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

Learning Outcomes

Upon completion of this course, students will be equipped to tackle several open research problems in communications engineering. They will have acquired the following essential skills:

• Mapping and manipulating complex mathematical expressions commonly found in communications engineering literature
• Utilising MATLAB's programming capabilities to replicate simulation results from other studies or to closely approximate them
• Developing simulation models for self-proposed ideas
• Efficiently applying simulation skills to design optimized MATLAB code that balances runtime efficiency with memory conservation
• Identifying key simulation parameters in a given communication system, extracting them from the system model, and analysing their impact on overall performance

Course Structure

The material in this course is highly interconnected. It is strongly advised that students complete each level sequentially, ensuring a deep understanding of prior content before progressing. This approach guarantees the continuity of knowledge. The course is divided into three levels, progressing from an introduction to MATLAB programming to complete system simulation, as outlined below.

Communications Mathematics with MATLAB
Sessions 01-06

By the end of this section, students will be able to evaluate complicated mathematical expressions and easily create appropriate graphs for various data representations, such as time and frequency domain plots, BER plots, and antenna radiation patterns.

Fundamental Concepts

• The concept of simulation
• The significance of simulation in communications engineering
• MATLAB as a simulation environment
• Matrix and vector representation of scalar signals in communications mathematics
• Matrix and vector representations of complex baseband signals in MATLAB

MATLAB Desktop

• Tool bar
• Command window
• Workspace
• Command history

Declaration of Variables, Vectors, and Matrices

• MATLAB pre-defined constants
• User-defined variables
• Arrays, vectors, and matrices
• Manual matrix entry
• Interval definition
• Linear spacing
• Logarithmic spacing
• Variable naming conventions

Special Matrices

• The ones matrix
• The zeros matrix
• The identity matrix

Element-wise and Matrix-wise Manipulations

• Accessing specific elements
• Modifying elements
• Selective elimination of elements (Matrix truncation)
• Adding elements, vectors, or matrices (Matrix concatenation)
• Finding the index of an element within a vector or matrix
• Reshaping matrices
• Matrix truncation
• Matrix concatenation
• Flipping from left to right and right to left

Unary Matrix Operators

• The Sum operator
• The expectation operator
• Min operator
• Max operator
• The trace operator
• Matrix determinant
• Matrix inverse
• Matrix transpose
• Matrix Hermitian

Binary Matrix Operations

• Arithmetic operations
• Relational operations
• Logical operations

Complex Numbers in MATLAB

• Complex baseband representation of passband signals and RF up-conversion (mathematical review)
• Creating complex variables, vectors, and matrices
• Complex exponentials
• The real part operator
• The imaginary part operator
• The conjugate operator
• The absolute value operator
• The argument or phase operator

MATLAB Built-in Functions

• Vectors of vectors and matrices of matrices
• The square root function
• The sign function
• The "round to integer" function
• The "nearest lower integer" function
• The "nearest upper integer" function
• The factorial function
• Logarithmic functions (exp, ln, log10, log2)
• Trigonometric functions
• Hyperbolic functions
• The Q-function
• The erfc function
• Bessel functions
• The Gamma function
• Diff and mod commands

Polynomials in MATLAB

• Handling polynomials in MATLAB
• Rational functions
• Polynomial derivatives
• Polynomial integration
• Polynomial multiplication

Linear Scale Plots

• Visual representations of continuous time-continuous amplitude signals
• Visual representations of stair-case approximated signals
• Visual representations of discrete time and discrete amplitude signals

Logarithmic Scale Plots

• dB-decade plots (BER)
• Decade-dB plots (Bode plots, frequency response, signal spectrum)
• Decade-decade plots
• dB-linear plots

2D Polar Plots

• Planar antenna radiation patterns

3D Plots

• 3D radiation patterns
• Cartesian parametric plots

Optional Section (Available upon Learner Request)

• Symbolic differentiation and numerical differencing in MATLAB
• Symbolic and numerical integration in MATLAB
• MATLAB help and documentation

