the
proposed model as we move towards higher SUI channels. It is because of the tilt
of the channel models from the Rician towards Rayleigh due to the fading LOS
component present in the Rician SUI channel models. As shown in Table-4.1, for
the SUI-1 channel model, it shows a strong Rican behaviour as shown by the value
of the K-factor as 4 for the first multipath component. But by moving from SUI-1
to SUI-2 and then to SUI-3 it can be observed the rician component of the channel
gets weaker till it completely vanishes in SUI-4 which is a perfect Rayleigh fading
channel with the specular power component almost zero compared to the scattered
power of the multipaths.
Next is to investigate the proposed model of turbo-coded OFDM for its
performance with frequency-domain pilot-assisted block-type channel estimation
strategy using BPSK modulation scheme through the six SUI channel models. In
the following lines the performance of the proposed model is mentioned.
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with BPSK through SUI-1 channel
SNR(dB)
BE
R
Coded BPSK+ZF+itr=1Coded BPSK+ZF+itr=2Coded BPSK+ZF+itr=4Coded BPSK+ZF+itr=6Coded BPSK+ZF+itr=10Coded BPSK+ZF+itr=20
Fig. 7.13: Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And
123
Block-Type Zero-Forcing Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-2 channel
Coded BPSK+ZF+itr=1Coded BPSK+ZF+itr=2
Coded BPSK+ZF+itr=4
Coded BPSK+ZF+itr=6
Coded BPSK+ZF+itr=10Coded BPSK+ZF+itr=20
Fig. 7.14: Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-2 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-3 channel
Coded BPSK+ZF+itr=1Coded BPSK+ZF+itr=2
Coded BPSK+ZF+itr=4
Coded BPSK+ZF+itr=6
Coded BPSK+ZF+itr=10Coded BPSK+ZF+itr=20
Fig. 7.15: Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-3 Channel Model
124
0 5 10 15 20 25 30 35 4010
-6
10-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-4 channel
Coded BPSK+ZF+itr=1
Coded BPSK+ZF+itr=2
Coded BPSK+ZF+itr=4Coded BPSK+ZF+itr=6
Coded BPSK+ZF+itr=10
Coded BPSK+ZF+itr=20
Fig. 7.16: Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-4 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-5 channel
Coded BPSK+ZF+itr=1
Coded BPSK+ZF+itr=2
Coded BPSK+ZF+itr=4
Coded BPSK+ZF+itr=6
Coded BPSK+ZF+itr=10
Coded BPSK+ZF+itr=20
Fig. 7.17: Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-5 Channel Model
125
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-6 channel
Coded BPSK+ZF+itr=1
Coded BPSK+ZF+itr=2
Coded BPSK+ZF+itr=4
Coded BPSK+ZF+itr=6
Coded BPSK+ZF+itr=10
Coded BPSK+ZF+itr=20
Fig. 7.18: Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-6 Channel Model
In the figures above, the performance of the proposed model is shown with
1,2,4,6,10 and 20 iterations of the MAP decoder. It is clear that the performance of
the system tend to show a degradation as we move from SUI-1 to SUI-2, 3, 4, 5
and SUI-6. It is because of the K-factor which tend to fade as we move towards
higher SUI channels.
Another important behaviour evident from the results of the system's
performance is that the system shows more improvement in the first few iterations
of the MAP decoder after which the performance attains a considerable static
behaviour. The value of the LLR diverges from mean zero towards the negative or
positive side during the first few iterations, next iterations tend to confirm the
results of the first iterations and there are few chances for the LLR to cross the
decision boundary, changing its value from negative to positive or vice-versa. Due
to this reason we observe a rapid improvement in the proposed system’s
performance in the first few iterations while this performance improvement show a
126
decreasing behaviour with increasing number of MAP decoder iterations.
The performance of the proposed model of turbo-coded OFDM with above-
mentioned channel estimation is shown using QPSK modulation scheme through
the six SUI channel models in the following lines.
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with QPSK through SUI-1 Channel
SNR(dB)
BE
R
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
Fig. 7.19: Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-2 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
127
Fig. 7.20: Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-2 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-3 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
Fig. 7.21: Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-4 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
128
Fig. 7.22: Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-4 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-5 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
Fig. 7.23: Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 40
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-6 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
129
Fig. 7.24: Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-6 Channel Model
When performance of the proposed model using QPSK modulation scheme
is compared with BPSK modulation scheme, a slight degradation in the
performance can be observed. It is because of the fact that the four constellation
points in QPSK are 90° apart compared to the 180° angular separation between the
constellation points of BPSK. When compared with BPSK, QPSK also shows a
slight deterioration in the performance as the characteristics of the succeeding
channels diverge from rician towards rayleigh distribution because of the loss of
the LOS component of the signal.
Correspondingly, when the performance of the proposed model is observed
with 16-QAM modulation scheme using the same frequency-domain pilot-assisted
block-type channel estimation scheme, the results are shown in Fig. 7.25, 7.26,
7.27, 7.28, 7.29 and 7.30.
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with 16-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 16-QAM+ZF+itr=1Coded 16-QAM+ZF+itr=2Coded 16-QAM+ZF+itr=4Coded 16-QAM+ZF+itr=6Coded 16-QAM+ZF+itr=10Coded 16-QAM+ZF+itr=20
130
Fig. 7.25: Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-2 Channel
Coded 16-QAM+ZF+itr=1Coded 16-QAM+ZF+itr=2Coded 16-QAM+ZF+itr=4Coded 16-QAM+ZF+itr=6Coded 16-QAM+ZF+itr=10Coded 16-QAM+ZF+itr=20
Fig. 7.26: Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-2 Channel Model
131
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-3 Channel
Coded 16-QAM+ZF+itr=1Coded 16-QAM+ZF+itr=2Coded 16-QAM+ZF+itr=4Coded 16-QAM+ZF+itr=6Coded 16-QAM+ZF+itr=10Coded 16-QAM+ZF+itr=20
Fig. 7.27: Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And
Block-Type Zero-Forcing Channel Estimation Through SUI-3 Channel Model
When one compare the Fig. 7.25 with the corresponding Fig. 7.19, which
shows the performance of the proposed model using QPSK modulation, a
degradation in the performance is can be observed while moving towards higher
modulation schemes. 10e-3 performance is attained by the proposed system of
turbo-coded OFDM at 35.5 dB in Fig. 7.25 when using 16-QAM digital mapping
while the same performance in terms of BER can be achieved from the system
using QPSK at 30.5 dB in Fig. 7.19 considering 20 iteration curve in each case.
