SIM UNIVERSITY
SCHOOL OF SCIENCE AND TECHNOLOGY
ONLINE EXPERIMENTS IN
DIGITAL COMMUNICATIONS
STUDENT
: WONG CHAN WEN
: (T6121401)
SUPERVISOR
: MR CHUA PENG HUAT
PROJECT CODE : JAN2009/BSTE/02
A project report submitted to SIM University
in partial fulfilment of the requirements for the degree of
Bachelor of Science in Technology with Electronics
Nov 2009
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ABSTRACT
The purpose of this project is to design and produce interactive online experiments in
digital communications. The experiments will serve as study aids for students of digital
communications which include Eye diagrams, constellation plots, BER-SNR plots and
digital modulation schemes with data inputted by them. There is also a section for self-test
questions where students can test their knowledge of digital communications.
The digital communications experiments presented were designed using Mathworks
MATLAB®. The scripts, known as m-scripts, were tested and built in MATLAB® Java
Builder to produce Java component jar file. The Java component file was compiled together
with a Java servlet written to access the methods inside the Java component file to produce
Java classes. In order for the experiment to function in a webpage, a HTML page, a JSP page
and a web.xml, which is a Web descriptor file, were written specifically for the Java classes’
interfaces.
The experiments simulation results were compared with theories and were verified
correctly designed and implemented successfully.
It is concluded that developing such a project is advantages where digital communication
theories and techniques can be shared over the Internet, promoting portability and also
reducing printed materials thereby promoting green environment. In addition, students or
users do not have to go through manual calculations for certain equations or formulas in
digital communications.
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ACKNOWLEDGEMENT
First, I would like to express my utmost gratitude towards my supervisor, Mr Chua Peng
Huat for giving me the opportunity and helping me to attain progression and smoothness
during the course of this project. This project may not have been carried out and completed
successfully without him for all his guidance, supervision and encouragement.
Second, my next sincere appreciation goes to the instructors especially Dr Alan Lim
Teik Cheng for his splendid support and assistance.
Third, I would like to thank my family for being tolerant and most importantly, the kind
words of encouragement.
Lastly, to everyone that has directly or indirectly contributed to my project completion,
my extended sincere thanks to you. Where this report succeeds, I share the credit with you.
Thank you!
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TABLE OF CONTENTS
ABSTRACT
ACKNOWLEDGEMENT
LIST OF FIGURES
LIST OF TABLES
CHAPTER ONE
Introduction
1.1 Background and Motivation
1.2 Objectives
1.3 Scope
1.4 Layout of the Project report
CHAPTER TWO
Technical Background
2.1 Digital Communications
2.2 Digital Modulations
2.3 Digital Transmissions
2.4 Line Codes
2.5 Constellation Diagrams
2.6 Equalizers
2.7 MATLAB®
2.8 Apache Tomcat
2.9 JavaScript
CHAPTER THREE
Experiments
3.1 Digital Modulations
3.1.1 Amplitude Shift Keying
3.1.2 Frequency Shift Keying
3.1.3 Phase Shift Keying
3.1.4 Pulse Amplitude Modulation
3.1.5 Quadrature Amplitude Modulation
3.2 Digital Transmissions
3.3 Line Codes
3.4 Constellation Diagrams
3.5 Equalizers
3.5.1 RLS and LMS Equalizers
3.5.2 MLS Equalizer
3.6 MATLAB®
3.7 Apache Tomcat
3.8 Quiz
CHAPTER FOUR
Results
4.1 Digital Modulations
4.1.1 Amplitude Shift Keying
4.1.2 Frequency Shift Keying
4.1.3 Phase Shift Keying
4.1.4 Pulse Amplitude Modulation
4.1.5 Quadrature Amplitude Modulation
4.2 Digital Transmissions
4.3 Line Codes
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4.4
4.5
Constellation Diagrams
Equalizers
4.5.1 RLS and LMS Equalizers
4.5.2 MLS Equalizer
4.6 Quiz
CHAPTER FIVE
Discussion
5.1 Digital Modulations
5.1.1 Amplitude Shift Keying
5.1.2 Frequency Shift Keying
5.1.3 Phase Shift Keying
5.1.4 Pulse Amplitude Modulation
5.1.5 Quadrature Amplitude Modulation
5.2 Digital Transmissions
5.3 Line Codes
5.4 Constellation Diagrams
5.5 Equalizers
5.5.1 RLS and LMS Equalizers
5.5.2 MLS Equalizer
5.6 Quiz
CHAPTER SIX
Reflections
CHAPTER SEVEN
Conclusions and Recommendations
LIST OF REFERENCES
BIBLIOGRAPHY
GLOSSARY
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LIST OF FIGURES
Figure 2.1
Figure 2.2
Figure 2.3
Figure 2.4
Figure 2.5
Figure 2.6
Figure 2.7
Figure 2.8
Figure 2.9
Figure 2.10
Figure 2.11
Figure 2.12
Figure 2.13
Figure 2.14
Figure 2.15
Figure 2.16
Figure 2.17
Figure 2.18
Figure 2.19
Figure 2.20
Figure 2.21
Figure 2.22
Figure 2.23
Figure 2.24
Figure 2.25
Figure 2.26
Figure 2.27
Figure 2.28
Figure 2.29
Block Diagram of a Digital Communications System
An Additive White Gaussian Noise (AWGN) Channel
ASK Modulator
On-Off Keying Modulation
FSK Modulator
Frequency Shift Keying Modulation
PSK Modulator
Phase Shift Keying Modulation
QPSK Modulator
QPSK Constellation Diagram
8-PSK Constellation Diagram
16-PSK Constellation Diagram
PAM Modulator
2-PAM with Raised Cosine Filter
QAM modulator
4-QAM Constellation Diagram
16-QAM Constellation Diagram
64-QAM Constellation Diagram
Transmitted Waveform
Bit Sequence 101101 Sent
Symbols Received
Transmitted Signal Versus Received Signal
Eye Diagram Interpretation
Unipolar Non-Return-To-Zero Coding
Polar Non-Return-To-Zero Coding
Unipolar Return-To-Zero Coding
Bipolar Return-To-Zero Coding
Manchester Coding
QPSK Constellation Diagram with   0 o
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Figure 2.30
QPSK Constellation Diagram with   45 o
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Figure 2.31
8-PSK Constellation Diagram with   0
Figure 2.32
Figure 2.33
Figure 2.34
Figure 2.35
Figure 2.36
Figure 2.37
Figure 2.38
Figure 3.1
Figure 3.2
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4.6
64-QAM Constellation Diagram with   0
BER vs E/No for p  1 , p  0 and p  1
QPSK Noisy Constellation
64-QAM Noisy Constellation
RLS Filter in Negative Feedback
LMS Filter Block Diagram
MLSE End to End Wireless System Block Diagram
Tomcat required files in bin folder
Digital Modulation Quiz Screen Capture
ASK Experiment Plots
ASK-SNR Experiment No of bits = 8, SNR = 10dB
ASK-SNR Experiment No of bits = 8, SNR = 15dB
ASK-SNR Experiment No of bits = 8, SNR = 35dB
OOK-SNR Experiment No of bits = 8, SNR = 5dB
OOK-SNR Experiment No of bits = 8, SNR = 10dB
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Figure 4.7
Figure 4.8
Figure 4.9
Figure 4.10
Figure 4.11
Figure 4.12
Figure 4.13
Figure 4.14
Figure 4.15
Figure 4.16
Figure 4.17
Figure 4.18
Figure 4.19
Figure 4.20
Figure 4.21
Figure 4.22
Figure 4.23
Figure 4.24
Figure 4.25
Figure 4.26
Figure 4.27
Figure 4.28
Figure 4.29
OOK-SNR Experiment No of bits = 8, SNR = 35dB
FSK Experiment Plots
FSK-SNR Experiment No of bits = 8, SNR = 5dB
FSK-SNR Experiment No of bits = 8, SNR = 15dB
FSK-SNR Experiment No of bits = 8, SNR = 35dB
FSK-BER Experiment using 20000 symbols, 8 symbol duration
FSK-BER Experiment using 20000 symbols, 32 symbol duration
PSK Experiment Plots
PSK-SNR Experiment No of bits = 8, SNR = 5dB
PSK-SNR Experiment No of bits = 8, SNR = 15dB
PSK-SNR Experiment No of bits = 8, SNR = 35dB
QPSK-SNR Experiment No of bits = 8, SNR = 5dB
QPSK-SNR Experiment No of bits = 8, SNR = 15dB
QPSK-SNR Experiment No of bits = 8, SNR = 35dB
8-PSK-SNR Experiment No of bits = 9, SNR = 5dB
8-PSK-SNR Experiment No of bits = 9, SNR = 25dB
8-PSK-SNR Experiment No of bits = 9, SNR = 35dB
PSK-BER Experiment No of bits = 300000, symbol duration = 8
16-PSK-SER Experiment No of bits = 300000
4-PAM-SER Experiment No of bits = 300000
4-QAM-SER Experiment No of bits = 300000
16-QAM-SER Experiment No of bits = 300000
64-QAM-SER Experiment No of bits = 300000
Figure 4.30
4-QAM Filtered Signal and
Figure 4.31
Figure 4.32
Figure 4.33
Figure 4.34
Figure 4.35
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 5dB Noisy Signal
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4-QAM Filtered Signal and s  20dB Noisy Signal
N0
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4-QAM Filtered Signal and s  40dB Noisy Signal
N0
E
4-PSK Filtered Signal and s  5dB Noisy Signal
N0
E
4-PSK Filtered Signal and s  25dB Noisy Signal
N0
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4-PSK Filtered Signal and s  40dB Noisy Signal
N0
Figure 4.36
Figure 4.37
4-QAM Transmitted Signal Eye Diagram
4-PSK Transmitted Signal Eye Diagram
Figure 4.38
4-QAM Received Signal Eye Diagram with
Figure 4.39
Figure 4.40
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 5dB
N0
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4-QAM Received Signal Eye Diagram with s  20dB
N0
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4-QAM Received Signal Eye Diagram with s  40dB
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Figure 4.41
Figure 4.42
Figure 4.43
Figure 4.44
Figure 4.45
Figure 4.46
Figure 4.47
Figure 4.48
Figure 4.49
Figure 4.50
Figure 4.51
Figure 4.52
Figure 4.53
Figure 4.54
Figure 4.55
Figure 4.56
Figure 4.57
Figure 4.58
Figure 4.59
Figure 4.60
Figure 4.61
Figure 4.62
Figure 4.63
Figure 4.64
Figure 4.65
Figure 4.66
Figure 4.67
Figure 4.68
Figure 4.69
Figure 4.70
Figure 4.71
Figure 4.72
Figure 4.73
Figure 4.74
Figure 4.75
Figure 4.76
Figure 4.77
Figure 4.78
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Figure 4.80
Figure 4.81
Figure 4.82
Figure 4.83
Figure 4.84
Figure 4.85
Figure 4.86
Es
 5dB
N0
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4-PSK Received Signal Eye Diagram with s  25dB
N0
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4-PSK Received Signal Eye Diagram with s  40dB
N0
4-PSK Received Signal Eye Diagram with
Unipolar NRZ - 11001010
Polar NRZ - 11001010
Unipolar RZ - 11001010
Bipolar RZ - 11001010
Manchester Coding - 11001010
Unipolar NRZ Power Spectral Density, Rb = 1kbps
Unipolar NRZ Power Spectral Density, Rb = 10kbps
Polar NRZ Power Spectral Density, Rb = 1kbps
Polar NRZ Power Spectral Density, Rb = 10kbps
Unipolar RZ Power Spectral Density, Rb = 1kbps
Unipolar RZ Power Spectral Density, Rb = 10kbps
Bipolar RZ Power Spectral Density, Rb = 1kbps
Bipolar RZ Power Spectral Density, Rb = 10kbps
Manchester Coding Power Spectral Density, Rb = 1kbps
Manchester Coding Power Spectral Density, Rb = 10kbps
Unipolar NRZ, AWGN = 0W
Unipolar NRZ, AWGN = 0.02W
Polar NRZ, AWGN = 0W
Polar NRZ, AWGN = 0.02W
Unipolar RZ, AWGN = 0W
Unipolar RZ, AWGN = 0.02W
Bipolar RZ, AWGN = 0W
Bipolar RZ, AWGN = 0.02W
Manchester Coding, AWGN = 0W
Manchester Coding, AWGN = 0.02W
Unipolar NRZ, Bandwidth = 1kHz
Unipolar NRZ, Bandwidth = 4kHz
Polar NRZ, Bandwidth = 1kHz
Polar NRZ, Bandwidth = 4kHz
Unipolar RZ, Bandwidth = 1kHz
Unipolar RZ, Bandwidth = 4kHz
Bipolar RZ, Bandwidth = 1kHz
Bipolar RZ, Bandwidth = 4kHz99
Manchester Coding, Bandwidth = 1kHz
Manchester Coding, Bandwidth = 4kHz
Unipolar NRZ, BW = 4kHz, AWGN = 0W
Unipolar NRZ, BW = 4kHz, AWGN = 0.02W
Polar NRZ, BW = 4kHz, AWGN = 0W
Polar NRZ, BW = 4kHz, AWGN = 0.02W
Unipolar RZ, BW = 4kHz, AWGN = 0W
Unipolar RZ, BW = 4kHz, AWGN = 0.02W
Bipolar RZ, BW = 4kHz, AWGN = 0W
Bipolar RZ, BW = 4kHz, AWGN = 0.02W
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Figure 4.87
Figure 4.88
Figure 4.89
Figure 4.90
Figure 4.91
Figure 4.92
Figure 4.93
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Figure 4.95
Figure 4.96
Figure 4.97
Figure 4.98
Figure 4.99
Figure 4.100
Figure 4.101
Figure 4.102
Figure 4.103
Figure 6.1
Manchester Coding, BW = 4kHz, AWGN = 0W
Manchester Coding, BW = 4kHz, AWGN = 0.02W
QPSK at SNR = 5dB Constellation Diagram
QPSK at SNR = 35dB Constellation Diagram
8-PSK at SNR = 5dB Constellation Diagram
8-PSK at SNR = 35dB Constellation Diagram
64-QAM at SNR = 5dB Constellation Diagram
64-QAM at SNR = 35dB Constellation Diagram
4-QAM RLS Equalized, Non-Equalized 1 Iteration Constellation
4-QAM LMS Equalized, Non-Equalized 1 Iterations Constellation
4-QAM RLS Equalized, Non-Equalized 8 Iterations Constellation
4-QAM LMS Equalized, Non-Equalized 8 Iterations Constellation
8-PSK MLSE Equalized, Non-Equalized Constellation
64-PSK MLSE Equalized, Non-Equalized Constellation
8-QAM MLSE Equalized, Non-Equalized Constellation
64-QAM MLSE Equalized, Non-Equalized Constellation
Digital Modulation Quiz Result
Revised TMA01 Gantt Chart
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LIST OF TABLES
Table 3.1
Table 3.2
Table 3.3
Table 3.4
Table 3.5
Table 3.6
Table 3.7
Table 3.8
Table 3.9
Table 3.10
Table 3.11
Table 3.12
Table 3.13
Table 3.14
Table 3.15
Table 5.1
Table 5.2
ASK-SNR and OOK-SNR Experiments Parameters
FSK-SNR Experiments Parameters
FSK-BER Experiments Parameters
PSK-SNR, QPSK-SNR and EPSK-SNR Experiments Parameters
PSK-BER,16-PSK-SER Experiments Parameters
4-QAM, 16-QAM, 64-QAM Experiments Parameters
Transmission Path Experiments Parameters
Line Code Experiments Parameters Part 1
Line Code Experiments Parameters Part 2
Line Code Experiments Parameters Part 3
Line Code Experiments Parameters Part 4
Line Code Experiments Parameters Part 5
Constellation Diagram Experiments Parameters
RLS, LMS Experiments Parameters
MLSE Experiments Parameters
FSK and PSK-BER Plots’ Comparisons
PSK, PAM, QAM-SER Plots’ Comparisons
Table 5.3
4-QAM, 4-PSK
Table 5.4
Table 5.5
Table 5.6
Table 5.7
Table 5.8
Table 5.9
4-QAM, 4-PSK Transmitted Signal Eye Diagram Comparisons
4-QAM, 4-PSK Received Signal Eye Diagram Comparisons
Line Codes’ Comparisons
Line Codes’ Eye Diagram Values
Line Codes’ Advantages and Disadvantages
RLS and LMS Comparisons
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(dB) Comparisons
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CHAPTER ONE
Introduction
1.1
Background and Motivation
Communications, becoming an important role in our daily lives, has evolved from
traditional television broadcast, to digital television; from public switched telephone network,
to voice-over-IP. It is one of the fastest growing technologies today which have advanced
from wired to wireless communications. At this rate of advancement, it is important that the
communications students are well-versed in this area of study before being presented with
more sophisticated and complex theories.
Books and texts are readily available for digital communications techniques and theories,
even for advanced digital communications. Some texts provide online printed version which
can be downloaded over the Internet. However, not many had online experiments where
visitors can perform digital communications simulations without the need for a physical
hardware laboratory presence.
There are three web sites that were found offering online digital communications
experiments. They are at: 
http://web.singnet.com.sg/~tanweb/.
The author has topics covered in baseband transmission on line codes and channel
coding on Convolutional codes and Hamming codes. Examples and experiments are
available for unipolar and polar non-return-to-zero and return-to-zero coding,
Manchester coding and alternate mark inversion coding. The experiments are written
using Java applet. They are straight forward but only offer 8 input bits entry. Other
examples and experiments include Convolutional coding and Hamming.