MATLAB Files

• MATLAB script files
• MATLAB function files
• MATLAB data files
• Local and global variables

Loops, Flow Control, and Decision Making in MATLAB

• The for-end loop
• The while-end loop
• The if-end condition
• The if-else-end conditions
• The switch-case-end statement
• Iterations, converging errors, and multi-dimensional sum operators

Input and Output Display Commands

• The input() command
• The disp command
• The fprintf command
• Message box (msgbox)

Signals and Systems Operations
Sessions 07-14

The primary objectives of this section include the following:

• Generating random test signals necessary for evaluating the performance of various communication systems
• Integrating multiple elementary signal operations to implement single communication processing functions, such as encoders, randomizers, interleavers, and spreading code generators at both the transmitter and receiver
• Properly interconnecting these blocks to achieve a complete communications function
• Simulating deterministic, statistical, and semi-random indoor and outdoor narrowband channel models

Generation of Communications Test Signals

• Generating random binary sequences
• Generating random integer sequences
• Importing and reading text files
• Reading and playing back audio files
• Importing and exporting images
• Representing images as 3D matrices
• RGB to grayscale transformation
• Serial bit streams of 2D grayscale images
• Sub-framing of image signals and reconstruction

Signal Conditioning and Manipulation

• Amplitude scaling (gain, attenuation, amplitude normalization, etc.)
• DC level shifting
• Time scaling (time compression, expansion)
• Time shifting (delay, advance, left and right circular shift)
• Measuring signal energy
• Energy and power normalization
• Energy and power scaling
• Serial-to-parallel and parallel-to-serial conversion
• Multiplexing and demultiplexing

Digitization of Analog Signals

• Time domain sampling of continuous time baseband signals in MATLAB
• Amplitude quantization of analog signals
• PCM encoding of quantized analog signals
• Decimal-to-binary and binary-to-decimal conversion
• Pulse shaping
• Calculating adequate pulse width
• Selecting the number of samples per pulse
• Convolution using conv and filter commands
• Autocorrelation and cross-correlation of time-limited signals
• Fast Fourier Transform (FFT) and Inverse FFT (IFFT) operations
• Viewing a baseband signal spectrum
• Effects of sampling rate and proper frequency window
• Relationship between convolution, correlation, and FFT operations
• Frequency domain filtering, specifically low-pass filtering

Auxiliary Communications Functions

• Randomizers and de-randomizers
• Puncturers and de-puncturers
• Encoders and decoders
• Interleavers and de-interleavers

Modulators and Demodulators

• Digital baseband modulation schemes in MATLAB
• Visual representation of digitally modulated signals

Channel Modelling and Simulation

• Mathematical modeling of the channel's effect on the transmitted signal:
  • 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

• Free space path loss and environment-dependent path loss
• Periodic Blockage Channels

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

• Generating uniformly distributed random variables (RV)
• Generating real-valued Gaussian distributed RVs
• Generating complex Gaussian distributed RVs
• Generating Rayleigh distributed RVs
• Generating Ricean distributed RVs
• Generating Lognormally distributed RVs
• Generating arbitrary distributed RVs
• Approximating an unknown probability density function (PDF) of an RV using a histogram
• Numerical calculation of the cumulative distribution function (CDF) of an RV
• Real and complex Additive White Gaussian Noise (AWGN) Channels

Channel Characterization by its Power Delay Profile

• Characterizing channels via their power delay profile
• Power normalization of the PDP
• Extracting the channel impulse response from the PDP
• Sampling the channel impulse response at arbitrary rates, including mismatched sampling and delay
• Quantization
• Issues with mismatched sampling of narrowband channel impulse responses
• Sampling a PDP at arbitrary rates and fractional delay compensation
• Implementing several IEEE standardized indoor and outdoor channel models
• (COST, SUI, Ultra Wide Band Channel Models, etc.)