The performance degradation goes to the credit of using higher modulation
schemes keeping the other parameters same in both the cases.
Similarly, the performance of the proposed model using 32-QAM and 64-
QAM modulation schemes is shown for the six SUI channel models in Fig. 7.31-
7.42.
132
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
Turbo-Coded OFDM with 32-QAM through SUI-1
SNR(dB)
BE
R
Coded 32-QAM+ZF+itr=1
Coded 32-QAM+ZF+itr=2
Coded 32-QAM+ZF+itr=4Coded 32-QAM+ZF+itr=6
Coded 32-QAM+ZF+itr=10
Coded 32-QAM+ZF+itr=20
Fig. 7.31: Performance of the Turbo-Coded OFDM With 32-QAM Modulation Scheme
And Block-Type Zero-Forcing Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-4
Coded 32-QAM+ZF+itr=1Coded 32-QAM+ZF+itr=2Coded 32-QAM+ZF+itr=4Coded 32-QAM+ZF+itr=6Coded 32-QAM+ZF+itr=10Coded 32-QAM+ZF+itr=20
Fig. 7.34: Performance of the Turbo-Coded OFDM With 32-QAM Modulation Scheme
And Block-Type Zero-Forcing Channel Estimation Through SUI-4 Channel Model
133
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-5
Coded 32-QAM+ZF+itr=1Coded 32-QAM+ZF+itr=2Coded 32-QAM+ZF+itr=4Coded 32-QAM+ZF+itr=6Coded 32-QAM+ZF+itr=10Coded 32-QAM+ZF+itr=20
Fig. 7.35: Performance of the Turbo-Coded OFDM With 32-QAM Modulation Scheme
And Block-Type Zero-Forcing Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-6
Coded 32-QAM+ZF+itr=1Coded 32-QAM+ZF+itr=2Coded 32-QAM+ZF+itr=4Coded 32-QAM+ZF+itr=6Coded 32-QAM+ZF+itr=10Coded 32-QAM+ZF+itr=20
Fig. 7.36: Performance of the Turbo-Coded OFDM With 32-QAM Modulation Scheme
And Block-Type Zero-Forcing Channel Estimation Through SUI-6 Channel Model
134
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
Turbo-Coded OFDM with 64-QAM through SUI-3 Channel
SNR(dB)
BE
R
Coded 64-QAM+ZF+itr=1Coded 64-QAM+ZF+itr=2Coded 64-QAM+ZF+itr=4Coded 64-QAM+ZF+itr=6Coded 64-QAM+ZF+itr=10Coded 64-QAM+ZF+itr=20
Fig. 7.39: Performance of the Turbo-Coded OFDM With 64-QAM Modulation Scheme
And Block-Type Zero-Forcing Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-4 Channel
Coded 64-QAM+ZF+itr=1Coded 64-QAM+ZF+itr=2Coded 64-QAM+ZF+itr=4Coded 64-QAM+ZF+itr=6Coded 64-QAM+ZF+itr=10Coded 64-QAM+ZF+itr=20
Fig. 7.40: Performance of the Turbo-Coded OFDM With 64-QAM Modulation Scheme
And Block-Type Zero-Forcing Channel Estimation Through SUI-4 Channel Model
135
7.5 Simulation Results for Proposed Model of Turbo-Coded/Uncoded
OFDM with Frequency-Domain Pilot-Assisted Block-type Modified
Least Square Channel Estimation Strategy:
When frequency-domain pilot-assisted block-type modified Least square channel
estimation strategy is tested with the proposed model of Turbo-coded OFDM the
results that we obtained using the six SUI channel models are shown in the
following lines.
Firstly the proposed model is tested with the above mentioned channel
estimation strategy without applying turbo codes and the performance is measured
using the six SUI channel models. The proposed system showed the same
performance for the six channel models using uncoded OFDM system as shown in
Fig. 7.43.
Fig. 7.43 shows that the proposed algorithm works well for uncoded OFDM
model provided the angular separation between the constellation points remains
atleast 90°. But for higher modulation schemes, the presented modified LSE
algorithm is not able to estimate the channel impulse response well for the uncoded
OFDM system. The performance of the proposed OFDM model without the
addition of channel coding, depicted in Fig. 7.43 for SUI-1 channel model is
replicated for rest of the five channel models as well but is not shown here to avoid
duplication.
136
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
Uncoded OFDM with different modulation schemes through SUI-1 Channel with presented LSE algorithm
SNR(dB)
BE
R
BPSK with LSQPSK with LS16-QAM with LS32-QAM with LS64-QAM with LS
Fig. 7.43: Performance of the Proposed Model with Uncoded OFDM And Presented Block-
Type LSE Channel Estimation Through SUI-1 Channel Model
When the same parameters were tested for the proposed model using turbo-
coded OFDM system with BPSK digital modulation, the results are shown in the
following lines.
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with BPSK through SUI-1 Channel
SNR(dB)
BE
R
Coded BPSK+LS+itr=1Coded BPSK+LS+itr=2Coded BPSK+LS+itr=4Coded BPSK+LS+itr=6Coded BPSK+LS+itr=10Coded BPSK+LS+itr=20
Fig. 7.44: Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And
137
Block-Type LSE Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-4 Channel
Coded BPSK+LS+itr=1Coded BPSK+LS+itr=2Coded BPSK+LS+itr=4Coded BPSK+LS+itr=6Coded BPSK+LS+itr=10Coded BPSK+LS+itr=20
Fig. 7.47: Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-4 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-5 Channel
Coded BPSK+LS+itr=1Coded BPSK+LS+itr=2Coded BPSK+LS+itr=4Coded BPSK+LS+itr=6Coded BPSK+LS+itr=10Coded BPSK+LS+itr=20
138
Fig. 7.48: Performance of the Turbo-Coded OFDM with BPSK Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-5 Channel Model
The graphs from Fig. 7.44-7.49 demonstrates the performance of the
proposed turbo-coded OFDM model with BPSK modulation scheme through the
six SUI Channel models. The performance of the system depicts a small
degradation in the systems performance as compared with the proposed model with
BER curves using zero-forcing channel estimation. But overall trend of the curves
shown in the Figs. 7.44-7.49 depicts that the coding gain in the performance of the
system shows a decreasing trend when the iterations of the MAP decoder are
increased 1 to 2 and then onwards. The reason of this decrease in the coding gain is
the divergence of the LLR from the zero mean towards negative or positive side
depicting a decoded 0 or 1. Subsequent iterations confirms the existing results and
adds very small new to it.