http://www.eng.newcastle.edu.au/~c3039337/index_August2.html.
The author displays working experiments on binary phase shift keying (BPSK) with
waveforms of transmitted modulated signal, received modulated signal, additive
white Gaussian noise (AWGN), product detector, integrator, sample and hold and
recovered signal. Some editable parameters are pulse shape, phase shift and input
bits. Fixed parameters are the carrier frequency and Energy per bit (Eb). The
waveforms can be freeze for analysis.
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
http://www.eng.newcastle.edu.au/~c3039032/.
The web site touches on digital modulation technique, non-coherent detection
method for differential phase shift keying and hamming code. The author
demonstrated clear experiments on amplitude shift keying, frequency shift keying
and phase shift keying all on a single page with user entry input bits and user defined
k for M-ary modulation where M = 2k. On the non-coherent differential phase shift
keying experiment, it is easy to understand and manipulate. The last two
experiments are on the hamming code (7, 4) and single error correction detection.
Both experiments are workable and nicely done up. This is a professionally done up
web site on web experiment on digital communications.
There is another similar project done by past year student dated year 2008 but stop short
on deploying the experiments online. The experiments are designed and developed using
MATLAB® GUI, demonstrating modulation and demodulation schemes, equalization and
channel coding. The author had problems deploying the experiments online, which in effect
is considered ‘offline’.
1.2
Objectives
In this project, an online web experiments in Digital Communications are to be designed
and developed. It will have experiments that are not covered by the three web sites found on
the Internet. In addition, the experiments will be deployed onto the web server for Internet
access which was not completed by past year student on the same project. The experiments
will serve as study aids for students of digital communications including digital modulation
schemes, Eye diagrams, digital transmission, constellation plots, line codes and equalizers.
Users are able to find plots of graphs with data inputted by them as well as self-test questions
to check on their knowledge of digital communications.
The overall objectives are to provide: 
Current and past students the ability to study their digital communications topics
online and revisit the topics; and to check on their knowledge;

Interested parties who want to learn more on digital communications techniques and
theories;

Instructors and lecturers who want to demonstrate the experiments to students.
The advantages on developing such a project is that firstly, digital communication
theories and techniques can be shared over the Internet, promoting portability and also
reducing printed materials thereby promoting green environment. Secondly, students or users
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do not have to go through manual calculations for certain equations or formulas in digital
communications. Thirdly, experiment simulations can be done without the need for physical
hardware involvement. Lastly, instructors and lecturers can make use of the online
experiments to assign laboratory tests and homework to students.
1.3
Scope
During the review of literature, it was noted that current similar websites covered topics
such as digital modulation but limited to amplitude shift keying, frequency shift keying and
phase shift keying. Number of bits that can be inputted is limited to only 8. No experiments
are touched on constellation diagram, digital transmission and equalization. It was also noted
that the programming language used for experiment design are based on Java applet. As with
the past year project, it was done using MATLAB® GUI but unable to be deployed onto the
Internet. Hence, the scope of this project will be threefold as listed below: 1. Improve on the digital communications experiments for uncovered topics;
2. Make use of MATLAB® to design and develop the experiments for simulations and
plots generation;
3. Deploy the complied MATLAB® experiments onto a web server, notably a Java
web server.
The additional experiments which will enhance the current online ones include: 
More digital modulation experiments such as pulse amplitude modulation and
quadrature amplitude modulation;

BER-SNR and SER-SNR plots for digital modulation schemes;

Constellation diagrams;

Digital transmissions;

Equalization;

