Simulation of Wireless Communication Systems using MATLAB (Dr Shehata) Training Course

• Course Outcomes
Upon completing this course, students will be equipped to tackle significant research challenges in communications engineering. You will have acquired the following key skills:

• The ability to map and manipulate complex mathematical expressions commonly found in communications engineering literature.

• Proficiency in using MATLAB's programming capabilities to reproduce simulation results from other research papers or closely approximate them.

• Competence in creating simulation models for self-proposed ideas.

• The ability to leverage simulation skills alongside MATLAB's powerful features to design optimized code that balances runtime efficiency with memory conservation.

• The capability to identify critical simulation parameters within communication systems, extract them from system models, and analyze their impact on system performance.

• Course Structure
The course material is highly interconnected. It is not advisable to progress to a subsequent level without thoroughly understanding the preceding one, to ensure continuity of knowledge. The course is divided into three levels, progressing from an introduction to MATLAB programming to complete system simulation:

Level 1: Communications Mathematics with MATLAB
Sessions 01-06

By the end of this section, students will be able to evaluate complex mathematical expressions and construct appropriate graphs for various data representations, such as time and frequency domain plots, Bit Error Rate (BER) plots, and antenna radiation patterns.

Fundamental Concepts

1. The concept of simulation
2. The importance of simulation in communications engineering
3. MATLAB as a simulation environment
4. Scalar signals in communications mathematics: matrix and vector representation
5. Complex baseband signals in MATLAB: matrix and vector representations

MATLAB Desktop Interface

6. Toolbar
7. Command Window
8. Workspace
9. Command History

Variable, Vector, and Matrix Declaration

10. MATLAB pre-defined constants
11. User-defined variables
12. Arrays, vectors, and matrices
13. Manual matrix entry
14. Interval definition
15. Linear space
16. Logarithmic space
17. Rules for variable naming

Special Matrices

18. The ones matrix
19. The zeros matrix
20. The identity matrix

Element-wise and Matrix-wise Manipulation

21. Accessing specific elements
22. Modifying elements
23. Selective elimination of elements (Matrix truncation)
24. Adding elements, vectors, or matrices (Matrix concatenation)
25. Finding the index of an element within a vector or matrix
26. Matrix reshaping
27. Matrix truncation
28. Matrix concatenation
29. Left-to-right and right-to-left flipping

Unary Matrix Operators

30. Sum operator
31. Expectation operator
32. Minimum operator
33. Maximum operator
34. Trace operator
35. Matrix determinant |.|
36. Matrix inverse
37. Matrix transpose
38. Matrix Hermitian
39. ...and others

Binary Matrix Operations

40. Arithmetic operations
41. Relational operations
42. Logical operations

Complex Numbers in MATLAB

43. Complex baseband representation of passband signals and RF up-conversion: a mathematical review
44. Forming complex variables, vectors, and matrices
45. Complex exponentials
46. Real part operator
47. Imaginary part operator
48. Conjugate operator (.*)
49. Absolute value operator |.|
50. Argument or phase operator

MATLAB Built-in Functions

51. Vectors of vectors and matrices of matrices
52. Square root function
53. Sign function
54. "Round to integer" function
55. "Nearest lower integer" function
56. "Nearest upper integer" function
57. Factorial function
58. Logarithmic functions (exp, ln, log10, log2)
59. Trigonometric functions
60. Hyperbolic functions
61. Q(.) function
62. erfc(.) function
63. Bessel functions Jo(.)
64. Gamma function
65. Diff and mod commands

Polynomials in MATLAB

66. Polynomials in MATLAB
67. Rational functions
68. Polynomial derivatives
69. Polynomial integration
70. Polynomial multiplication

Linear Scale Plots

71. Visual representation of continuous time-continuous amplitude signals
72. Visual representation of staircase-approximated signals
73. Visual representation of discrete time–discrete amplitude signals

Logarithmic Scale Plots

74. dB-decade plots (BER)
75. Decade-dB plots (Bode plots, frequency response, signal spectrum)
76. Decade-decade plots
77. dB-linear plots

2D Polar Plots

78. Planar antenna radiation patterns

3D Plots

79. 3D radiation patterns
80. Cartesian parametric plots

Optional Section (Provided based on learner demand)

81. Symbolic differentiation and numerical differencing in MATLAB
82. Symbolic and numerical integration in MATLAB
83. MATLAB help and documentation

MATLAB Files

84. MATLAB script files
85. MATLAB function files
86. MATLAB data files
87. Local and global variables

Loops, Conditional Flow Control, and Decision Making in MATLAB

88. The for-end loop
89. The while-end loop
90. The if-end condition
91. The if-else-end conditions
92. The switch-case-end statement
93. Iterations, converging errors, multi-dimensional sum operators

Input and Output Display Commands

94. The input(' ') command
95. disp command
96. fprintf command
97. Message box (msgbox)

Level 2: Signals and Systems Operations (24 hours)
Sessions 07-14

The main objectives of this section are:

• To generate random test signals necessary for evaluating the performance of various communication systems.