When the same parameters are applied to the proposed model with QPSK
digital modulation scheme and LSE channel estimation scheme, the following
results are observed:
139
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with QPSK through SUI-1 Channel
SNR(dB)
BE
R
Coded QPSK+LS+itr=1Coded QPSK+LS+itr=2Coded QPSK+LS+itr=4Coded QPSK+LS+itr=6Coded QPSK+LS+itr=10Coded QPSK+LS+itr=20
Fig. 7.50: Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-2 Channel
Coded QPSK+LS+itr=1Coded QPSK+LS+itr=2Coded QPSK+LS+itr=4Coded QPSK+LS+itr=6Coded QPSK+LS+itr=10Coded QPSK+LS+itr=20
Fig. 7.51: Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-2 Channel Model
140
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-6 Channel
Coded QPSK+LS+itr=1Coded QPSK+LS+itr=2Coded QPSK+LS+itr=4Coded QPSK+LS+itr=6Coded QPSK+LS+itr=10Coded QPSK+LS+itr=20
Fig. 7.55: Performance of the Turbo-Coded OFDM with QPSK Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-6 Channel Model
The performance curves for the QPSK digital modulation through the six
SUI channel models depicts a small performance degradation compared to the six
graphs of the BPSK using the same parameters, channel models and error
correction technique.
Next is to show the performance of the proposed model using 16-QAM
modulation scheme.
141
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with 16-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.56: Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-2 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.57: Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-2 Channel Model
142
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-3 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.58: Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-4 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.59: Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-4 Channel Model
143
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-5 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.60: Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-6 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.61: Performance of the Turbo-Coded OFDM with 16-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-6 Channel Model
The performance of the proposed model with 32-QAM and 64 QAM is given
144
below.
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
Turbo-Coded OFDM with 32-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.62: Performance of the Turbo-Coded OFDM with 32-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-2 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.63: Performance of the Turbo-Coded OFDM with 32-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-2 Channel Model
145
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-3 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.64: Performance of the Turbo-Coded OFDM with 32-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-4 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.65: Performance of the Turbo-Coded OFDM with 32-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-4 Channel Model
146
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-5 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.66: Performance of the Turbo-Coded OFDM with 32-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-6 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.67: Performance of the Turbo-Coded OFDM with 32-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-6 Channel Model
147
Using the same parameters the performance curves for the proposed model with
64-QAM modulation scheme are given below.
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
Turbo-Coded OFDM with 64-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.68: Performance of the Turbo-Coded OFDM with 64-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-2 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.69: Performance of the Turbo-Coded OFDM with 64-QAM Modulation Scheme And
148
Block-Type LSE Channel Estimation Through SUI-2 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-3 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.70: Performance of the Turbo-Coded OFDM with 64-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-4 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
149
Fig. 7.71: Performance of the Turbo-Coded OFDM with 64-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-4 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-5 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.72: Performance of the Turbo-Coded OFDM with 64-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-5 Channel Model
150
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-6 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.73: Performance of the Turbo-Coded OFDM with 64-QAM Modulation Scheme And
Block-Type LSE Channel Estimation Through SUI-6 Channel Model
7.6 Simulation Results for Proposed Model of Turbo-Coded/Uncoded
Frequency-Domain Pilot-Assisted Comb-type Zero-Forcing Channel
Estimation Strategy:
As already discussed in chapter-6, the comb-type pilot insertion method
suffers from an inherent interpolation error at the positions which are missing with
the pilot tones. This effect is evident in the results presented in this section. When
the proposed model of turbo-coded OFDM is checked for BER performance with
frequency-domain pilot-assisted comb-type zero forcing channel estimation
strategy, the results show a difference in the performance of the system compared
with the block-type pilot-inserted channel estimation. Especially the inherent error
into the
151
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Uncoded OFDM through SUI-1 Channel
BPSK with ZFQPSK with ZF16-QAM with ZF32-QAM with ZF64-QAM with ZF
Fig. 7.74: Performance of the Uncoded OFDM with Comb-Type Zero-Forcing Channel
Estimation Through SUI-1 Channel Model
system due to the interpolation at the subcarrier positions missing with the pilot
tones is also prominent. The BER performance curves for the proposed uncoded
OFDM model using the above mentioned algorithm are shown in the following
graph of Fig. 7.74.
The curve in the Fig. 7.74 shows the performance of the proposed model
with uncoded OFDM system using frequency-domain pilot-assisted comb-type
zero forcing channel estimation strategy through SUI-1 channel. Performance
curves for the rest of the five channel models show almost the same trend so are
not shown here to avoid repetition. It is evident from the presented curves that the
proposed channel equalization algorithm deals well with the lower modulation
schemes having angular separation between the constellation points equal to or
greater than 90°.i.e. BPSK and QPSK. While for higher modulation schemes e.g.
16-QAM, 32-QAM and 64-QAM the channel impulse response estimation is not
possible using the presented algorithm. This effect is also evident from the curves
showing the performance of the system for 16, 32, 64-QAM using the presented
152
algorithm in the coming lines.
When the similar parameters are used for turbo-coded BPSK modulated
OFDM system, results are shown below bearing almost same general trends of the
curves as are depicted for the uncoded OFDM system.