Quiz section for self-test questions.
Instead of using Java applet to design and develop the experiments, MATLAB® will be
used for simulations and plots generation. MATLAB® is a high-performance language for
technical computing. It integrates computation, visualization, and programming in an easyto-use environment where problems and solutions are expressed in familiar mathematical
notation [1].
As for the deployment of experiments over the Internet, a web server is required. The
choice of web server needs to fulfill the requirement to support JSP pages as the MATLAB®
Java compiled experiments only work in this format. Apache Tomcat web server was chosen
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ahead of the rest of Java web server due to easy deployment and more importantly, ability to
support JSP pages for the Java compiled MATLAB® experiments. It also supports
JavaScript which will be used to write the quiz programs.
1.4
Layout of the Project report
The layout of the project report continues with Chapter 2, which will have a technical
background review that is required to understand the remaining sections. The topics covered
include digital communications theories and techniques, essentially being the backbone to
the design and development of the MATLAB® experiments. It will also touch on
MATLAB® Java Builder for Java component jar file compilation and interfaces necessary
for the Java jar file and classes to function on JSP web server.
Chapter 3 provides detailed methods for all the MATLAB® experiments, describing
their functions, features and interfaces. Experiments will be shown how they are conducted.
Chapter 4 takes a critical look at each experiment and how they will perform against
their intended task with screen captured figures and generated plots.
Chapter 5 discusses the results obtained from experiments and identifies any error
against the manual calculations if any.
Chapter 6 summarises and concludes on the work done and identifies if any
improvements can be made to further enhance the features.
The final chapter, chapter 7, will discuss how the project was tackled and what was
learnt. An account will be provided on how skills have developed. Achievements are
compared with targets specified in TMA01 with any discrepancies if any.
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CHAPTER TWO
Technical Background
2.1
Digital Communications
In the simplest form, transmission-reception system is a three-block system, consisting
of a) a transmitter, b) a transmission medium and c) a receiver [2]. An illustrated block
diagram showing the important processes of modulation and demodulation; source coding
and decoding and channel encoding and decoding is shown in Figure 2.1 below: -
Transmitter
Input
Receiver
Source
Encoder
Source
Decoder
Source
Codeword
Output
Estimated Source
Codeword
Channel
Coder
Channel
Decoder
Channel
Codeword
Received
Codeword
Modulator
Demodulator
Channel
Noise
Figure 2.1 Block Diagram of a Digital Communications System
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Basically the source encoder has the function of converting the input from its original
form into a sequence of bits [3]. This approach of conversion is normally known as analog to
digital conversion (A/D). During this conversion, the source encoder often has to transmit as
few bits as possible, performing function such as data compression.
The sequence of bits entering the channel encoder is typically long and never ending.
Hence the channel encoder must be capable of keeping with the stream of incoming bits,
encoding and transmitting them so that they can be recreated at the decoder with small error
probability. In other words, it has to ensure that the data sent over is protected using error
correction code.
The modulator accepts bit sequence from the channel encoder and modulates the signal
using digital modulation schemes such as the amplitude shift keying or frequency shift
keying.
Channel is a communication medium known as physical channel going from source
location to destination. It can be a coaxial cable, an optical fibre or just a pair of wires. It is
inevitable that there is zero noise in the channel. Therefore, a common channel model
involves a waveform input X (t ) , an added noise waveform Z (t ) and a waveform output
Y (t ) , where Y (t )  X (t )  Z (t ) as shown in Figure 2.2.
Z(t)
Noise
X(t)
Input
Output
Y(t)
Figure 2.2 An Additive White Gaussian Noise (AWGN) Channel
2.2
Digital Modulations
Modulation is the process of facilitating the transfer of information over a medium [4].
In digital modulation, an analog carrier signal is modulated by a digital bit stream. Digital
modulation methods can be considered as digital to analog conversion. The three basic types
of digital modulation techniques are amplitude shift keying (ASK), frequency shift keying
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(FSK) and phase shift keying (PSK). Some modulation schemes use both analog and digital
modulation such as the pulse amplitude modulation (PAM) and the quadrature amplitude
modulation (QAM).
Amplitude shift keying (ASK) is a form of modulation that represents digital data as
variations in the amplitude of a carrier wave [5]. In its simplest form as binary amplitude
shift keying or BASK, binary logic ‘0’ and ‘1’ represents variations in the amplitude of a
carrier wave. Figure 2.3 shows the block diagram of a ASK modulator.
Figure 2.3 ASK Modulator
While keeping frequency and phase constant of an analog carrier signal, this signal varies
accordance with modulating signal’s bit stream. Similar to amplitude modulation, amplitude
shift keying is also linear. It is unavoidable to distortions, propagation conditions and
atmospheric noise on different kinds of routes during transmission. On-off keying (OOK) is
a special form of amplitude shift keying modulation where one of the amplitudes is zero as
shown in Figure 2.4. The amplitude shift keying modulation is represented in equation (2.1).
x(t )  s (t ) sin( 2ft )
(2.1)
Where x(t ) is the modulated signal; sin( 2ft ) is the carrier signal; s (t ) is the modulating
signal.
8
Figure 2.4 On-Off Keying Modulation
In frequency shift keying (FSK), the signal has various frequencies representing different
binary data. In its simplest form as binary frequency shift keying (BFSK), it uses two
symbols of binary logic ‘0’ and ‘1’ corresponding to two different frequencies. The FSK
modulator block diagram is shown in Figure 2.5.
9
Figure 2.5 FSK Modulator
The equation representing a frequency shift keying modulation is shown as follows in
equation (2.2): -
sin( 2f1t )
x(t )  
sin( 2f 2 t )
for bit 1
for bit 0
(2.2)
Where x(t ) is the modulated signal; sin( 2f1t ) is the carrier signal A; sin( 2f 2 t ) is the
carrier signal B.
The performance of a FSK signal is evaluated by means of the bit error probability p e
or Bit Error Rate, BER as expressed in equation (2.3) for coherent FSK and equation (2.4)
for non-coherent FSK.
E
No
Coherent FSK
pe  T
Non-coherent FSK
1  .
p e  e 2 No
2
(2.3)
1 E
(2.4)
In non-coherent FSK, the instantaneous frequency is shifted between two discrete values. In
coherent FSK, there is no phase discontinuity in the output signal.
An example of a frequency shift keying modulation is shown in Figure 2.6.
10
Figure 2.6 Frequency Shift Keying Modulation
In phase shift keying, the carrier frequency remains constant while its phase changes in
discrete quantities in accordance with the login state of the data bit [6]. It uses a finite
number of phases which encodes an equal number of bits with a unique pattern of binary bits.
Figure 2.7 shows the PSK modulator block.
11
Figure 2.7 PSK Modulator
Each pattern of bits will form symbols which will represent a particular phase. The simplest
form of phase shift keying is the binary phase shift keying (BPSK). It uses two symbols to
represent binary logic ‘0’ and ‘1’ which is segments of a sinusoid of the same frequency but
differ in their phase. Because the two symbols can be distinguished if their phases differ by
as much as possible, they are invariably separated by 180°. The equation representing a
phase shift keying modulation is shown as follows in equation (2.5): -
sin( 2ft)
x(t )  
sin( 2ft   )
for bit 1
for bit 0
(2.5)
Where x(t ) is the modulated signal; sin( 2ft ) is the carrier signal.
The bit error probability p e for BPSK is expressed in equation (2.6).
BPSK
pe  T 2.
E
No
Figure 2.8 shows an example of phase shift keying modulation.
(2.6)
12
Figure 2.8 Phase Shift Keying Modulation
QPSK, quadrature phase shift keying, uses four points on the constellation diagram also
known as scatter plot, equally spaced, around a circle. A constellation diagram is utilized to
show the relationship among the different amplitude and phase states of the modulated signal
by displaying the error vector at the symbol sample time. The error vector is the difference
between the theoretical symbol location and the actual symbol location on the constellation
diagram [7]. In PSK, modulation alphabet is often conveniently represented on a
constellation diagram, showing the amplitude of the In-phase channel, known as I-channel at
the x-axis and the amplitude of the Quadrature channel, known as Q-channel at the y-axis for
each symbol. QPSK signal is an extended version of BPSK where both are of type M-ary
signals. In mathematics polar form, the modulated QPSK signal can be represented by
equation (2.7): -
xi (t )  Ac p s (t ). cos( 2f c t 
Where p s (t ) is the pulse shaping functions,
modulation.
2i
)
M
(2.7)
2i
is the phase change, M is the order of
M
13
2
T
p s (t ) 
0t T
(2.8)
Substituting equation (2.8) into equation (2.7), we have
xi (t )  Ac
2
2i
cos( 2f c t 
)
T
M
(2.9)
Where i  0,1,...M
Expanding equation (2.9), we have
xi (t )  Ac
2
2i 
2i 
[cos( 2f c t ) cos(
 )  sin( 2f c t ) sin(
 )]
T
M 4
M 4
(2.10)
The relation of the In-phase and Quadrature projections of the signal are: Magnitude of signal
x  I 2  Q2
Phase of the signal
x  tan 1
I
Q
I  Ac
2
cos( 2f c t )
T
Q  Ac
2
sin( 2f c t )
T
With
(2.11)
(2.12)
(2.13)
Multiplying equation (2.10) with (2.13), we have
x(t )  Ac
2
2
cos( (t )) cos( 2f c t )  Ac
sin(  (t )) sin( 2f c t )
T
T
Where  
2
M
(2.14)
Equation (2.14) is called the quadrature form of the modulation equation where
Ac
2
2
cos( (t )) is the amplitude of I channel, while Ac
sin(  (t )) is the
T
T
amplitude of Q channel. Hence a phase modulated signal is seen as a combination of
two quadrature signals, the amplitude changes in response to the phase change. The
modulating signal is seen as a vector with the I-channel and Q-channel as its x and y
components. Do note that the signal created by I and Q channels is not transmitted.
In fact, it is the sum of these two channels that is the real modulated signal.
Going back to the M denotation, hence for QPSK, M = 4. The bit error probability p e for
QPSK is expressed in equation (2.15): -
14
QPSK
pe  T 2.
E
No
(2.15)
Where M = 8, it is said of an 8-PSK signal with eight points on the constellation diagram,
equally spaced. In 8-PSK, there are four different phase values, namely
 3 5
8
,
8
,
8
and
7
.
8
Each of these phase shifts is 45° apart, which when applied to the sine and cosine waveforms
result in a total of eight values. This modulation scheme has a smaller phase transitions than
QPSK but since the signals are also less distinctly difference from each other, it is thus prone
to higher bit errors. A QPSK modulator block is shown in Figure 2.9.
Figure 2.9 QPSK Modulator
Where M = 16, it is said of a 16-PSK signal with sixteen points on the constellation diagram,
equally spaced. 16-PSK can convey 4bits in a symbol and bit rate is four times of BPSK for
the same symbol rate. Despite 16-PSK being bandwidth efficient, it has higher bit error rate
than a common modulation which resulted that it is rarely used. As 16-PSK is considered Mary PSK modulation scheme, the bit error probability p e for M-ary PSK is expressed in
equation (2.16): -
15
M-ary PSK
pe  2T . 4.
E

. sin(
)
No
2M
(2.16)
Figure 2.10, 2.11 and 2.12 show the constellation diagrams for the QPSK, 8PSK and 16PSK
signals respectively.
Figure 2.10 QPSK Constellation Diagram
16
Figure 2.11 8-PSK Constellation Diagram
Figure 2.12 16-PSK Constellation Diagram
17
Pulse amplitude modulation (PAM) allows the narrowband of an analog signal to be
transferred as a digital signal in quantized discrete time signal at a fixed bit rate over a digital
transmission system. As shown in Figure 2.13, the block diagram of a PAM modulator
demonstrates the sequence of bits going through the process.
Figure 2.13 PAM Modulator
Each data symbol in a PAM contains J bits of information. Thus, for M-ary PAM
with M  2 J , each discrete message matches the amplitude of the waveform in each symbol
of period Tsym . The bit rate is Jf sym where symbol rate f sym 
1
Tsym
. The uniformly spaced
amplitude is given by: -
ai  d (2i  1)
Where i  
(2.17)
M
M
 1,...,0,...,
2
2
The PAM modulated signal is thus expressed as: 
 a  (t  kT )
x * (t ) 
k  
k
(2.18)
The x * (t ) signal has an infinite bandwidth and cannot be sent by a real transmitter. Hence,
the bandwidth has to be limited by a pulse shaping filter whose impulse response is g r (t )
which gives
x(t ) 

a
k  
k
g r (t  kT )
(2.19)
PAM is widely used in Ethernet communication standard where a 5-level PAM running at
25Mpulses/sec over two wire pairs. A 2-PAM modulated signal is shown in Figure 2.14
below where the generated PAM sequence goes through a Raised cosine filter.
18
Figure 2.14 2-PAM with Raised Cosine Filter
Quadrature amplitude modulation (QAM) conveys two digital bit streams by changing
the amplitudes of two carrier waves using amplitude shift keying. These two sinusoidal
waves are out of phase with each other by 90 degrees. Figure 2.15 shows a QAM modulator
block.
Figure 2.15 QAM modulator
The in-phase signal (the I-signal, e.g., cosine waveform) and a quadrature phase signal (the
Q-signal, e.g., sine waveform) are amplitude modulated with a finite number of amplitudes
and summed, resulting a combination of phase shift keying and amplitude shift keying. The
QAM equation is represented as follow in equation (2.20): -
19
s(t )  Ac
2
2
cos( (t )) cos(2f c t )  Ac
sin(  (t )) sin( 2f c t )
T
T
Where  
2
M
(2.20)
For a 4-QAM signal, we have M = 4, so we have 4 symbols representing a two bit word.
Therefore, for M = 16 and 64, we have 16 symbols representing a four bit word and 64
symbols representing a six bit word respectively.
The bit error probability p e for M-ary QAM is expressed in equation (2.21): M-ary QAM
pe  4(1 
1
M
).T .
3
E
.
M  1 No
(2.21)
Figure 2.16, 2.17 and 2.18 show the constellation diagram for the 4-QAM, 16-QAM and 64QAM diagrams.
Figure 2.16 4-QAM Constellation Diagram
20
Figure 2.17 16-QAM Constellation Diagram
Figure 2.18 64-QAM Constellation Diagram
21
2.3
Digital Transmissions
A digital transmission system may or may not include conversions between analog and
digital signals (sampling, A/D- and D/A- conversion). The transmitter end of the
transmission chain converts a digital bit-stream into an analog waveform which is sent to the
physical channel, which is in practice analog. The receiving end converts the received analog
waveform back to digital format [8].
The transmitter filter forms a continuous time signal from the symbol sequence Am with
g (t ) as the impulse response of the filter. The resulting signal is known as the transmitted
pulse shape and is represented in equation (2.22) below: -
s (t ) 

A
m  
Where T is the symbol interval; and
m
g (t  mT )
(2.22)
1
is the symbol rate
T
An example of the transmitted waveform would look like Figure 2.19.
Figure 2.19 Transmitted Waveform
Most of the important modulation methods in digital transmission systems are based on
complex alphabets and complex quadrature modulation which is the I/Q-modulation. In
quadrature modulation, the sine and cosine waveforms with the same carrier frequency can
be double-side-band modulated and detected independently which results the sine and cosine
waves carrying independent information signals. The two independent real baseband signals
(I and Q) are transmitted by modulating them into cosine and sine waveforms of the carrier
frequency. In the I-components and Q-components, nyquist pulse shaping is applied to
achieve high spectral efficiency. In a complex modulation model, the equation representing
the signal s (t ) is: -
22
s (t ) 


k  
a k g (t  kT )
(2.23)
At the channel block, the received waveform is represented in equation (2.24) as follow:
R(t )  b(t ) * s(t )  N (t )   b( )s(t   )d  N (t )



  b( )  Am g (t  mT   )d  N (t )


m  

A
m  
m
h(t  mT )  N (t )
(2.24)


Where h(t ) is the received pulse shape, h(t )  b(t ) * g (t )  b( ) g (t   )d

At the receiver block, the receiver has to recover the signal as much as possible after the
channel attenuates and distorts the signal. The receiver also needs to minimize the bit error
rate. The receiver filter filters out the adjacent channels and out-of-band noise and
interferences. It also effects on the pulse shape and acts as equalizer to compensate the linear
distortion of the channel. The timing recovery defines the right symbol timings for different
blocks and the correct sampling rate. Samples are taken at the sampling block from the
continuous time signal. In the ideal case, the samples are taken at time instants that
correspond to the transmitted symbol and when ISI, inter-symbol interference, is at
minimum.
Inter-symbol interference (ISI) is an unavoidable consequence of both wired and
wireless communication systems [9]. How ISI affects the signal is illustrated in Figure 2.20
and 2.21, waveform transmitted and waveform received.
Figure 2.20 Bit Sequence 101101 Sent
23
Figure 2.21 Symbols Received
It is observed that the each symbol received, interferes with one or more of the
subsequent symbols. The transmission medium has created a tail of energy that stretches
longer than needed. Figure 2.22 shows the actual transmitted signal (in dotted lines) versus
the received signal (green line), where it is the sum of the distorted symbols. This spreading
and smearing of symbols at the receiver which effects adjacent symbols have a high
probability of incorrect interpreting is known as inter-symbol interference or ISI.
Figure 2.22 Transmitted Signal Versus Received Signal
An eye diagram consists of many synchronized, overlaid traces of small sections (a few
symbols) of a signal. It is assumed that symbols are random and independent, so all the
possible symbol combinations are expected to have occurred [10]. The inter-symbol
interference can easily be seen in the eye diagram. The eye diagram depends on the received
pulse shape and the used constellation. Depending on the vertical opening, it is said to be
smaller with greater noise. The relation between the eye diagram and the ISI here is that the
ISI will reduce the vertical opening. The smaller the horizontal opening, the greater the
sensitivity to errors in timing phase. Figure 2.23 shows an interpretation of an eye diagram.
24
Figure 2.23 Eye Diagram Interpretation
2.4
Line Codes
A line code is a mapping of bits to signals that takes account of the transmission medium
through which it will be propagated [11]. The advantages of line coding includes: 
resistance to noise and inter-symbol interference;

has good match to the channel medium. No energy is wasted on unnecessary
frequencies;

simple decoding method;

less complex resulting in lesser cost;

highly reliable;

system can be easily predictable.
The intended goals of line coding are to: 
keep the spectrum narrow

remove DC component in AC coupled system

avoid synchronization problem
Notable line coding methods are: 
Unipolar non-return-to-zero