• To integrate elementary signal operations that can implement single communication processing functions, such as encoders, randomizers, interleavers, and spreading code generators at the transmitter, along with their counterparts at the receiver.

• To interconnect these blocks properly to achieve the desired communication function.

• To simulate deterministic, statistical, and semi-random indoor and outdoor narrowband channel models.

Generation of Communications Test Signals

98. Generation of random binary sequences
99. Generation of random integer sequences
100. Importing and reading text files
101. Reading and playback of audio files
102. Importing and exporting images
103. Images as 3D matrices
104. RGB to grayscale transformation
105. Serial bit stream of a 2D grayscale image
106. Sub-framing of image signals and reconstruction

Signal Conditioning and Manipulation

107. Amplitude scaling (gain, attenuation, amplitude normalization...)
108. DC level shifting
109. Time scaling (time compression, expansion)
110. Time shift (time delay, time advance, left and right circular time shift)
111. Measuring signal energy
112. Energy and power normalization
113. Energy and power scaling
114. Serial-to-parallel and parallel-to-serial conversion
115. Multiplexing and demultiplexing

Digitization of Analog Signals

116. Time domain sampling of continuous time baseband signals in MATLAB
117. Amplitude quantization of analog signals
118. PCM encoding of quantized analog signals
119. Decimal-to-binary and binary-to-decimal conversion
120. Pulse shaping
121. Calculation of adequate pulse width
122. Selection of the number of samples per pulse

123. Convolution using conv and filter commands
124. Autocorrelation and cross-correlation of time-limited signals
125. Fast Fourier Transform (FFT) and Inverse FFT (IFFT) operations
126. Viewing a baseband signal spectrum
127. Effect of sampling rate and proper frequency window
128. Relationship between convolution, correlation, and FFT operations
129. Frequency domain filtering (low-pass filtering only)

Auxiliary Communications Functions

130. Randomizers and de-randomizers
131. Puncturers and de-puncturers
132. Encoders and decoders
133. Interleavers and de-interleavers

Modulators and Demodulators

134. Digital baseband modulation schemes in MATLAB
135. Visual representation of digitally modulated signals

Channel Modelling and Simulation

136. Mathematical modeling of channel effects on transmitted signals
• Addition – Additive White Gaussian Noise (AWGN) channels
• Time domain multiplication – Slow fading channels, Doppler shift in vehicular channels
• Frequency domain multiplication – Frequency selective fading channels
• Time domain convolution – Channel impulse response

Examples of Deterministic Channel Models

137. Free space path loss and environment-dependent path loss
138. Periodic Blockage Channels

Statistical Characterization of Common Stationary and Quasi-Stationary Multipath Fading Channels

139. Generation of uniformly distributed Random Variables (RV)
140. Generation of real-valued Gaussian distributed RVs
141. Generation of complex Gaussian distributed RVs
142. Generation of Rayleigh distributed RVs
143. Generation of Ricean distributed RVs
144. Generation of Lognormally distributed RVs
145. Generation of arbitrarily distributed RVs
146. Approximation of an unknown Probability Density Function (PDF) of an RV using a histogram
147. Numerical calculation of the Cumulative Distribution Function (CDF) of an RV
148. Real and complex Additive White Gaussian Noise (AWGN) channels

Channel Characterization by its Power Delay Profile

149. Channel characterization by its power delay profile
150. Power normalization of the PDP
151. Extracting the channel impulse response from the PDP
152. Sampling the channel impulse response with an arbitrary sampling rate, mismatched sampling, and delay quantization
153. The problem of mismatched sampling of the channel impulse response in narrowband channels
154. Sampling a PDP with an arbitrary sampling rate and fractional delay compensation
155. Implementation of several IEEE-standardized indoor and outdoor channel models
156. (COST – SUI - Ultra Wide Band Channel Models...)

Level 3: Link Level Simulation of Practical Communication Systems (30 hours)
Sessions 15-24

This section addresses the critical issue for research students: how to reproduce simulation results from published papers.

Bit Error Rate Performance of Baseband Digital Modulation Schemes

1. Performance comparison of different baseband digital modulation schemes in AWGN channels (Comprehensive comparative study via simulation to verify theoretical expressions); scatter plots, bit error rate.