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-1 Channel
Coded BPSK+ZF+itr=1Coded BPSK+ZF+itr=2Coded BPSK+ZF+itr=4Coded BPSK+ZF+itr=6Coded BPSK+ZF+itr=10Coded BPSK+ZF+itr=20
Fig. 7.75: Performance of the Turbo-Coded OFDM with BPSK modulation scheme using
comb-type zero-forcing channel estimation through SUI-1 Channel model
0 5 10 15 20 25 30 35 40
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-2 Channel
Coded BPSK+ZF+itr=1Coded BPSK+ZF+itr=2Coded BPSK+ZF+itr=4Coded BPSK+ZF+itr=6Coded BPSK+ZF+itr=10Coded BPSK+ZF+itr=20
Fig. 7.76: Performance of the Turbo-coded OFDM with BPSK modulation scheme using
comb-type zero-forcing channel estimation through SUI-2 Channel model
153
0 5 10 15 20 25 30 35 40
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-3 Channel
Coded BPSK+ZF+itr=1Coded BPSK+ZF+itr=2Coded BPSK+ZF+itr=4Coded BPSK+ZF+itr=6Coded BPSK+ZF+itr=10Coded BPSK+ZF+itr=20
Fig. 7.77: Performance of the Turbo-coded OFDM with BPSK modulation scheme using
comb-type zero-forcing channel estimation through SUI-3 Channel model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-4 Channel
Coded BPSK+ZF+itr=1Coded BPSK+ZF+itr=2Coded BPSK+ZF+itr=4Coded BPSK+ZF+itr=6Coded BPSK+ZF+itr=10Coded BPSK+ZF+itr=20
Fig. 7.78: Performance of the Turbo-coded OFDM with BPSK modulation scheme using
comb-type zero-forcing channel estimation through SUI-4 Channel model
154
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-5 Channel
Coded BPSK+ZF+itr=1Coded BPSK+ZF+itr=2Coded BPSK+ZF+itr=4Coded BPSK+ZF+itr=6Coded BPSK+ZF+itr=10Coded BPSK+ZF+itr=20
Fig. 7.79: Performance of the Turbo-coded OFDM with BPSK modulation scheme using
comb-type zero-forcing channel estimation through SUI-5 Channel model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with BPSK through SUI-6 Channel
SNR(dB)
BE
R
Coded BPSK+ZF+itr=1Coded BPSK+ZF+itr=2Coded BPSK+ZF+itr=4Coded BPSK+ZF+itr=6Coded BPSK+ZF+itr=10Coded BPSK+ZF+itr=20
Fig. 7.80: Performance of the Turbo-coded OFDM with BPSK modulation scheme using
155
comb-type zero-forcing channel estimation through SUI-6 Channel model
The performance curves shown in the Fig.7.75-7.80 for the proposed model
confirms the presence of an interpolation error in the comb-type pilot insertion
technique when compared with the same system parameters checked with block-
type pilot insertion technique shown in Fig. 7.13-7.18 e.g. Fig. 7.13 shows a
performance of 10e-4 using the proposed model at the SNR of 26 dB. But the same
performance of 10e-4 is achieved by the comb-type pilot insertion technique
explained in chapter no. 5 using the same system parameters at 28.5 dB in Fig.
7.75, considering 20 iterations curve in each case. This coding gain of 2.5 dB by
the block-type pilot insertion technique goes to the credit of estimating the channel
at all the subcarrier positions. In comb-type pilot insertion technique the channel
impulse response is measured at only those positions of the multicarrier OFDM
symbol, where pilot tones are present (01 out of 08 in our case) while for rest of
the positions a suitable interpolation is applied to estimate the channel impulse
response. This interpolation generates an inherent error which results in the BER
performance degradation for the proposed system. Contrary to it, in the block-type
pilot-insertion technique the channel impulse response is measured at all the
subcarrier positions practically using the same amount of control information . Due
to this property there is a clear difference of 2.5 dB performance when graphs in
Fig. 7.75 and Fig. 7.13 are compared.
Next is to evaluate the performance of the proposed model with QPSK
modulation with the proposed model of OFDM using the frequency-domain pilot-
assisted comb-type zero-forcing channel estimation strategy. The performance
156
curves for the proposed model through the six channel models is given below.
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with QPSK through SUI-1 Channel
SNR(dB)
BE
R
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
Fig. 7.81: Performance of the Turbo-Coded OFDM With QPSK Modulation Scheme Using
Comb-Type Zero-Forcing Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-2 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
Fig. 7.82: Performance of the Turbo-Coded OFDM With QPSK Modulation Scheme Using
157
Comb-Type Zero-Forcing Channel Estimation Through SUI-2 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-3 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
Fig. 7.83: Performance of the Turbo-Coded OFDM With QPSK Modulation Scheme Using
Comb-Type Zero-Forcing Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-4 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
Fig. 7.84: Performance of the Turbo-Coded OFDM With QPSK Modulation Scheme Using
158
Comb-Type Zero-Forcing Channel Estimation Through SUI-4 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-5 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
Fig. 7.85: Performance of the Turbo-Coded OFDM With QPSK Modulation Scheme Using
Comb-Type Zero-Forcing Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with QPSK through SUI-6 Channel
Coded QPSK+ZF+itr=1Coded QPSK+ZF+itr=2Coded QPSK+ZF+itr=4Coded QPSK+ZF+itr=6Coded QPSK+ZF+itr=10Coded QPSK+ZF+itr=20
Fig. 7.86: Performance of the Turbo-Coded OFDM With QPSK Modulation Scheme Using
Comb-Type Zero-Forcing Channel Estimation Through SUI-6 Channel Model
159
When the Fig. 7.81 is compared with the succeeding Figs. 7.82-7.85, a
performance degradation is observed due to the loss of the LOS component of the
signal. The K-factor is not that much prominent and the channel shows a clear
signs of tilt towards rayleigh distributed coefficients. But when the same
performance curves are compared with the Fig. 7.19-7.24 for the QPSK modulated
OFDM with block-type zero-forcing estimation we can see that the block-type
pilot insertion technique shows an improved performance when compared with the
same algorithm implemented using the same parameters of the system but with
comb-type pilot insertion method. The fact, as mentioned before, is the inclusion of
interpolation error into the system whenever we use comb-type technique. Because
for estimating the channel impulse response at the positions which are missing
with the pilot tones, we have to use a suitable interpolation method which
generates an inherent interpolation error into the system which is depicted by a
coding loss of 2.5dB for 10e-3 BER performance for SUI-6 channel in Fig. 7.86
compared with Fig. 7.24. 10e-3 performance is attained at 29.5 dB for the block-
type pilot insertion technique using frequency-domain pilot-assisted zero forcing
channel estimation strategy but the same performance is attained at 32 dB using the
same system and channel parameters but using frequency domain pilot-assisted
comb-type pilot insertion method in Fig. 7.86. This loss of 2.5 dB goes to the credit
of the interpolation error in the comb-type pilot insertion method.