Polar non-return-to-zero

Unipolar return-to-zero
25

Bipolar return-to-zero

Manchester coding
The signal levels used for unipolar line code are ‘+V’ and ‘0’; polar are ‘+V’ and ‘–V’;
and bipolar are ‘+V’, ‘0’ and ‘–V’.
As shown in Figure 2.24, in unipolar non-return-to-zero coding, the ‘+A’ is represented
by bit 1 and remains ‘+A’ on the trailing clock edge of the previous bit. The ‘0’ is
represented by bit 0 and remains low on the trailing clock edge of the previous bit.
Figure 2.24 Unipolar Non-Return-To-Zero Coding
In polar non-return-to-zero coding, shown in Figure 2.25, the ‘+A’ is represented by bit
1 and remains ‘+A’ on the trailing clock edge of the previous bit while ‘-A’ is represented by
bit 0 and remains low on the trailing clock edge of the previous bit.
Figure 2.25 Polar Non-Return-To-Zero Coding
26
In unipolar return-to-zero coding, the ‘+A’ is represented by bit 1 and drops to zero
between each pulse while the ‘0’ is represented by bit 0 and remains low between each pulse
shown in Figure 2.26.
Figure 2.26 Unipolar Return-To-Zero Coding
In bipolar return-to-zero coding, the ‘+A’ is represented by bit 1 and drops to zero
between each pulse while the ‘-A’ is represented by bit 0 and remains low between each
pulse shown in Figure 2.27.
Figure 2.27 Bipolar Return-To-Zero Coding
Lastly, as shown in Figure 2.28, in Manchester coding, a ‘+A’ is represented by bit 1
from high to low transition at midpoint of a period while a ‘-A’ is represented by bit 0 from
low to high transition at midpoint of a period.
27
Figure 2.28 Manchester Coding
2.5
Constellation Diagrams
Signal constellations are graphical descriptions of the used signal space (set of signal
vectors in terms of desired basis). The signal space may be 1, 2, 3 or even N-dimensional. In
practice, there is always a combination of modulation errors that may be difficult to separate
and identify, as such, it is recommended to evaluate the measured constellation diagrams
using mathematical and statistically methods [12].
In a signal si (t ) , we may represent the signal by a point w S i in a D-dimensional
Euclidean space [13]. The set of points specified by the columns of matrix is therefore the
signal constellation represented below: -

W  wS w , wS2 ,..., wS M

The distance between two signals of si (t ) and s j (t ) is the Euclidean distance between
their associate vectors w S i and wS j is represented by d ij as: -
d ij  Ei  E j  2  ij Ei E j
(2.25)
For a M-ary PSK signal, the channel symbols are: -
si (t )  A. cos( 2Fc t 
2
.(i  1)   )
M
(2.26)
Where i  1,2,..., M
The constellation points are on circle at regular intervals.
If M  4 and   0 o , the alphabet size is 2 B  4 , two bits per symbol, B  2 and the
constellation diagram will look like Figure 2.29 below which is a QPSK modulation: -
28
Figure 2.29 QPSK Constellation Diagram with   0 o
If M  4 and   45 o , the alphabet size is 2 B  4 , two bits per symbol, B  2 and the
QPSK constellation diagram will look like Figure 2.30 below: -
Figure 2.30 QPSK Constellation Diagram with   45 o
29
If M  8 , the alphabet size is 2 B  8 which is three bits per symbol B  3 and it is a 8PSK signal. With   0 o , the constellation diagram for the 8-PSK will look like Figure 2.31
below: -
Figure 2.31 8-PSK Constellation Diagram with   0 o
For a M-ary QAM signal, the channel symbols are: -
si (t )  Ai . cos( 2Fc t   i   )
(2.27)
Where i  1,2,..., M
The constellation points are in regular rectangular shape.
If M  64 , the alphabet size is 2 B  64 , 6 bits per symbol B  6 , it is a 64-QAM
signal that will look like Figure 2.32 with   0 o .
30
Figure 2.32 64-QAM Constellation Diagram with   0 o
Generally, the quality of a digital communication system is expressed in terms of the
accurate delivery of the binary digits at the output of the detector with the binary digits that
were fed into the digital modulator. The measurement of the quality of the communication
system where a fraction of the binary digits that are delivered back in error is termed as the
bit error probability, p e or Bit Error Rate, BER. The performance of M-ary communication
systems is evaluated by means of the average probability of symbol error p e ,ce , which,
M  2 , is different than the average probability of bit error (or Bit-Error-Rate BER),
p e [14].
pe,cs  pe
for M  2
pe,cs  pe
for M  2 (i.e. Binary Communication Systems)
It is because that binary data are transmitted, the probability of bit error p e is chosen over
p e ,ce for performance evaluation in digital communication systems. The probability of error
can be calculated using the following equation (2.28): -
pe  T (1  p).
E
No
(2.28)
Where average signal energy =
1 Tcs
E  . ( s 0 (t ) 2  s1 (t ) 2 )dt
2 0
Time cross-correlation between signals =
(2.29)
31
p
1 Tcs
. s 0 (t ) s1 (t )dt
E 0
(2.30)
If p  1 , s0 (t )   s1 (t ) which is know as the optimum or ideal binary communication
system. Figure 2.33 illustrates when p  1 , p  0 and p  1 .
Figure 2.33 BER vs E/No for p  1 , p  0 and p  1
Because of ISI, noise, and other type of distortions, the received samples will not
correspond exactly to points in the signal constellation, for example, as shown in Figure 2.34
and 2.35, noisy constellations for QPSK and 64-QAM respectively.
Figure 2.34 QPSK Noisy Constellation
32
Figure 2.35 64-QAM Noisy Constellation
2.6
Equalizers
Equalization refers to any signal processing or filtering technique that is designed to
eliminate or reduce channel distortions. How well the channel effects can be equalized
depends on how well we know the channel transfer function [15]. The equalization process is
based on the knowledge that the impulse response of a finite impulse response or FIR filter is
same as its tap weights. Once the channel impulse response is known or estimated, by
applying the inverse of this to the filter, signal is distorted in the opposite direction of the
channel impulse response, and thus equalizing it.
The use of adaptive equalizers include: 
Compensate for signal distortion attributed to inter-symbol interference (ISI) which
is caused by multipath within time-dispersive channels;

Employed in high speed communication systems which do not use differential
modulation schemes or frequency division multiplexing [16].
Recursive least square (RLS) algorithm is used in adaptive filters to find the filter
coefficients that relate to recursively producing the least squares (minimum of the sum of the
33
absolute squared) of the error signal (difference between the desired and the actual signal)
[17].
The block diagram shown in Figure 2.36 demonstrates the RLS filter in minimizing a cost
function by selecting the filter coefficients wn and updating the filter whenever new data
arrives. e(n) and d (n ) are the error signals and desired signal respectively.
Figure 2.36 RLS Filter in Negative Feedback
The RLS algorithm for p  th order RLS filter can then be summarized as: With parameters of p = filter order;  = forgetting factor;  = value of initialize P (0) .
At initialization, wn  0 , P(0)   1 I , where I is the ( p  1)  by  ( p  1) identity matrix.
At computation for n  0,1,2,... ,
 x ( n)

 x(n  1) 

x ( n)  
 



 x(n  p)
(2.31)
 (n)  d (n)  w(n  1) T x(n)
(2.32)


g (n)  P(n  1) x * (n)   x T (n) P(n  1) x * (n)
1
(2.33)
P(n)  1 P(n  1)  g (n) x T (n)1 P(n  1)
(2.34)
w(n)  w(n  1)   (n) g (n)
(2.35)
Least mean squares (LMS) algorithms are used in adaptive filters to find the filter
coefficients that relate to producing the least mean squares of the error signal (difference
34
between the desired and the actual signal). It is a stochastic gradient descent method in that
the filter is only adapted based on the error at the current time [18]. Figure 2.37 shows the
block diagram of a LMS example.
Figure 2.37 LMS Filter Block Diagram
The LMS algorithm for p  th order LMS filter can then be summarized as: With parameters of p = filter order;  = step size.
^
At initialization, h(0)  0
At computation for n  0,1,2,... ,
x(n)  x(n), x(n  1),..., x(n  p  1)
T
(2.36)
^H
e(n)  d (n)  h (n) x(n)
^
^
h(n  1)  h(n)  e * (n) x(n)
^H
(2.37)
(2.38)
^
Where h (n) denotes the Hermitian transpose of h(n)
Maximum likelihood sequence estimation (MLSE) avoids the problem of noise
enhancement since it doesn’t use an equalizing filter: instead it estimates the sequence of
transmitted symbols [19]. The block diagram for a MLSE end to end wireless system is
shown in Figure 2.38 below: -
35
Figure 2.38 MLSE End to End Wireless System Block Diagram
The MLSE is described by the following equation.
^ L
 


d  arg max 2 d k* yk    d k d m* f k  m (2.39)
 k m
 k

It is obvious that MLSE output depends only on the sampler output of y(k ) and the
channel parameters of f n  k   f nTs  kTs  given that f (t )  h(t ) * h * (t ) .
2.7
MATLAB®
MATLAB® is a high-performance language for technical computing. It integrates
computation, visualization, and programming in an easy-to-use environment where problems
and solutions are expressed in familiar mathematical notation. MATLAB is an interactive
system whose basic data element is an array that does not require dimensioning. This allows
you to solve many technical computing problems, especially those with matrix and vector
formulations, in a fraction of the time it would take to write a program in a scalar noninteractive language such as C or Fortran [20]. For the purpose of this project, MATLAB
will be used to generate plots and spectrums in a web page for topics that will be covered
such as digital modulation, eye diagram and equalization. With the help of Simulink models
such as the frequency shift keying model can be simulated.
The MATLAB® Builder™ JA product is an extension to the MATLAB® Compiler™
product [21]. Using the MATLAB Builder JA product, M-code functions can be wrapped
from the MATLAB® product into one or more Java™ classes. A Java class is a portion of
36
Java code that houses a Java method, or a unit of code that performs some action. Java
classes are compiled into Java components, self-contained modules that run Java
applications. When deployed, each MATLAB function is encapsulated as a method of a Java
class and can be invoked from within a Java application. When Java packages are created,
there is an option of including the MATLAB Compiler Runtime (MCR), allowing users to
run and deploy their new applications on computers that do not have MATLAB installed.
2.8
Apache Tomcat
Apache Tomcat is an open source software implementation of Java Servlet and Java
Server Pages technologies [22]. Apache Tomcat is chosen as the web server for this project
because in order to run MATLAB® Builder™ JA compiled files, the web server must be
capable of running accepted Java frameworks like J2EE.
2.9
JavaScript
JavaScript is an interpreted programming language with object-oriented (OO)
capabilities. Syntactically, the core JavaScript language resembles C, C++ and Java, with
programming constructs such as the if statement, the while loop and the && operator.
JavaScript is most commonly used in web browsers and in that context, the general purpose
core is extended with objects that allow scripts to interact with the user, control the web
browser, and alter the document content that appears within the web browser window. This
embedded version of JavaScript runs scripts embedded within HTML web pages. It is
commonly called client-side JavaScript to emphasize that scripts are run by the client
computer rather than the web server [23].
37
CHAPTER THREE
Experiments
3.1
Digital Modulations
3.1.1
Amplitude Shift Keying
There are three experiments on ASK as listed below: 
Amplitude shift keying

Amplitude shift keying with Signal-to-Noise ratio

On-off keying with Signal-to-Noise ratio
The amplitude shift keying experiment is created to take in any length of bit streams
consisting “0” and “1” and output the modulated signal against the modulating signal and its
carrier waveform. In this experiment, bit streams of “10101111” is inputted to demonstrate
how it will perform with the carrier signal being a sine wave of sin( 2ft ) where f = 1 kHz.
The amplitude shift keying with signal-to-noise ratio experiment demonstrates how SNR
will affect the modulated signal in this experiment. The number of bits entered is randomized
to have equal probability of “0” and “1” with the frequency set at 2 kHz.
The on-off keying with signal-to-noise ratio is similar to the ASK with SNR except that bit
“0” is represented as level “0”.
Both SNR experiments output plots are shown in the Results section using the following
parameters in Table 3.1.
ASK-SNR
OOK-SNR
Number of bits SNR (dB) Number of bits SNR (dB)
1st simulation
8
10
8
5
nd
2 simulation
8
15
8
10
3rd simulation
8
35
8
35
Simulation
Table 3.1
3.1.2
ASK-SNR and OOK-SNR Experiments Parameters
Frequency Shift Keying
Three experiments in the frequency shift keying are demonstrated as follow: 
Frequency shift keying