Link Level Simulation of Practical Communication Systems
Sessions 15-24

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

Bit Error Rate Performance of Baseband Digital Modulation Schemes

• Performance comparison of different baseband digital modulation schemes in AWGN channels (comprehensive comparative simulation study to verify theoretical expressions); scatter plots, bit error rate
• Performance comparison of different baseband digital modulation schemes in various stationary and quasi-stationary fading channels; scatter plots, bit error rate (comprehensive comparative simulation study to verify theoretical expressions)
• Impact of Doppler shift channels on the performance of baseband digital modulation schemes; scatter plots, bit error rate
• Helicopter-to-Satellite Communications:
  • Paper 1: Low-Cost Real-Time Voice and Data System for Aeronautical Mobile Satellite Service (AMSS) – Problem statement and analysis
  • Paper 2: Pre-Detection Time Diversity Combining with Accurate AFC for Helicopter Satellite Communications – The first proposed solution
  • Paper 3: An Adaptive Modulation Scheme for Helicopter-Satellite Communications – A performance improvement approach

Simulation of Spread Spectrum Systems

• Typical architecture of spread spectrum-based systems
• Direct sequence spread spectrum-based systems
• Pseudo random binary sequence (PRBS) generators:
  • Generation of Maximal length sequences
  • Generation of Gold codes
  • Generation of Walsh codes
• Time hopping spread spectrum-based systems
• 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
• Bit Error Rate Performance of spread spectrum-based systems in multipath Slow Rayleigh Fading Channels with Zero Doppler Shift
• Bit error rate performance analysis of spread spectrum-based systems in high-mobility fading environments
• Bit error rate performance analysis of spread spectrum-based systems in the presence of multi-user interference
• RGB image transmission over spread spectrum systems
• Optical CDMA (OCDMA) systems:
  • Optical orthogonal codes (OOC)
  • Performance limits of OCDMA systems; bit error rate performance of synchronous and asynchronous OCDMA systems

Ultra Wide Band SS Systems

OFDM-Based Systems

• Implementation of OFDM systems using the Fast Fourier Transform
• Typical architecture of OFDM-based systems
• 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
• Bit Error Rate Performance of OFDM Systems in multipath Slow Rayleigh Fading Channels with Zero Doppler Shift
• Bit Error Rate Performance of OFDM Systems in multipath Slow Rayleigh Fading Channels with CFO
• Channel Estimation in OFDM Systems
• Frequency Domain Equalization in OFDM Systems:
  • Zero Forcing Equalizer
  • MMSE Equalizers
• Other common performance metrics in OFDM-based systems (Peak-to-Average Power Ratio, Carrier-to-Interference Ratio, etc.)
• Performance analysis of OFDM-based systems in high-mobility fading environments (simulation project consisting of three papers):
  • Paper 1: Inter-carrier interference mitigation
  • Paper 2: MIMO-OFDM Systems

Optimization of a MATLAB Simulation Project

The aim of this section is to learn how to build and optimize a MATLAB simulation project to simplify and organize the overall simulation process. Additionally, memory space and processing speed are considered to avoid memory overflow issues in limited storage systems or excessive run times caused by slow processing.

• Typical structure of small-scale simulation projects
• Extraction of simulation parameters and mapping from theoretical to simulation
• Building a simulation project
• Monte Carlo Simulation Technique
• A typical procedure for testing a simulation project
• 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 a debug-free MATLAB code that works properly and produces correct results is a significant achievement. However, a set of key parameters in a simulation project controls its behavior. For this reason, and others, an extra lecture on "Graphical User Interface (GUI) Programming" is included to provide control over various parts of the simulation project without diving into long source codes. Moreover, encapsulating MATLAB code with a GUI facilitates presenting work by combining multiple results in one master window, making data comparison easier.

• What is a MATLAB GUI
• Structure of MATLAB GUI function files
• Main GUI components (important properties and values)
• Local and global variables

Note: The topics covered in each level of this course include, but are not limited to, those stated. Items in each particular lecture may be adjusted based on the learners' needs and research interests.