When the performance of the system is checked for the proposed algorithm
with 16, 32, 64-QAM, we observed that the algorithm is not able to coup well with
the higher modulation schemes. This effect is shown in the figures given below.
160
0 5 10 15 20 25 30 35 40
10-0.31
10-0.3
10-0.29
Turbo-Coded OFDM with 16-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 16-QAM+ZF+itr=1Coded 16-QAM+ZF+itr=2Coded 16-QAM+ZF+itr=4Coded 16-QAM+ZF+itr=6Coded 16-QAM+ZF+itr=10Coded 16-QAM+ZF+itr=20
Fig. 7.87: Performance of the Turbo-coded OFDM with 16-QAM Modulation Scheme
Using Comb-Type Zero-Forcing Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 40
10-0.31
10-0.3
10-0.29
Turbo-Coded OFDM with 32-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 32-QAM+ZF+itr=1Coded 32-QAM+ZF+itr=2Coded 32-QAM+ZF+itr=4Coded 32-QAM+ZF+itr=6Coded 32-QAM+ZF+itr=10Coded 32-QAM+ZF+itr=20
Fig. 7.88: Performance of the Turbo-coded OFDM with 32-QAM Modulation Scheme
Using Comb-Type Zero-Forcing Channel Estimation Through SUI-1 Channel Model
161
0 5 10 15 20 25 30 35 40
10-0.31
10-0.3
10-0.29
Turbo-Coded OFDM with 64-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 64-QAM+ZF+itr=1Coded 64-QAM+ZF+itr=2Coded 64-QAM+ZF+itr=4Coded 64-QAM+ZF+itr=6Coded 64-QAM+ZF+itr=10Coded 64-QAM+ZF+itr=20
Fig. 7.89: Performance of the Turbo-coded OFDM with 64-QAM Modulation Scheme
Using Comb-Type Zero-Forcing Channel Estimation Through SUI-1 Channel Model
The curves in Fig. 7.87-7.89 shows the performance of the proposed model
of OFDM with 16, 32 and 64-QAM modulation schemes using the proposed
algorithm for channel estimation using comb-type pilot insertion method through
SUI-1 channel. Results for rest of the channel were also bearing the same trend of
the curves. Graphs in these figures show that the comb-type pilot insertion method
is not suitable to be implemented with higher modulation schemes having less than
90° angular separation. This is because of the dual effect of the interpolation error
of the comb-type pilot insertion method and the higher angular separation between
the constellation points on the constellation map. Both these effect combine to
degrade the received signal quality below the acceptable levels.
7.7 Simulation Results for Proposed Model of Turbo-Coded/Uncoded
162
OFDM with Frequency-Domain Pilot-Assisted Comb-type Modified
Least Square Channel Estimation Algorithm:
When the presented scheme of modified Least Square Algorithm was employed in
the proposed model of uncoded/turbo-coded OFDM system using Comb-type pilot
insertion method, the results showed improved performance with different digital
modulation schemes. Compared to the block-type pilot insertion method, the
comb-type pilot insertion scheme is suitable for the fast fading environments. On
the other hand, the Block-type technique works well for the static or quasi-static
environments since once the channel impulse response is calculated for the current
fading distribution prevailed in the channel at that moment, the same channel
estimation matrix is used for a fix number of incoming data symbols in order to
equalize the effect of channel impulse response variations from them. Contrary to
it, the comb-type channel estimation works in an adaptive manner to calculate the
channel impulse response in real-time from the channel. The current channel
estimation matrix is then used to equalize the effects of the channel impulse
response variations from the received OFDM symbols in a more improved manner.
But this process is done at the expense of extra computations at the receiver end
which are used to do the interpolation at the positions which are missing with the
pilot tones.
First of all the performance of the proposed model of OFDM will be tested
with the aid of the proposed frequency-domain pilot-assisted comb-type modified
LS channel estimation without the application of error correcting Turbo codes.
BER curves showing the performance of the system through the six SUI channel
models are shown in the following lines.
163
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Uncoded OFDM with different modulation schemes through SUI-1 Channel
BPSK with LSQPSK with LS16-QAM with LS32-QAM with LS64-QAM with LS
Fig. 7.90: Performance of the Proposed Model with Uncoded OFDM and Presented Comb-
Type LSE Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Uncoded OFDM with different modulation schemes through SUI-2 Channel
BPSK with LSQPSK with LS16-QAM with LS32-QAM with LS64-QAM with LS
Fig. 7.91: Performance of the Proposed Model with Uncoded OFDM and Presented Comb-
Type LSE Channel Estimation Through SUI-2 Channel Model
164
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Uncoded OFDM with different modulation schemes through SUI-3 Channel
BPSK with LSQPSK with LS16-QAM with LS32-QAM with LS64-QAM with LS
Fig. 7.92: Performance of the Proposed Model with Uncoded OFDM and Presented Comb-
Type LSE Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Uncoded OFDM with different modulation schemes through SUI-4 Channel
BPSK with LSQPSK with LS16-QAM with LS32-QAM with LS64-QAM with LS
Fig. 7.93: Performance of the Proposed Model with Uncoded OFDM and Presented Comb-
Type LSE Channel Estimation Through SUI-4 Channel Model
165
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Uncoded OFDM with different modulation schemes through SUI-5 Channel
BPSK with LSQPSK with LS16-QAM with LS32-QAM with LS64-QAM with LS
Fig. 7.94: Performance of the Proposed Model with Uncoded OFDM and Presented Comb-
Type LSE Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
Uncoded OFDM with different modulation schemes through SUI-6 Channel
SNR(dB)
BE
R
BPSK with LSQPSK with LS16-QAM with LS32-QAM with LS64-QAM with LS
Fig. 7.95: Performance of the Proposed Model with Uncoded OFDM and Presented Comb-
Type LSE Channel Estimation Through SUI-6 Channel Model
The basic trend shown by the curves in Fig. 7.90-7.95 is same except the fact
that the succeeding graphs are showing a comparatively degraded performance due
to the tilt of the higher SUI channel models from Rician towards Rayleigh
behaviour.