Frequency shift keying with Signal-to-Noise ratio
38

Frequency shift keying Bit Error Rate plot
The FSK experiment here will have a bit stream of “10010011” being its input. Carrier
signal A is a sine wave of sin( 2ft ) and carrier signal B is a sine wave of sin( 6ft ) where f
= 1 kHz. The bit stream is modulated by both carrier signal A and B. The output plots are
shown in the Results section.
The FSK-SNR experiment demonstrates how SNR affects the modulated signal. Like the
ASK-SNR experiment, the number of bits entered is randomized to have equal number of
“0” and “1”. The frequency of carrier A and B are set to 1 kHz and 2 kHz respectively.
Using Table 3.2 as the experiment parameters, the results are shown in Results section.
Simulation
1st simulation
2nd simulation
3rd simulation
Table 3.2
FSK-SNR
Number of bits
SNR (dB)
8
5
8
15
8
35
FSK-SNR Experiments Parameters
The FSK-BER plot demonstrates the simulated plot against the theory calculated plot.
The number of bits entered is randomized and converted to appropriate frequencies. They are
then passed through additive white Gaussian noise channel and demodulated. The equation
representing the FSK is given by: -
si (t ) 
2E
cos( 2f i t   )
T
(3.1)
Where E is the energy, T is the symbol duration and  is the arbitrary phase (assume to be
zero). The two frequencies f1 and f 2 are orthogonal as shown: T

0
T

0
2E
2
cos(2f1t   )
cos(2f1t   )dt  E
T
T
(3.2)
2E
2
cos( 2f1t   )
cos( 2f 2 t   )dt  0
T
T
(3.3)
The bit error probability for coherent frequency shift keying is: -
Pb
Eb
1
erfc(
)
2
2N 0
The input parameters for this experiment are shown in Table 3.3.
(3.4)
39
FSK-BER
Number of bits Symbol Duration
1st simulation
20000
8
2nd simulation
20000
32
Simulation
Table 3.3
3.1.3
FSK-BER Experiments Parameters
Phase Shift Keying
Under phase shift keying experiments, there are six experiments listed as follow: 
Phase shift keying

Phase shift keying with Signal-to-Noise ratio

Quadrature phase shift keying with Signal-to-Noise ratio

8-Phase shift keying with Signal-to-Noise ratio

Phase shift keying Bit Error Rate plot

16-Phase shift keying Symbol Error Rate plot
In phase shift keying, the experiment will receive input bit stream of “11011011”. The
carrier signal is set at sin( 2ft ) where f = 1 kHz and is modulated with the bit stream. The
output of this experiment will plot the modulating signal, carrier signal and the modulated
signal.
For the PSK-SNR, QPSK-SNR and 8-PSK-SNR experiments, the SNR and number of
bits are set according to Table 3.4. These numbers of bits are randomized with equal
probability of “0” and “1” with frequency set at 2 kHz. The output plots are shown in Results
section.
Simulation
PSK-SNR
Number of
SNR
bits
(dB)
1st
simulation
2nd
simulation
3rd
simulation
Table 3.4
QPSK-SNR
Number of
SNR
bits
(dB)
EPSK-SNR
Number of
SNR
bits
(dB)
8
5
8
5
9
5
8
15
8
15
9
25
8
35
8
35
9
35
PSK-SNR, QPSK-SNR and EPSK-SNR Experiments Parameters
40
Both the PSK-BER and 16-PSK-SER experiments randomized the number of bits
entered pass them through additive white Gaussian noise. The received symbols are
demodulated based on the location in the constellation and compared with the theory
calculated BER or SER plots. Symbol duration is needed for the PSK-BER experiment.
The bit error probability for PSK-BER experiment is:-
Pb 
E
1
erfc( b )
2
N0
(3.5)
The symbol error rate for 16-PSK is: -
 Es
 
Pe  erfc 
sin( )
M 
 N 0
(3.6)
Where M  16 for this experiment.
Table 3.5 shows the parameters for both experiments.
Modulation Number of bits Symbol Duration
PSK
300000
8
16-PSK
300000
NA
Table 3.5
3.1.4
PSK-BER,16-PSK-SER Experiments Parameters
Pulse Amplitude Modulation
The 4-pulse amplitude modulation symbol error rate experiment will plot the symbol
error rate from a randomly generated signal against a theory calculated signal. The symbol
error probability for PAM is: -
Ps 
 Es
3
erfc
 5N
4
0





The input parameters for this experiment are set at 300000 bits.
3.1.5
Quadrature Amplitude Modulation
There are three experiments on quadrature amplitude modulation as listed below: 
4-Quadrature amplitude modulation Symbol Error Rate plot

16-Quadrature amplitude modulation Symbol Error Rate plot

64-Quadrature amplitude modulation Symbol Error Rate plot
(3.7)
41
For 4-QAM, the probability distribution function is:-
P4QAM  erfc(
Es
)
2N 0
(3.8)
The total probability of symbol error for 16-QAM is: -
P16QAM 
Es
3
erfc(
)
2
10 N 0
(3.9)
Using the above 16-QAM as reference, the total probability of symbol error for 64-QAM
is:-
P64QAM 
Es
Es 
15 
)  erfc 2 (k
)
erfc(k
16 
N0
N 0 
(3.10)
Table 3.6 shows the parameters for these experiments.
Modulation Number of bits
4-QAM
300000
16-QAM
300000
64-QAM
300000
Table 3.6
3.2
4-QAM, 16-QAM, 64-QAM Experiments Parameters
Digital Transmissions
In this digital transmission experiment, demonstrations consist of three parts, namely: 
Transmitted signal

Transmitted signal Eye diagram

Received signal Eye diagram
All three experiments have the following parameters:i.
sampling frequency = 10000 Hz
ii. symbol rate = 100 Hz/sample
iii. number of samples = 100 samples
iv. roll-off factor is 0.5
Transmitted signal experiment generates plot for the modulated signal either by 4-QAM
or 4-PSK. The signal will pass through a square root raised cosine filter.
42
The Transmitted signal Eye diagram experiment outputs the Eye diagram showing the
upper plot for the in-phase component (real) of the analyzed signal and the lower plot for the
quadrature component (imaginary).
The Received signal Eye diagram experiment plots the received signal Eye diagram. The
receiver employs a matched filter and therefore the combined filter seen by the receiver is an
approximate raised cosine filter with minimal inter-symbol interference (ISI).
The parameters for these experiments are shown in Table 3.7.
Experiment
Modulation type
4-QAM
Transmitted signal
4-PSK
Transmitted signal Eye diagram
4-QAM
4-PSK
4-QAM
Received signal Eye diagram
4-PSK
Table 3.7
3.3
Es
(dB)
N0
5
20
40
5
25
40
NA
NA
5
20
40
5
25
40
Transmission Path Experiments Parameters
Line Codes
There are 5 parts in this experiment where

Part 1, plotting of line code waveforms, parameters shown in Table 3.8

Part 2, plotting the power spectral density of the line codes, parameters shown in
Table 3.9

Part 3, AWGN effects on the line codes, parameters shown in Table 3.10

Part 4, Bandwidth effects on the line codes, parameters shown in Table 3.11

Part 5, Bandwidth and AWGN effects on the Eye diagram of the line codes,
parameters shown in Table 3.12
The experiment will investigate the different signaling formats and their properties, in
particular: -
43

causes of signal distortion in data communications channels,

effects of the inter-symbol interference (ISI) and channel noise by observing the eye
pattern.
Line Code
Bit Stream
Unipolar Non-Return-To-Zero
Polar Non-Return-To-Zero
Unipolar Return-To-Zero
11001010
Bipolar Return-To-Zero
Manchester Coding
Table 3.8
Line Code Experiments Parameters Part 1
Line Code
Binary Data Rate (kHz)
1
Unipolar Non-Return-To-Zero
10
1
Unipolar Non-Return-To-Zero
10
1
Polar Non-Return-To-Zero
10
1
Unipolar Return-To-Zero
10
1
Bipolar Return-To-Zero
10
1
Manchester Coding
10
Table 3.9
Line Code Experiments Parameters Part 2
Line Code
Bit Stream AWGN (Watt)
0
Unipolar Non-Return-To-Zero
0.02
0
Unipolar Non-Return-To-Zero
0.02
0
Polar Non-Return-To-Zero
0.02
11001010
0
Unipolar Return-To-Zero
0.02
0
Bipolar Return-To-Zero
0.02
0
Manchester Coding
0.02
Table 3.10 Line Code Experiments Parameters Part 3
44
Line Code
Bit Stream Bandwidth (kHz)
1
Unipolar Non-Return-To-Zero
4
1
Unipolar Non-Return-To-Zero
4
1
Polar Non-Return-To-Zero
4
11001010
1
Unipolar Return-To-Zero
4
1
Bipolar Return-To-Zero
4
1
Manchester Coding
4
Table 3.11 Line Code Experiments Parameters Part 4
Line Code
Bit Stream
Bandwidth (kHz) AWGN (Watt)
0
Unipolar Non-Return-To-Zero
4
0.02
0
Unipolar Non-Return-To-Zero
4
0.02
0
Polar Non-Return-To-Zero
4
0.02
1010110011001010
0
Unipolar Return-To-Zero
4
0.02
0
Bipolar Return-To-Zero
4
0.02
0
Manchester Coding
4
0.02
Table 3.12 Line Code Experiments Parameters Part 5
3.4
Constellation Diagrams
For this experiment, constellation plots for QPSK, 8-PSK and 64-QAM are generated.
The experiments give an idea how SNR affects the constellation points in those modulation
schemes.
 3 5 7
The QPSK signal consists of symbols e j where   { ,
4
4
,
4
,
4
} . Constellation
points are located symmetrically on the unit circle in the complex domain. The 8-PSK
consists of symbols e j where   {0,
  3
,
4 2
,
4
, ,
5 3 7
, , } . The constellation points
4 2 4
are like the QPSK, located symmetrically on the unit circle in the complex domain. The 64-
45
QAM signal consists of alphabet symbols a  jb where a, b  {7,5,3,1,1,3,5,7} . The
constellation points for 64-QAM are not on the unit circle, in fact, they are located on a
symmetric grid in the complex domain. All three modulation schemes have a symbol
sequence of 20000 symbols.
To demonstrate the effect of noise, AWGN is added to the generated complex symbol
streams where SNR is defined by the user. This noise is also a complex value. The SNR is
defined as: -
SNR  10 log
signal power
signal
 20 log
noise power
noise
(3.11)
The parameters for this experiment are listed in Table 3.13 below: -
Modulation Scheme SNR (dB)
QPSK
5
35
8-PSK
5
35
64-QAM
5
35
Table 3.13 Constellation Diagram Experiments Parameters
3.5
Equalizers
3.5.1
RLS and LMS Equalizers
The RLS and LMS equalizers experiments illustrate how to equalize a M-QAM signal
with
user
defined
iterations.
The
channel
coefficients
are
given
as chan  [1 0.45 0.3  0.2i ] . First, a random message is created and modulated by MQAM modulation scheme where M  4,8,16,32,64 as selected by user. Then a training
sequence is setup for the first iteration and decision-directed mode kicks in if iterations > 1.
Channel distortion is introduced to the signal with random noise and the received signal is
equalized. The experiment shows how equalization helps in recovering a signal through
noisy channel.
For this experiment, the parameters are listed in Table 3.14.
46
Equalizer Type
M-QAM Iteration
1
Recursive Least Square
4
8
1
Least Mean Square
4
8
Table 3.14 RLS, LMS Experiments Parameters
3.5.2
MLS Equalizer
The MLSE experiment uses the Viterbi algorithm to equalize a signal through a
dispersive channel. The experiment receives a baseband linearly modulated input signal and
outputs the maximum likelihood sequence estimate of the signal, using an estimate of the
channel modeled as a finite input response (FIR) filter. The modulation schemes available
are the M-PSK and M-QAM where M  4,8,16,32,64 . 200 symbols will be used in this
experiment while the number of iterations is set to 25. The received signal is decoded by
applying the FIR filter, corresponding to the channel estimate and the symbols in the input
signal. Thereafter, use the Viterbi algorithm to compute the traceback paths and the state
metric. These states metric are assigned to the symbols at each step of the Viterbi algorithm
and are based on Euclidean distance. The maximum likelihood sequence estimate of the
signal is plot on the constellation diagram, as a sequence of complex number corresponding
to the constellations points of the modulated signal.
The experiment will also output the number of symbol errors if any. Theoretically, the
MLSE equalizer yields the best possible performance. However, it is computationally
intensive. Table 3.15 details the experiment parameters.
Modulation Type M-ary
8
PSK
64
8
QAM
64
Table 3.15 MLSE Experiments Parameters
47
3.6
MATLAB®
All the experiments in this project are created using Mathworks MATLAB®. The
MATLAB® m-files are then compiled using MATLAB® Builder JA into Java archives and
classes to be implemented onto the web pages. The pre-requisite for Web implementation
includes: 
MATLAB, MATLAB Compiler, MATLAB Builder JA,