2. Performance comparison of different baseband digital modulation schemes in various stationary and quasi-stationary fading channels; scatter plots, bit error rate (Comprehensive comparative study via simulation to verify theoretical expressions).

3. Impact of Doppler shift channels on the performance of baseband digital modulation schemes; scatter plots, bit error rate.

Helicopter-to-Satellite Communications

4. Paper (1): Low-Cost Real-Time Voice and Data System for Aeronautical Mobile Satellite Service (AMSS) – Problem statement and analysis.
5. Paper (2): Pre-Detection Time Diversity Combining with Accurate AFC for Helicopter Satellite Communications – The first proposed solution.
6. Paper (3): An Adaptive Modulation Scheme for Helicopter-Satellite Communications – A performance improvement approach.

Simulation of Spread Spectrum Systems

1. Typical Architecture of spread spectrum-based systems.
2. Direct sequence spread spectrum-based systems.
3. Pseudo random binary sequence (PBRS) generators.
• Generation of Maximal length sequences.
• Generation of Gold codes.
• Generation of Walsh codes.

4. Time hopping spread spectrum-based systems.
5. Bit Error Rate Performance of spread spectrum-based systems in AWGN channels.
• Impact of coding rate r on BER performance.
• Impact of code length on BER performance.

6. Bit Error Rate Performance of spread spectrum-based systems in multipath Slow Rayleigh Fading Channels with Zero Doppler Shift.
7. Bit error rate performance analysis of spread spectrum-based systems in high mobility fading environments.
8. Bit error rate performance analysis of spread spectrum-based systems in the presence of multi-user interference.
9. RGB image transmission over spread spectrum systems.
10. Optical CDMA (OCDMA) systems.
• Optical orthogonal codes (OOC).
• Performance limits of OCDMA systems; bit error rate performance of synchronous and asynchronous OCDMA systems.

Ultra Wideband SS Systems

OFDM-Based Systems

11. Implementation of OFDM systems using the Fast Fourier Transform.
12. Typical Architecture of OFDM-based systems.
13. Bit Error Rate Performance of OFDM Systems in AWGN channels.
• Impact of coding rate r on BER performance.
• Impact of the cyclic prefix on BER performance.
• Impact of FFT size and subcarrier spacing on BER performance.

14. Bit Error Rate Performance of OFDM Systems in multipath Slow Rayleigh Fading Channels with Zero Doppler Shift.
15. Bit Error Rate Performance of OFDM Systems in multipath Slow Rayleigh Fading Channels with Carrier Frequency Offset (CFO).
16. Channel Estimation in OFDM Systems.
17. Frequency Domain Equalization in OFDM Systems.
• Zero Forcing Equalizer.
• MMSE Equalizers.
18. Other Common Performance Metrics in OFDM-Based Systems (Peak-to-Average Power Ratio, Carrier-to-Interference Ratio...)
19. Performance analysis of OFDM-based systems in high mobility fading environments (as a simulation project consisting of three papers).
20. Paper (1): Inter-carrier interference mitigation.
21. Paper (2): MIMO-OFDM Systems.

Optimization of a MATLAB Simulation Project

The aim of this section is to teach how to build and optimize a MATLAB simulation project to simplify and organize the overall simulation process. Memory space and processing speed are also considered to avoid memory overflow in limited storage systems or long run times due to slow processing.

1. Typical Structure of small-scale simulation projects.
2. Extraction of simulation parameters and theoretical to simulation mapping.
3. Building a Simulation Project.
4. Monte Carlo Simulation Technique.
5. A Typical Procedure for Testing a Simulation Project.
6. Memory Space Management and Simulation Time Reduction Techniques.
• Baseband vs. Passband Simulation.
• Calculation of adequate pulse width for truncated arbitrary pulse shapes.
• Calculation of the adequate number of samples per symbol.
• Calculation of the Necessary and Sufficient Number of Bits to Test a System.

GUI Programming

Having MATLAB code free from bugs and working correctly to produce accurate results is a significant achievement. However, a set of key parameters in a simulation project controls the workflow. For this reason and others, an extra lecture on "Graphical User Interface (GUI) Programming" is provided to give you direct control over various parts of your simulation project, rather than diving into long source codes full of commands. Additionally, masking your MATLAB code with a GUI helps present your work in a way that facilitates combining multiple results in one master window, making it easier to compare data.

1. What is a MATLAB GUI.
2. Structure of MATLAB GUI function files.
3. Main GUI components (important properties and values).
4. Local and global variables.

Note: The topics covered in each level of this course include, but are not limited to, those stated in each level. Furthermore, the items of each particular lecture are subject to change depending on the needs of the learners and their research interests.