166
Next, the performance of the proposed model is analysed using the BPSK
digital modulation scheme. The results are shown in the following lines.
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with BPSK through SUI-1 Channel
SNR(dB)
BE
R
Coded BPSK+LS+itr=1Coded BPSK+LS+itr=2Coded BPSK+LS+itr=4Coded BPSK+LS+itr=6Coded BPSK+LS+itr=10Coded BPSK+LS+itr=20
Fig. 7.96: Performance of the Turbo-coded OFDM with BPSK Modulation Scheme Using
Comb-Type LSE Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-5 Channel
Coded BPSK+LS+itr=1Coded BPSK+LS+itr=2Coded BPSK+LS+itr=4Coded BPSK+LS+itr=6Coded BPSK+LS+itr=10Coded BPSK+LS+itr=20
Fig. 7.100: Performance of the Turbo-coded OFDM with BPSK Modulation Scheme Using
167
Comb-Type LSE Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with BPSK through SUI-6 Channel
Coded BPSK+LS+itr=1Coded BPSK+LS+itr=2Coded BPSK+LS+itr=4Coded BPSK+LS+itr=6Coded BPSK+LS+itr=10Coded BPSK+LS+itr=20
Fig. 7.101: Performance of the Turbo-coded OFDM with BPSK Modulation Scheme Using
Comb-Type LSE Channel Estimation Through SUI-6 Channel Model
By looking closely at these figures, one can observe that owing to the loss of
the LOS component as shown by the weakening of the K-factor, there is a
considerable performance degradation in the system when we move towards higher
SUI channel. Similarly when compared to the block-type pilot insertion technique
shown in the Fig. 7.44-7.49, this technique shows a performance improvement
over the comb type method due to the interpolation error which is produced in the
estimation of the channel impulse response effect at the subcarrier position which
are missing with the pilot tones e.g. 10e-3 BER performance is achieved by the
block-type pilot insertion method channel estimation technique at an SNR of
22.8dB in the Fig. 7.49 while the same performance is achieved by the comb-type
pilot insertion based channel estimation implemented in the turbo-coded OFDM
168
environment at an SNR of 26 dB for SUI-6 channel in the Fig. 7.101, considering
20 iterations curve in both the cases. The 3.2 dB performance degradation in the
comb-type pilot insertion method refers to the interpolation error into the system as
discussed in the preceding lines. For rest of the five channel model we can see the
same performance edge of block type coding over the comb type coding.
Next is to investigate the performance of the Least Square channel
estimation algorithm in the proposed environment of turbo-coded OFDM using
QPSK modulation scheme. Results for different SUI channel are shown below.
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
Turbo-Coded OFDM QPSK through SUI-1 Channel
SNR(dB)
BE
R
Coded QPSK+LS+itr=1Coded QPSK+LS+itr=2Coded QPSK+LS+itr=4Coded QPSK+LS+itr=6Coded QPSK+LS+itr=10Coded QPSK+LS+itr=20
Fig. 7.102: Performance of the Turbo-coded OFDM with QPSK Modulation Scheme Using
Comb-Type LSE Channel Estimation Through SUI-1 Channel Model
169
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM QPSK through SUI-2 Channel
Coded QPSK+LS+itr=1Coded QPSK+LS+itr=2Coded QPSK+LS+itr=4Coded QPSK+LS+itr=6Coded QPSK+LS+itr=10Coded QPSK+LS+itr=20
Fig. 7.103: Performance of the Turbo-coded OFDM with QPSK Modulation Scheme Using
Comb-Type LSE Channel Estimation Through SUI-2 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM QPSK through SUI-3 Channel
Coded QPSK+LS+itr=1Coded QPSK+LS+itr=2Coded QPSK+LS+itr=4Coded QPSK+LS+itr=6Coded QPSK+LS+itr=10Coded QPSK+LS+itr=20
Fig. 7.104: Performance of the Turbo-coded OFDM with QPSK Modulation Scheme Using
170
Comb-Type LSE Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM QPSK through SUI-4 Channel
Coded QPSK+LS+itr=1Coded QPSK+LS+itr=2Coded QPSK+LS+itr=4Coded QPSK+LS+itr=6Coded QPSK+LS+itr=10Coded QPSK+LS+itr=20
Fig. 7.105: Performance of the Turbo-coded OFDM with QPSK Modulation Scheme Using
Comb-Type LSE Channel Estimation Through SUI-4 Channel Model
171
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM QPSK through SUI-5 Channel
Coded QPSK+LS+itr=1Coded QPSK+LS+itr=2Coded QPSK+LS+itr=4Coded QPSK+LS+itr=6Coded QPSK+LS+itr=10Coded QPSK+LS+itr=20
Fig. 7.106: Performance of the Turbo-coded OFDM with QPSK Modulation Scheme Using
Comb-Type LSE Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM QPSK through SUI-6 Channel
Coded QPSK+LS+itr=1Coded QPSK+LS+itr=2Coded QPSK+LS+itr=4Coded QPSK+LS+itr=6Coded QPSK+LS+itr=10Coded QPSK+LS+itr=20
Fig. 7.107: Performance of the Turbo-coded OFDM with QPSK Modulation Scheme Using
Comb-Type LSE Channel Estimation Through SUI-6 Channel Model
The performance of the proposed model with 16-QAM modulation scheme
is given in the following lines.
172
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
Turbo-Coded OFDM with 16-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.108: Performance of the Turbo-coded OFDM with 16-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-2 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.109: Performance of the Turbo-coded OFDM with 16-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-2 Channel Model
173
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-3 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.110: Performance of the Turbo-coded OFDM with 16-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-4 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.111: Performance of the Turbo-coded OFDM with 16-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-4 Channel Model
174
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-5 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.112: Performance of the Turbo-coded OFDM with 16-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-4
10-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 16-QAM through SUI-6 Channel
Coded 16-QAM+LS+itr=1Coded 16-QAM+LS+itr=2Coded 16-QAM+LS+itr=4Coded 16-QAM+LS+itr=6Coded 16-QAM+LS+itr=10Coded 16-QAM+LS+itr=20
Fig. 7.113: Performance of the Turbo-coded OFDM with 16-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-6 Channel Model
175
Similarly the same system parameters when used with the 32-QAM
modulation scheme, generates the results shown below.