Java Development Kit (JDK). Sun JDK v1.6.0 and above,

A Java Compliant Web server which is capable of running accepted Java
frameworks like J2EE.
The first step to Web implementation for experiments created by MATLAB® is to build
the Java component on the m-files as follow: 1. Start deploytool from the MATLAB® command line
2. Select New Project > MATLAB® Builder JA Project
3. Specify the project name and click OK
4. In the Deployment Tool, right click the project class and select Add File
5. Using the MATLAB® Current Directory browser, navigate to the directory with the
m-files and add the m-file to the class by dragging it to the project class folder in the
Deployment Tool GUI
6. Click the icon on the Deployment Tool toolbar to build the project, creating the
project jar files
Next the Java code created for the specified m-file is compiled as follow: 1. Use javac to compile the Java source file created. The javac.exe is located in the bin
directory of the JDK installation
2. Ensure that the windows classpath is set to include javabuilder.jar and servlet-api.jar
After compilation of m-files and Java source file, the experiment is ready to be deployed
onto the Web server.
3.7
Apache Tomcat
The Web server used in this project is the Apache Tomcat, which can support J2EE and
Java Server Pages (JSP). Prior to the deployment of MATLAB® compiled Java jar and
classes, the Apache Tomcat’s lib directory must include MATLAB® javabuilder.jar and
servlet-api.jar as shown in Figure 3.1. Otherwise the experiment will not execute.
48
Figure 3.1 Tomcat required files in bin folder
3.8
Quiz
JavaScript are used to create the Quiz section of this project. Users are able to test their
digital communications knowledge online. The use of JavaScript allows validation of forms
in Internet browser and acts according to the functions written specifically to specific
response. Each topic has two html files written for the questions and results; and a JavaScript
to check user’s answers against the correct answers. The user will be prompted with the
correct answer and also those incorrect answers. Figure 3.2 shows the digital modulation
quiz screen capture.
49
Figure 3.2 Digital Modulation Quiz Screen Capture
50
CHAPTER FOUR
Results
4.1
Digital Modulations
4.1.1
Amplitude Shift Keying
The obtained results for ASK, ASK-SNR and OOK-SNR experiments are shown below.
Figure 4.1 ASK Experiment Plots
51
Figure 4.2 ASK-SNR Experiment No of bits = 8, SNR = 10dB
Figure 4.3 ASK-SNR Experiment No of bits = 8, SNR = 15dB
52
Figure 4.4 ASK-SNR Experiment No of bits = 8, SNR = 35dB
Figure 4.5 OOK-SNR Experiment No of bits = 8, SNR = 5dB
53
Figure 4.6 OOK-SNR Experiment No of bits = 8, SNR = 10dB
Figure 4.7 OOK-SNR Experiment No of bits = 8, SNR = 35dB
54
4.1.2
Frequency Shift Keying
The results for FSK experiment using bit stream of “10010011” is shown in Figure 4.10.
Figure 4.8 FSK Experiment Plots
The rest of the FSK experiments, namely SNR and BER are shown below.
55
Figure 4.9 FSK-SNR Experiment No of bits = 8, SNR = 5dB
Figure 4.10 FSK-SNR Experiment No of bits = 8, SNR = 15dB
56
Figure 4.11 FSK-SNR Experiment No of bits = 8, SNR = 35dB
57
Figure 4.12 FSK-BER Experiment using 20000 symbols, 8 symbol duration
Figure 4.13 FSK-BER Experiment using 20000 symbols, 32 symbol duration
58
4.1.3
Phase Shift Keying
The PSK experiment output the following figure as shown in Figure 4.14, with input bit
stream of “11011011”.
Figure 4.14 PSK Experiment Plots
PSK-SNR, QPSK-SNR, 8-PSK-SNR, PSK-BER and 16-PSK-SER results are shown in
the following figures.
59
Figure 4.15 PSK-SNR Experiment No of bits = 8, SNR = 5dB
Figure 4.16 PSK-SNR Experiment No of bits = 8, SNR = 15dB
60
Figure 4.17 PSK-SNR Experiment No of bits = 8, SNR = 35dB
Figure 4.18 QPSK-SNR Experiment No of bits = 8, SNR = 5dB
61
Figure 4.19 QPSK-SNR Experiment No of bits = 8, SNR = 15dB
Figure 4.20 QPSK-SNR Experiment No of bits = 8, SNR = 35dB
62
Figure 4.21 8-PSK-SNR Experiment No of bits = 9, SNR = 5dB
Figure 4.22 8-PSK-SNR Experiment No of bits = 9, SNR = 25dB
63
Figure 4.23 8-PSK-SNR Experiment No of bits = 9, SNR = 35dB
Figure 4.24 PSK-BER Experiment No of bits = 300000, symbol duration = 8
64
Figure 4.25 16-PSK-SER Experiment No of bits = 300000
65
4.1.4
Pulse Amplitude Modulation
The output plot for the 4-PAM-SER is shown in Figure 4.26.
Figure 4.26 4-PAM-SER Experiment No of bits = 300000
4.1.5
Quadrature Amplitude Modulation
The output plots for the three M-QAM symbol error rate experiment are shown in the
following figures.
66
Figure 4.27 4-QAM-SER Experiment No of bits = 300000
Figure 4.28 16-QAM-SER Experiment No of bits = 300000
67
Figure 4.29 64-QAM-SER Experiment No of bits = 300000
4.2
Digital Transmissions
The following figures are the output results for the Digital Transmission experiments.
68
Figure 4.30 4-QAM Filtered Signal and
Es
 5dB Noisy Signal
N0
69
Figure 4.31 4-QAM Filtered Signal and
Es
 20dB Noisy Signal
N0
70
Figure 4.32 4-QAM Filtered Signal and
Es
 40dB Noisy Signal
N0
71
Figure 4.33 4-PSK Filtered Signal and
Es
 5dB Noisy Signal
N0
72
Figure 4.34 4-PSK Filtered Signal and
Es
 25dB Noisy Signal
N0
73
Figure 4.35 4-PSK Filtered Signal and
Es
 40dB Noisy Signal
N0
Referring to Table 3.7, the parameters for the Transmitted signal Eye diagram generated
plots as shown in the below figures.
74
Figure 4.36 4-QAM Transmitted Signal Eye Diagram
Figure 4.37 4-PSK Transmitted Signal Eye Diagram
75
Finally, referring to Table 3.7 for the Received signal Eye diagram experiment, the
results are shown in the following figures.
Figure 4.38 4-QAM Received Signal Eye Diagram with
Es
 5dB
N0
76
Figure 4.39 4-QAM Received Signal Eye Diagram with
Es
 20dB
N0
77
Figure 4.40 4-QAM Received Signal Eye Diagram with
Es
 40dB
N0
78
Figure 4.41 4-PSK Received Signal Eye Diagram with
Es
 5dB
N0
79
Figure 4.42 4-PSK Received Signal Eye Diagram with
Es
 25dB
N0
80
Figure 4.43 4-PSK Received Signal Eye Diagram with
Es
 40dB
N0
81
4.3
Line Codes
With Table 3.8 parameters, the results for Part 1 experiments are shown below.
Figure 4.44 Unipolar NRZ - 11001010
82
Figure 4.45 Polar NRZ - 11001010
Figure 4.46 Unipolar RZ - 11001010
83
Figure 4.47 Bipolar RZ - 11001010
Figure 4.48 Manchester Coding - 11001010
84
The results for Part 2 of the experiments are shown in the figures below.
Figure 4.49 Unipolar NRZ Power Spectral Density, Rb = 1kbps
85
Figure 4.50 Unipolar NRZ Power Spectral Density, Rb = 10kbps
Figure 4.51 Polar NRZ Power Spectral Density, Rb = 1kbps
86
Figure 4.52 Polar NRZ Power Spectral Density, Rb = 10kbps
Figure 4.53 Unipolar RZ Power Spectral Density, Rb = 1kbps
87
Figure 4.54 Unipolar RZ Power Spectral Density, Rb = 10kbps
Figure 4.55 Bipolar RZ Power Spectral Density, Rb = 1kbps
88
Figure 4.56 Bipolar RZ Power Spectral Density, Rb = 10kbps
Figure 4.57 Manchester Coding Power Spectral Density, Rb = 1kbps
89
Figure 4.58 Manchester Coding Power Spectral Density, Rb = 10kbps
The results for Part 3 of the experiments are shown in the figures below.
90
Figure 4.59 Unipolar NRZ, AWGN = 0W
Figure 4.60 Unipolar NRZ, AWGN = 0.02W
91
Figure 4.61 Polar NRZ, AWGN = 0W
Figure 4.62 Polar NRZ, AWGN = 0.02W
92
Figure 4.63 Unipolar RZ, AWGN = 0W
Figure 4.64 Unipolar RZ, AWGN = 0.02W
93
Figure 4.65 Bipolar RZ, AWGN = 0W
Figure 4.66 Bipolar RZ, AWGN = 0.02W
94
Figure 4.67 Manchester Coding, AWGN = 0W
Figure 4.68 Manchester Coding, AWGN = 0.02W
95
The results of the bandwidth effects on line code experiments are shown n the figures
below.
Figure 4.69 Unipolar NRZ, Bandwidth = 1kHz
96
Figure 4.70 Unipolar NRZ, Bandwidth = 4kHz
Figure 4.71 Polar NRZ, Bandwidth = 1kHz
97
Figure 4.72 Polar NRZ, Bandwidth = 4kHz
Figure 4.73 Unipolar RZ, Bandwidth = 1kHz
98
Figure 4.74 Unipolar RZ, Bandwidth = 4kHz
Figure 4.75 Bipolar RZ, Bandwidth = 1kHz
99
Figure 4.76 Bipolar RZ, Bandwidth = 4kHz
Figure 4.77 Manchester Coding, Bandwidth = 1kHz
100
Figure 4.78 Manchester Coding, Bandwidth = 4kHz
The final part of the experiments is shown in the figures below.
101
Figure 4.79 Unipolar NRZ, BW = 4kHz, AWGN = 0W
Figure 4.80 Unipolar NRZ, BW = 4kHz, AWGN = 0.02W
102
Figure 4.81 Polar NRZ, BW = 4kHz, AWGN = 0W
Figure 4.82 Polar NRZ, BW = 4kHz, AWGN = 0.02W
103
Figure 4.83 Unipolar RZ, BW = 4kHz, AWGN = 0W
Figure 4.84 Unipolar RZ, BW = 4kHz, AWGN = 0.02W
104
Figure 4.85 Bipolar RZ, BW = 4kHz, AWGN = 0W
Figure 4.86 Bipolar RZ, BW = 4kHz, AWGN = 0.02W
105
Figure 4.87 Manchester Coding, BW = 4kHz, AWGN = 0W
Figure 4.88 Manchester Coding, BW = 4kHz, AWGN = 0.02W
106
4.4
Constellation Diagrams
Experiments results on constellation diagram are shown in the following figures.
Figure 4.89 QPSK at SNR = 5dB Constellation Diagram
107
Figure 4.90 QPSK at SNR = 35dB Constellation Diagram
Figure 4.91 8-PSK at SNR = 5dB Constellation Diagram
108
Figure 4.92 8-PSK at SNR = 35dB Constellation Diagram
Figure 4.93 64-QAM at SNR = 5dB Constellation Diagram
109
Figure 4.94 64-QAM at SNR = 35dB Constellation Diagram
4.5
Equalizers
4.5.1
RLS and LMS Equalizers
Using Table 3.14 parameters, the RLS, LMS experiments output the following results as
shown.
110
Figure 4.95 4-QAM RLS Equalized, Non-Equalized 1 Iteration Constellation
Figure 4.96 4-QAM LMS Equalized, Non-Equalized 1 Iterations Constellation
111
Figure 4.97 4-QAM RLS Equalized, Non-Equalized 8 Iterations Constellation
Figure 4.98 4-QAM LMS Equalized, Non-Equalized 8 Iterations Constellation
112
4.5.2
MLS Equalizer
Referring to Table 3.15, the output results for 8-PSK modulation and 64-PSK
modulation are shown in the following figures.
Figure 4.99 8-PSK MLSE Equalized, Non-Equalized Constellation
113
Figure 4.100
64-PSK MLSE Equalized, Non-Equalized Constellation
114
Figure 4.101
8-QAM MLSE Equalized, Non-Equalized Constellation
115
Figure 4.102
4.6
64-QAM MLSE Equalized, Non-Equalized Constellation
Quiz
The result for the digital modulation quiz is shown in Figure 4.103 screen capture. Users
are able to re-take or view solution by clicking either the Re-take Quiz or View Solution
buttons.
116
Figure 4.103
Digital Modulation Quiz Result
117
CHAPTER FIVE
Discussion
5.1
Digital Modulations
5.1.1
Amplitude Shift Keying
The ASK experiment’s result well explains that when bit "1" is sent, the modulated
signal's amplitude, frequency and phase are kept constant. At bit "0", the modulated signal is
represented by the absence of the carrier signal.
The ASK-SNR experiment’s output results with 8 bits stream and SNR = 10dB shows
that the modulated signal is noisy compared with a zero noise modulated signal. Some of bit
“0” is interpreted as bit “1”. Increasing the SNR to 15dB gives an acceptable modulated
signal even though it is not clean. With SNR = 35dB, the modulated signal appears clean.
The OOK-SNR experiment results shown in Figure 4.5 when an 8 bits stream is sent
with SNR = 5dB outputs a noisy modulated signal. At one point, a bit "0" is not well
represented on the modulated signal, varying between amplitude level 1 and 2, which can be
mistaken as a bit "1". When SNR is increased to 10dB, the modulated signal seems
acceptable. Obvious improvement is shown when SNR = 30dB, the bit stream is well
represented after modulation.
5.1.2
Frequency Shift Keying
The FSK experiment demonstrated how a bit stream of “10010011” is modulated using
two carrier signals resulting in bit “0” represented by carrier signal B and bit “1” represented
by carrier signal A.
Output results of the FSK-SNR experiments show that with a SNR of 5dB, the
modulated signal is distorted. At some point, there is no distinction between a bit “0” and
bit”1” representations. An acceptable signal would be to increase the SNR to 15dB as shown
in Figure 4.10. Frequencies change are better visible than when SNR = 5dB. A clean
modulated signal is best achieved when SNR = 35dB which looks like a complete replica of
the zero noise modulated signal.
118
Comparing the two figures for the FSK-BER experiments outputs, it is noted that with 8
symbols/sec symbol duration, to achieve a BER of 1 * 10 3 , the required
approximately 9.6dB while to achieve the same BER, the required
Eb
is
N0
Eb
is 10dB for 32
N0
symbols/sec symbol duration. It is observed that for higher symbol duration, higher
Eb
is
N0
required. The comparisons of BER plots are tabled in Table 5.1 among FSK and PSK
modulation schemes.
5.1.3
Phase Shift Keying
The PSK experiment for a bit stream of “11011011” demonstrated how the signal is
modulated with a change in phase whenever there is a change in bit. This fits the theory of
PSK where the signal is modulated by the phase of the carrier signal.
Experiments demonstration for the PSK-SNR simulations when SNR is 5dB shows that
the distorted modulated signal does not have a clear indication of bit “0” or “1”. With an
increase to about 15dB, the modulated signal seems acceptable as shown in Figure 4.16.
Keep on increasing the SNR to about 35dB will result in a clean modulated signal.