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
Turbo-Coded OFDM with 32-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.114: Performance of the Turbo-coded OFDM with 32-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-1 Channel Model
176
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-2 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.115: Performance of the Turbo-coded OFDM with 32-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-2 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-3 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.116: Performance of the Turbo-coded OFDM with 32-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-3 Channel Model
177
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-4 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.117: Performance of the Turbo-coded OFDM with 32-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-4 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-5 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.118: Performance of the Turbo-coded OFDM with 32-QAM Modulation Scheme
178
Using Comb-Type LSE Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 32-QAM through SUI-6 Channel
Coded 32-QAM+LS+itr=1Coded 32-QAM+LS+itr=2Coded 32-QAM+LS+itr=4Coded 32-QAM+LS+itr=6Coded 32-QAM+LS+itr=10Coded 32-QAM+LS+itr=20
Fig. 7.119: Performance of the Turbo-coded OFDM with 32-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-6 Channel Model
Next is to look at the performance of the proposed model with 64-QAM
modulation scheme using the comb-type pilot-assisted modified Least Square
Channel Estimation Algorithm. Performance of the proposed system through the
six SUI channel models is given below.
179
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
Turbo-Coded OFDM with 64-QAM through SUI-1 Channel
SNR(dB)
BE
R
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.120: Performance of the Turbo-coded OFDM with 64-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-1 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-2 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.121: Performance of the Turbo-coded OFDM with 64-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-2 Channel Model
180
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-3 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.122: Performance of the Turbo-coded OFDM with 64-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-3 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-4 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.123: Performance of the Turbo-coded OFDM with 64-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-4 Channel Model
181
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-5 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.124: Performance of the Turbo-coded OFDM with 64-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-5 Channel Model
0 5 10 15 20 25 30 35 4010
-3
10-2
10-1
100
SNR(dB)
BE
R
Turbo-Coded OFDM with 64-QAM through SUI-6 Channel
Coded 64-QAM+LS+itr=1Coded 64-QAM+LS+itr=2Coded 64-QAM+LS+itr=4Coded 64-QAM+LS+itr=6Coded 64-QAM+LS+itr=10Coded 64-QAM+LS+itr=20
Fig. 7.125: Performance of the Turbo-coded OFDM with 64-QAM Modulation Scheme
Using Comb-Type LSE Channel Estimation Through SUI-6 Channel Model
182
From the results shown above, it can be observed very clearly that the
performance of the proposed model deteriorates as we move towards higher SUI
channel models. e.g. a performance of 8.2e-3 is achieved by the proposed model
for 20 iteration curve for SUI-1 channel model at SNR of 38 dB in Fig. 7.120.
While using the same parameters and SNR of 38 dB, the proposed model shows a
performance of 1.04e-2 for SUI-3 channel model and a performance of 1.14e-2 for
SUI-5 channel. Thus a constant degradative trend in the performance of the
proposed model is can be seen while moving towards higher channel models, due
to the continue trend of the channel towards Rayleigh distribution. Similarly if we
compare the curves in the Fig. 7.120 with that in the Fig.7.68 we observe that the
performance of 10e-2 is achieved at 36 dB for the block type pilot insertion
method used in the curves of Fig. 7.68 considering 20 iterations curve while the
same performance is achieved at 37.2 dB in Fig. 7.120 for 20 iterations curve and
using comb-type pilot insertion method keeping all the parameters same in both the
cases. The degradation of 1.2 dB goes to the credit of the interpolation error in the
comb type pilot insertion method compared to the block-type pilot insertion
method as already discussed in the relevant section.
7.8 Summary
In this chapter, the simulation results for the proposed model of turbo-coded
OFDM has been given using MATLAB® simulator. The chapter starts a brief
introduction to the simulator and then the proposed model of turbo-coded OFDM
with the anticipated algorithms for channel estimation and equalization, namely
183
zero forcing algorithm and modified Least Square channel estimation algorithms
has been given. Next the simulation results has been given by dividing the total
results into four sets. Each of the set shows results for one proposed channel
estimation algorithm with one of the two pilot insertion methods. Results have
been shown for five different digital modulation schemes namely BPSK, QPSK,
16-QAM, 32-QAM and 64-QAM. Results have been shown for both the uncoded
and turbo-coded OFDM model using the multipath Rayleigh fading channel and
six SUI channel models.
190
Chapter-8
CONCLUSIONS AND FUTURE PROSPECTS
8.1 Overview
In this dissertation, we have presented a model of OFDM in which the joint
capability of dealing with the channel induced fading effects and noise effects in
the most improved manner have been introduced. In order to coup with the channel
impulse response fading effects, two modified channel estimation strategies have
been integrated into the proposed model which have the capability to nullify the
effects of the channel induced fading from the received OFDM symbols. Since the
way of inserting pilot tones into the OFDM system can cast a great effect on the
overall performance of the system. Therefore two different methods have been
used to insert pilots into the OFDM symbol. Performance of the model have been
tested with both these pilot insertion techniques namely block-type and comb-type
pilot insertion methods and is compared.
In order to mitigate the effects of the noise introduced into the proposed
model, Forward Error Correcting Turbo Codes have been integrated into the
proposed model. These codes have the capability of mitigating the effects of the
noises incorporated into the proposed model in the efficient manner. Encoder has
been designed with parallel concatenation of two convolutional encoders while the
decoder is designed with the Maximum a posteriori decoding algorithm. The
decoding algorithm is implemented in an iterative manner using two component
decoders which gain from eachothers information exchange and improve the final
191
estimate regarding the decoded bit as a 0 or 1.
The performance of the model has been tested with six different fading
channels whose parameters have been borrowed from the Stanford University
Interim (SUI) Channel Models architecture.
8.2 Achievements
The research objectives set forth during the initial phases have been accomplished.