Likewise, the QPSK-SNR experiment gives a clean modulated signal when SNR is at
35dB. The acceptable signal was obtained when SNR = 15dB while a badly distorted
modulated signal is when SNR = 5dB.
The 8-PSK experiment has a slightly different acceptable level for SNR. At SNR = 5dB,
the modulated signal was distorted. The SNR has to be increased to 25dB for an acceptable
modulated signal. Like the rest, when SNR = 35dB, the 8-PSK modulated signal is clean.
The 8-PSK transmitted signal shows smaller phase transitions than QPSK which is a
good thing but since the signals are also less distinctly difference from each other, makes 8PSK prone to higher bit errors. The reason why 8-PSK is still being used is because 8-PSK
can pack more bits per symbol, 3 bits can be conveyed with each symbol transmitted. In
addition, the throughput of 8-PSK is 50% better than QPSK which can transmit just 2 bits
per symbol, making 8-PSK a bandwidth efficient modulation.
Binary PSK modulation is the most robust of the all PSK modulations as it requires
extreme distortion to make the demodulator give an incorrect recovery of the modulated
signal. However, because of the limitation that is can only modulate 1 bit/symbol, it is
considered unsuitable for high bitrate transmission if bandwidth is limited.
119
By looking at the PSK-BER plot shown in Figure 4.24, it is noted that to achieve a BER
of 1 * 10 5 , the required
Eb
is approximately 9.3dB. Comparing to the FSK-BER
N0
experiment with the same symbol duration of 8, only a BER of 3 *10 3 is achieved for
Eb
of 9.6dB. Referring to Table 5.1, it is obvious that the PSK modulation scheme fares
N0
better than the FSK modulation scheme.
Modulation
Type
Symbol
Duration
Frequency
Shift Keying
8
Frequency
Shift Keying
32
Phase Shift
Keying
8
BER
1 * 10 1
1 * 10 2
1 * 10 3
1 * 10 4
1 * 10 5
2
7.3
9.6
NA
NA
2
7.3
10
NA
NA
-1
4.4
6.8
8.4
9.3
Eb
(dB)
N0
Eb
(dB)
N0
Eb
(dB)
N0
Table 5.1
FSK and PSK-BER Plots’ Comparisons
The 16-PSK-SER plot demonstrates that in order to achieve a BER of 1 * 10 5 , an
approximate
Es
of 24dB is required. We will be comparing the 16-PSK-SER plots with
N0
those of the 4-PAM and M-QAM symbol error rate plots as tabled in Table 5.2
5.1.4
Pulse Amplitude Modulation
The results obtained from the 4-PAM-SER experiment plots show that to achieve a BER
of 1 * 10 5 , an approximate
5.1.5
Es
of 24dB is needed.
N0
Quadrature Amplitude Modulation
From the results of the symbol error rate plots for QAM modulations, to achieve a BER
of 1 * 10 5 , an approximate
Es
of 13dB, 19.5dB and 26.5dB are required for 4-QAM, 16N0
QAM and 64-QAM respectively.
120
Referring to Table 5.2, the 4-QAM offers the best
Even though it requires only 13dB for its
Es
at 13dB for a BER of 1 * 10 5 .
N0
Es
, lesser bits are packed into the symbols during
N0
transmission, making it not very bit efficient as opposed to 16-QAM and 64-QAM. However,
it performs much better than the 4-PAM due to noise variance is higher in PAM and hence
higher symbol error rate. In addition, 4-PAM is not fully utilizing the bandwidth as opposed
to 4-QAM. The 64-QAM requires the highest
Es
of 26.5dB because more bits per symbols
N0
are transmitted and if the energy of the constellation is to remain the same, the points on the
constellation plot must be close together. This results the transmission to be more susceptible
to noise as compared to 4-QAM and 16-QAM. The 16-PSK may be bandwidth efficient but
it requires
Es
 24dB to achieve a BER of 1 * 10 5 . The 16-QAM however fares better than
N0
the 16-PSK where it only requires
Es
 19.5dB for the same BER for the same bit
N0
efficiency.
Modulation Type
16-Phase Shift Keying
4-Pulse Amplitude
Modulation
4-Quadrature Amplitude
Modulation
16-Quadrature Amplitude
Modulation
64-Quadrature Amplitude
Modulation
Table 5.2
1 * 10 1
1 * 10 2
1 * 10 3
1 * 10 4
1 * 10 5
(dB)
15.5
19
22
23
24
(dB)
7.5
12
13.5
15.5
16.5
(dB)
4.2
8.2
10.3
11.8
13
(dB)
12.5
15.5
17.5
19
19.5
(dB)
18.5
22
24
25
26.5
BER
Es
N0
Es
N0
Es
N0
Es
N0
Es
N0
PSK, PAM, QAM-SER Plots’ Comparisons
121
5.2
Digital Transmissions
Referring to the output results obtained from the 4-QAM and 4-PSK experiments, both
modulation schemes have badly distorted signal when
Es
= 5dB. However, 4-QAM
N0
performs better than the 4-PSK when comparing the figures shown in Figure 4.31 and 4.34
where 4-QAM only needs a 20dB
25dB
Es
for an acceptable signal while 4-PSK needs a
N0
Es
E
. The two modulation schemes are able to transmit a clean signal when s =
N0
N0
40dB. Summary for comparisons are shown in Table 5.3.
Experiment
Modulation type
4-QAM
Transmitted signal
4-PSK
Table 5.3
Es
(dB)
N0
Modulated Signal Observation
5
20
40
5
25
40
Badly distorted
Acceptable
Clean
Badly Distorted
Acceptable
Clean
4-QAM, 4-PSK
Es
(dB) Comparisons
N0
The comparisons of the Eye diagram during transmission between the 4-QAM and 4PSK are shown in Table 5.4. As observed, the Eye diagram for the 4-QAM has a bigger and
wider opening opposed to 4-PSK in the absence of noise. The 4-QAM would require a
longer time interval over which the waveform can be sampled. The best time to sample for
both modulations is at 10ms.
Modulation
4-QAM
4-PSK
A
9ms
8.5ms
Table 5.4
B
0.4375
0.28125
C
1ms
1.27ms
D
0.4375
0.281
E
0.3125
0.25
t*
10ms
10ms
4-QAM, 4-PSK Transmitted Signal Eye Diagram Comparisons
Where A = time interval over which the waveform can be sampled;
B = margin over noise;
122
C = distortion of zero crossing;
D = slope: sensitivity to timing error;
E = maximum distortion;
t* = best time to sample
At the receiving end, the Eye diagram data obtained from the received signals are tabled
in Table 5.5. The 4-QAM has a lower time interval over which the waveform can be sampled
hence more sensitive to errors in timing phase. But it has a wider vertical opening which
means less prone to inter-symbol interference. The 4-PSK on the other hand, has a lower
maximum distortion and less sensitive to errors in timing phase but narrower vertical
opening which is more prone to inter-symbol interference.
Modulation
4-QAM
4-PSK
Es
(dB)
N0
A
B
C
D
E
t*
5
NA
NA
NA
NA
NA
NA
20
40
6ms
7.4ms
0.8
0.875
3ms
2.76ms
0.8
0.875
0.4
0.25
10ms
10ms
5
NA
NA
NA
NA
NA
NA
25
40
7ms
7.6ms
0.3125
0.375
2.5ms
2.5ms
Table 5.5
0.3125 0.125
0.375 0.0625
10ms
10ms
Remarks
Badly
distorted
Acceptable
Clean
Badly
distorted
Acceptable
Clean
4-QAM, 4-PSK Received Signal Eye Diagram Comparisons
123
5.3
Line Codes
Part 1 for the line codes experiments behave as expected. Bit stream of “11001010” was
sent to observe how each line coding responses with the corresponding output waveforms.
The results obtained for Part 2 of the experiments from the PSD plots for the line codes
are tabled in Table 5.6.
Line Code
Unipolar
NRZ
Polar NRZ
Unipolar RZ
Bipolar RZ
Manchester
Rb (kbps)
f p1 (kHz)
f p 2 (kHz)
f n1 (kHz)
f n 2 (kHz)
1
10
1
10
1
10
1
10
1
10
0
0
0
0
0
0
0.45
4.5
0.75
7.5
1.5
14
1.4
14
3
28
1.35
13.5
2.9
29
1
10
1
10
2
20
1
10
2
20
2
18
2
20
4
40
2
20
4
40
Table 5.6
BW
(kHz)
1
10
1
10
2
20
1
10
2
20
Line Codes’ Comparisons
For Part 3 of the experiment, we could see the effects of AWGN on the line codes when
we compare a zero AWGN to a 0.02 AWGN. Noise could be seen on the resultant line coded
signals.
Part 4 of the experiment investigate the channel bandwidth effects on the line codes.
From Part 2 of the experiment, we had known that the minimum bandwidth for Unipolar
NRZ, Polar NRZ and Bipolar RZ require bandwidth  Rb where Rb = Binary Data Rate
(kbps). As for Unipolar RZ and Manchester coding, the minimum bandwidth required
are 2 Rb . Therefore, the results obtained for this part of the experiments conforms to theories.
The final part of the experiment investigates the effects of bandwidth and AWGN on the
line codes’ Eye diagram. Theoretically, the required minimum bandwidth is B 
Rb
.
2
Since Rb  1kbps therefore, B  500Hz . Bandwidth used in this part of the experiment =
4kHz which conforms to B 
Rb
. The Eye diagram values obtained are listed in Table 5.7.
2
124
Line Code
BW
(kHz)
Unipolar
NRZ
4
Polar NRZ
4
Unipolar
RZ
4
Bipolar RZ
4
Manchester
4
AWGN
(Watt)
0
0.02
0
0.02
0
0.02
0
0.02
0
0.02
Table 5.7
A(ms)
B
C(ms)
D
E
t*(ms)
1
0.95
1
1
0.71
0.63
0.71
0.63
0.5
0.47
0.45
0.21
0.92
0.63
0.9
0.71
0.88
0.48
0.88
0.48
0.02
0.04
0.02
0.04
0.02
0.06
0.02
0.05
0.02
0.04
0.45
0.43
0.96
0.89
0.73
0.53
0.72
0.81
1
0.72
0.018
0.27
0.04
0.36
0.018
0.52
0.018
0.4
0.08
0.44
1
1
1
1
0.75
0.75
0.75
0.75
0.75
0.75
Line Codes’ Eye Diagram Values
Apparently, we could deduce the advantages and disadvantages of each line codes
presented in Table 5.8.
Line Code
Unipolar
NRZ
Advantages
1. Simple to implement
2. Less bandwidth for
transmission
1. Simple to implement
2. No DC component
Polar NRZ
1. Simple to implement
2. Spectral lines can be used as
symbol timing clock
Unipolar RZ
Bipolar RZ
Manchester
1. No DC component
2. Lesser bandwidth
3. No signal droop
4. Single error detection
capability
1. No DC component
2. No signal droop
3. Easy to synchronize
Table 5.8
Disadvantages
1. Presence of DC level at 0kHz
2. Low frequency components result in
signal droop
3. No error correction capability
4. No clocking capability
5. Loss of synchronization if long string
of zeros are transmitted
1. Non-zero at 0kHz causes signal droop
2. No error correction capability
3. No clocking capability
4. Loss of synchronization if long string
of zeros are transmitted
1. Presence of DC level at 0kHz
2. Non-zero at 0kHz causes signal droop
3. No error correction capability
4. Double bandwidth needed opposed to
Unipolar NRZ
5. Loss of synchronization if long string
of zeros are transmitted
1. No clocking capability
2. Loss of synchronization if long string
of zeros are transmitted
1. Large bandwidth
2. No error detection capability
Line Codes’ Advantages and Disadvantages
125
5.4
Constellation Diagrams
From the output results obtained for the constellation diagram experiments, it is clear
that the diagram shows the mapping of binary digits to QPSK, 8-PSK and 64-QAM channel
symbols in the absence of noise. With reference to Figure 4.89, 4.91 and 4.93, it is apparent
that with SNR = 5dB, mapping of binary digits are not possible. The samples will not
correspond exactly to points in the signal constellation. The received samples form a
Gaussian cloud around the points in the constellation.
For SNR = 35dB, the received samples are mapped to the corresponding constellation
points, denoting the transmitted symbols.
5.5
Equalizers
5.5.1
RLS and LMS Equalizers
The output obtained from the RLS and LMS equalizers experiments demonstrated that
the LMS pales when one iteration cycle was used. The equalized signal had points away
from the corresponding constellations. When 8 iteration cycles was used, the equalized
signal converged well to the constellation points for both equalizations. Table 5.9 compares
the advantages and disadvantages for both equalization methods.
Equalization
Recursive Least Square
Least Mean Square
Advantages
1. Simple algorithm
2. Useful for testing
purposes
3. No inverted matrices
thereby saving
computational power
4. Provides intuition
1. Simple to implement
2. No preliminary modeling
needed
Table 5.9
Disadvantages
1. Unstable when used with a
forgetting factor
1. Slow rate of convergence
2. Convergence speed
affected by step size and
Eigenvalue spread of the
correlation matrix
RLS and LMS Comparisons
126
5.5.2
MLS Equalizer
The results as shown in the MLSE experiments present the constellation plots for
equalized and non-equalized PSK and QAM modulated signals. Accordingly, the MLSE is a
non-linear equalizer and a data sequence estimator. It tests all possible data sequence and
picks the sequence that maximum MLSE criterion as transmitted one.
With 8-PSK and 8-QAM, the experiments resulted in 0 errors whilst with 64-PSK and
64-QAM, number of errors are 21 and 14 respectively. This shows that for MLSE,
complexity of this technique grows exponentially with the length of the delay spread,
therefore impractical on most channels of interest. However performance of MLSE is often
used as an upper bound on performance for other equalization techniques. MLSE is the
optimal form of sequence detection but is highly complex
5.6
Quiz
The quizzes were written in JavaScript that check on answered questions against the
correct answers. Users are tested on their knowledge for each topics covered in the
experiments which comprises of 10 questions each. The JavaScript employed works
successfully as expected.
127
CHAPTER SIX
Reflections
The objectives and scopes as stated in the Introduction chapter emphasize on designing
and developing an online digital communications experiment which will caters to students,
lecturers and interested parties. In order to fulfill the goals, the key learning elements
include:
MATLAB®, including learning to create experiments and analyze results,