The desired modifications and enhancement in the proposed model as promised in
the abstract portion has been made and desired outcome has been received. The
results of the presented algorithms has been tested with the already published work
in the Chapter 7 and the results of our proposed algorithm have shown much
improved performance equivalent to 2.1 dB compared to the already published
work. The two modified algorithms have been compared amongst eachother in
different practical environment based simulated channels and with two different
pilot insertion methods and a detailed analysis of proposed scheme has been
carried out that can serve as a very good platform for finding solutions to the
addressed problem.
8.3 Limitations
Verification through simulation is an important step during research and
development, which has been achieved. But there is no substitute to live testing.
Therefore implementing the proposed model on different hardware e.g. Software
Defined Radio (SDR) or at the RF front-end of the Universal Software Radio
192
Peripheral (USRP) can further confirm the results of the simulated model for
practical implementation. The time constraints has not allowed us to achieve this
goal.
8.4 Future Work
As discussed earlier, the proposed model has been tested in this dissertation in a
number of different scenarios and environments. Two different channel estimation
algorithms have been proposed alongwith testing their performance with two
different pilot insertion methods most commonly employed in the communication
systems. As a future work, the same system model can be tested for a number of
other pilot insertion methods as well e.g. diagonal, two dimensional etc. Similarly,
though the simulation study of the proposed model is an essential step towards
research and development but as a next important step the same proposed model
can be tested in the practical environment using the USRP or SDR to evaluate the
performance of the proposed model in the practical environment which is also a
necessary and essential step towards implementation of the proposed system.
193
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202
APPENDIX-A
Frequency-Domain Pilot-Assisted Modified Least Square Channel Estimation
Equation Proof:
PHPT
HTP
HTP
ep YFPPF
PF
h
Identities Used:
The identities used in this proof are given below
TTT ABAB )(
TTTT ABCABC )(
AB
BAT
AB
ABT
symmetricisAIfABB
ABBT
2
Proof:
The received signal at pilot subcarriers is given by
WhFPY epH
PTp (A)
203
Since essence of LS estimator is to minimize the square of the difference between
the estimated (PTFgHhep) and detected signal(Yp). Therefore
)()()( epH
pTpH
epH
pTpep hFPYhFPYhE (B)
Using identity (2) we get,
)()()( epH
pTpH
TpHep
Hpep hFPYPFhYhE (C)
Using Identity (1) we get,
epH
PtH
TPHepP
HTp
Hepep
HPt
Hpp
Hpep hFPPFhYPFhhFPYYYhE )( (D)
Since epH
PtH
p hFPY and PH
TpHep YPFh are both scalors therefore combining these two
scalors in Eq. (D) we get,
epH
PtH
TPHepP
HTp
Hepp
Hpep hFPPFhYPFhYYhE 2)( (E)
Taking gradient of Eq. (E) we get,
ep
epHepH
PTH
TPPH
TPep
ep
h
hhFPPFYPF
h
hE
)(20
)( (F)
Using identity (5) we get,
epH
PTH
TPPH
TPep
ep hFPPFYPFh
hE22
)(
In order to find minimum value of the difference between the estimation and
detection , setting the above value of gradient equal to zero,
epH
PTH
TPPH
TP hFPPFYPF 220
204
PHPT
HTP
HTP
ep YFPPF
PFh
This is the value of the channel estimates at the pilot subcarrier positions of the
OFDM symbol.
PUBLICATIONS
Journal Papers: 1. S. Zeeshan, B. M. Inayatullah, S. S. Waqar, “Performance Enhancement of
Wireless Mobile Adhoc Networks through Improved Error Correction and ICI Cancellation,” EURASIP Journal on Advances in Signal Processing, 2012(2012):216, Oct. 2012.(Impact Factor: 0.81)
2. S. Zeeshan, B. M. Inayatullah, U. K. Latif, “Comparison of Two Modified Pilot-aided Channel Estimation Approaches for Turbo-Coded OFDM using SUI Channel Models – Physical Layer implementation of MANETS,” submitted in International Jour. Of Comm. Sys., Aug., 2012.(Impact Factor:0.406)
3. S. Zeeshan, B. M. Inayatullah, K. U. Amjad, U. R. Saeed, “Evaluating Performance of three different Routing Protocols in MANETs Environment based on TCP Window Size Evaluation and PDR using NS-2.35 Simulation,” submitted in Jour. Of Engg. & Applied Sciences, Sep 2012.(Category-X)
4. M. M. Tahir, I. Javed, S. Zeeshan, B. M. Inayatullah, “Multi Hop Cluster Design in Wireless Sensor Networks,” submitted in Jour. Of Engg. And Applied Sciences, June 2012(Category-X).
Conference Papers:
5. S. Zeeshan, Y. S. Abdur Rehman, B. M. Inayatullah, M. Arif Wahla, "Improved Joint ICI Cancellation and Error Correction for OFDM System," in proc. 2011 Intl. Conf. on Elect. Commerce, Web App. And Comm., ECWAC, Springer Berlin Heidelberg, vol. 143, No. 1, pp:1-11, China, April 2011.
6. U. K. Latif, S. Zeeshan, B. M. Inayatullah, S. S. Waqar, “Robust Modified MMSE Estimator for Comb-Type Channel Estimation in OFDM Systems” in proc. IEEE Int. Conference on Comm. Tech. (ICACT-2013), Korea.(Best Paper Award)
7. U. R. Saeed, Farukkh A. Bhatti, R. Faiz, M. I. Yasir, S. Zeeshan “Modeling the Impact of Deferred Transmission in CSMA/CA Algorithm for IEEE 802.15.4 using Markov Chain Model,” in the proc. of the 14th IEEE International Multitopic
205
Conference (INMIC 2011), pp:334-339, Pakistan, Dec 2011.
8. Md. H. Nazmul, S. S. Waqar, B. M. Inayatullah, S. Zeeshan “Design and Simulation Based Studies of a Dual Band U-slot Patch Antenna for WLAN Application,” in the proc. of The IEEE 14th International Conference on Advanced Communication Technology (ICACT), Phoenix Park, PyeongChang, South Korea.
9. U. K. Latif, S. Zeeshan, S. S. Waqar, B. M. Inayatullah, “Fortifying OFDM Performance using Channel Estimation and Forward Error Correction Technique,” in proc. IEEE 19th Asia-Pacific Conf. on Comm. (APCC 2013), Indonesia. (Paper accepted but Registration fees not paid due to funding issues).