JavaScript,

Digital Communications theories,

Interfacing JavaScript, Apache Tomcat and MATLAB® experiments.
During the initial phase of the project development, I faced problems such as unable to
deploy MATLAB® experiments over the Web, which is part of the core objectives. Failure
in producing online experiments would mean that I have to develop the experiments using
MATLAB® GUI. Using MATLAB® GUI will then be similar to past year project. It defeats
the purpose of sharing digital communications techniques and theories with current students,
lecturers and interested parties. Fortunately, because of my persistence in getting the online
part of the project to work, I manage to deploy my first experiment, with the help of
MATLAB® file exchange forums where I found examples related to online deployment.
Those examples required some modifications and tweaking to fit into what I needed for my
project to work, which was a breeze after I understood the underlying methods.
Some of the other problems that I faced and had them fixed during the course of
development are:
Installation of Apache Web server failed on my notebook. Port 8080 was used by
another application which I do not know. Re-format my notebook and could have a
fresh installation of the Web server. However the Web server was dropped because
it does not support JAVA JSP and war-files.

Installation of Microsoft IIS7 Web server failed on my notebook. Port 80 was used
by an application which I do not know. Re-format my notebook. IIS7 installed with
success but was later abandoned as it does not support JAVA JSP and war-files.
128

MATLAB 7R14 was installed and used initially. However due to the Web server has
bugs, it was dropped.

MATLAB 2006b was installed and used. Because there weren’t many examples on
Web deployment, it was dropped.

MATLAB 2008b was installed and used. But the student version does not come with
NET builder or JAVA builder. Chose MATLAB 2007b version that has JAVA
builder.
These were very time consuming which resulted in some changes to the experiments
previously mentioned in TMA01. Experiments such as the transmitter and receivier for
digital communications and binary symmetric channel were replaced by digital modulation,
digital transmission, line codes, constellation plots and equalizers. The changes are reflected
in my amended Gantt chart as shown in Figure 6.1.
Each experiment in this project outputs a Webpage of plots or waveforms. Some may
have a single waveform; the others may have 2 plots on the same Webpage. The experiments
for a topic such as Digital Transmission were split into parts. The reason for doing so is
because each html page form is assigned to a Java Serlvet. Tests were made to incorporate
multiples Java Servlet on a single html page but it proves to be unsuccessfully and time was
running out. Therefore, if I had done the project differently, I would spend more time on
applications’ research efficiently. Although time was wasted, I would say that my project
was completed successfully and as expected within the project development cycle.
129
Figure 6.1 Revised TMA01 Gantt Chart
130
CHAPTER SEVEN
Conclusions and Recommendations
From the experiments that I have worked on and developed, the output results obtained
demonstrated that there is no need for hardware apparatus or equipment for digital
communications simulations. Experiments can be simulated with the help of MATLAB®
and deployed over the Internet using Apache Tomcat and some Java compiled classes.
The digital modulation sections demonstrated modulation schemes such as the
Amplitude Shift Keying, Pulse Amplitude Modulation and Quadrature Amplitude
Modulation. Plots obtained include bit stream waveforms, bit error rate plots and symbol
error rate plots. By comparing the BER and SER plots among the different modulation
schemes, we could conclude the pros and cons of each modulation schemes and using them
appropriately for future projects. The digital transmission experiment compares the 4-PSK
and 4-QAM transmitted and received signals over a noiseless and noisy channel. It was
noted that the 4-QAM fares better than the 4-PSK where it is less prone to inter-symbol
interference. However it is more sensitive to errors in timing phase. Line codes experiments
show that Unipolar RZ has spectral lines that can be used as symbol timing clock. The
Manchester coding needs large bandwidth for coding. Both the Unipolar NRZ and Polar
NRZ are easy to implement while the Bipolar RZ uses lesser bandwidth. The constellation
diagram experiment gave an idea of how SNR affects the mapping of symbols in the
constellation points. The acceptable SNR level was 35dB as experimented. Lastly the
equalizers experiments compare the RLS and LMS equalizers in terms of performance and
the comparisons of PSK and QAM modulation schemes using MLSE equalization. The RLS
has a faster convergence rate than the LMS counterpart but it is unstable when used with a
forgetting factor. The 64-QAM performs better than 64-PSK in terms of number of symbol
errors during transmission.
Because of time limitation, the project did not cover topics such as:
Error Control Coding

Source Coding

Special Filters
131
In addition, each experiment outputs only a single Webpage of plot or waveforms. Therefore,
my recommendations for future students who are going to pursue further on this project is to
implement the above mentioned experiments and work on improving the output of each
experiment which will incorporate different waveforms and plots on a single Webpage for
comparisons.
Despite the time constraints that I faced, the project was completed successfully
according to requirements and time frame which is a great achievement and act as an
invaluable reference for future students who will be undertaking similar projects.
132
LIST OF REFERENCES
[1]
Section 1.3, The Mathworks, Inc. MATLAB and Simulink for Technical Computing,
http://www.mathworks.com
[2]
Section 2.1.1, National Programme on Technology Enhanced Learning (NPTel),
http://nptel.iitm.ac.in
[3]
Section 2.1.1, Massachusetts Institute of Technology, Principles of Digital
Communications I, http://ocw.mid.edu/OcwWeb/Electrical-Engineering-andComputer-Science/6-450Fall-2006/CourseHome/index.htm
[4]
Section 2.1.2, Charan Langton, Complex2Real.com,
http://complextoreal.com/chapters/mod1.pdf, p. 6
[5]
Section 2.1.2, Amplitude-shift keying, Wikipedia, the free encyclopedia,
http://en.wikipedia.org/wiki/Amplitude_Shift_Keying
[6]
Section 2.1.2, K.F.Ibrahim, “Newnes guide to Television & Video Technology,
1st Edition” p. 114, Elsevier Ltd, 2007
[7]
Section 2.1.2, Clint Smith, “Wireless Telecom FAQs” p.306, McGraw-Hill 2001
[8]
Section 2.1.3, Markku Renfors, Department of Communications Engineering,
Tampere University of Technology
[9]
Section 2.1.3, Charan Langton, Complex2Real.com,
http://complextoreal.com/chapters/isi.pdf, p. 1
[10] Section 2.1.3, Markku Renfors, Department of Communications Engineering,
Tampere University of Technology
[11] Section 2.1.4, A/Prof. Vaughan Clarkson, School of Information Technology &
Electrical Engineering, The University of Queensland Australia
[12] Section 2.1.5, Prof Timo O. Korhonen, HUT Communications Laboratory, Helsinki
University of Technology - TKK
[13] Section 2.1.5, Prof Thanassis Manikas, Department of Electrical and Electronic
Engineering, Imperial College London
[14] Section 2.1.5, Prof Thanassis Manikas, Department of Electrical and Electronic
Engineering, Imperial College London
[15] Section 2.1.6, Charan Langton, Complex2Real.com,
http://complextoreal.com/chapters/filters.pdf, p. 20
[16] Section 2.1.6, Kevin Banovic, Department of Electrical and Computer Engineering,
University of Windsor
133
[17] Section 2.1.6, Recursive least squares filter, Wikipedia, the free encyclopedia,
http://en.wikipedia.org/wiki/Recursive_least_squares_filter
[18] Section 2.1.6, Least mean squares filter, Wikipedia, the free encyclopedia,
http://en.wikipedia.org/wiki/Least_mean_squares_filter
[19] Section 2.1.6, Dr Andrea Goldsmith, “Wireless Communications” p. 337, Cambridge
University Press 2005
[20] Section 2.1.7, The Mathworks, Inc. MATLAB and Simulink for Technical Computing,
http://www.mathworks.com
[21] Section 2.1.7, The Mathworks, Inc. MATLAB® Builder™ JA 2 User’s Guide p. 1
[22] Section 2.1.8, The Apache Software Foundation, Apache Tomcat,
http://tomcat.apache.org
[23] Section 2.1.9, David Flanagan, “JavaScript, The Definitive Guide, 5th Edition” p. 1
O’Reilly Media, Inc. 2006
134
BIBLIOGRAPHY
David Flanagan, “JavaScript, The Definitive Guide, 5th Edition” O’Reilly Media, Inc. 2006
Clifford F. Grary, Erik W. Larson, “Project Management, The Managerial Process, 3rd
Edition” McGraw-Hill International 2006
UniSIM, “TZS 305 Digital Communications”
Brian D. Hahn, Daniel T. Valentine, “Essential MATLAB, For Engineers and Scientists,
Reprinted” Butterworth-Heinemann 2008
135
GLOSSARY
A/D
Analog to Digital
ASK
Amplitude shift keying
AWGN
Additive White Gaussian Noise
BASK
Binary Amplitude Shift Keying
BER
Bit error rate
BFSK
Binary Frequency Shift Keying
BPSK
Binary Phase Shift Keying
Eb
Energy per bit
Es
Energy per symbol
FIR
Finite impulse response filter
FSK
Frequency shift keying
GUI
Graphical User Interface
Html
Hypertext Markup Language
I-Channel
Imaginary Channel
IDE
Integrated Development Environment
ISI
Inter-Symbol Interference
J2EE
Java 2 Platform Enterprise Edition
Jar
Java Archive
JDK
Java Development Kit
JSP
Java Server Pages
LMS
Least Mean Square
MLSE
Maximum-Likelihood Sequence Estimation
N0
Noise Power Spectral Density
NRZ
Non-Return-To-Zero
OOK
On-Off Keying
PAM
Pulse Amplitude Modulation
PSK
Phase Shift Keying
D/A
Digital to Analog
QAM
Quadrature Amplitude Modulation
Q-Channel
Quadrature Channel
QPSK
Quadrature Phase Shift Keying
RLS
Recursive Least Square
136
RZ
Return-To-Zero
SER
Symbol Error Rate
SNR
Signal-To-Noise-Ratio