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

Course Outcomes

Upon completion of this course, students will be equipped to tackle open research problems in communications engineering, having acquired at least the following competencies:

• Map and manipulate complex mathematical expressions commonly found in communications engineering literature.
• Utilize MATLAB's programming features to replicate simulation results from existing research or approximate them accurately.
• Develop simulation models for original, self-proposed ideas.
• Apply simulation skills efficiently alongside MATLAB's capabilities to design optimized code that balances runtime speed with memory efficiency.
• Identify key simulation parameters within a communication system, extract them from the model, and analyze their impact on overall system performance.

Course Structure

The content of this course is highly interconnected. It is recommended that students attend each level only after thoroughly understanding the previous one to ensure continuous knowledge development. The curriculum is divided into three levels, progressing from introductory MATLAB programming to complete system simulation, as detailed below.

Communications Mathematics with MATLAB
Sessions 01-06

By the end of this section, students will be able to evaluate complex mathematical expressions and create appropriate visualizations for various data representations, including time and frequency domain plots, BER curves, 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 representation of complex baseband signals in MATLAB

MATLAB Desktop

• Toolbar
• Command window
• Workspace
• Command history

Variable, Vector, and Matrix Declaration

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

Special Matrices

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

Element-wise and Matrix-wise Manipulation

• 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
• Matrix reshaping
• Matrix truncation
• Matrix concatenation
• Left-to-right and right-to-left flipping

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: a mathematical review
• Forming complex variables, vectors, and matrices
• Complex exponentials
• The real part operator
• The imaginary part operator
• The conjugate operator (.)*
• The absolute operator |.|
• The argument or phase operator

MATLAB Built-in Functions

• Vectors of vectors and matrices of matrices
• \u00a0The 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 Jo (.)
• The Gamma function
• Diff and mod commands

Polynomials in MATLAB

• Polynomial handling in MATLAB
• Rational functions
• Polynomial derivatives
• Polynomial integration
• Polynomial multiplication

Linear Scale Plots

• Visual representations of continuous time-continuous amplitude signals
• Visual representations of staircase approximated signals
• Visual representations of discrete time – 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 (Provided Based on Learner Demand)

• 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, Conditional 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
• disp command
• fprintf command
• Message box msgbox

Signals and Systems Operations
Sessions 07-14

The primary objectives of this section include:

• Generating random test signals necessary to evaluate 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 ends.
• Properly interconnecting these blocks to achieve specific communications functions
• Simulating deterministic, statistical, and semi-random indoor and outdoor narrowband channel models

Generation of Communications Test Signals

• Generation of random binary sequences
• Generation of random integer sequences
• Importing and reading text files
• Reading and playing back audio files
• Importing and exporting images
• Image representation as a 3D matrix
• RGB to grayscale transformation
• Serial bit stream of a 2D grayscale image
• 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 (time delay, time advance, left and right circular time 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
• Calculation of adequate pulse width
• Selection of 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 IFFT operations
• Viewing baseband signal spectra
• Effect of sampling rate and proper frequency windowing
• Relationship between convolution, correlation, and FFT operations
• Frequency domain filtering, specifically low-pass filtering

Auxiliary Communications Functions

• Randomizers and derandomizers
• \u00a0Puncturers and depuncturers
• Encoders and decoders
• Interleavers and deinterleavers

Modulators and Demodulators

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

Channel Modeling and Simulation

• 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

• Free space path loss and environment-dependent path loss
• Periodic blockage channels

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

• Generation of uniformly distributed random variables
• Generation of real-valued Gaussian distributed random variables
• Generation of complex Gaussian distributed random variables
• Generation of Rayleigh distributed random variables
• Generation of Ricean distributed random variables
• Generation of Lognormally distributed random variables
• Generation of arbitrarily distributed random variables
• Approximation of an unknown probability density function (PDF) of a random variable using a histogram
• Numerical calculation of the cumulative distribution function (CDF) of a random variable
• Real and complex additive white Gaussian noise (AWGN) channels

Channel Characterization by Power Delay Profile

• Channel characterization using the power delay profile
• Power normalization of the PDP
• Extracting the channel impulse response from the PDP
• Sampling the channel impulse response at arbitrary sampling rates, including mismatched sampling and delay
• Quantization
• Issues related to mismatched sampling of the channel impulse response in narrowband channels
• Sampling a PDP at arbitrary rates with fractional delay compensation
• Implementation of 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 addresses a critical aspect for research students: how to reproduce simulation results from published papers. It focuses on practical verification and analysis.

Bit Error Rate Performance of Baseband Digital Modulation Schemes

• \u00a0Performance comparison of different baseband digital modulation schemes in AWGN channels (comprehensive comparative simulation to verify theoretical expressions); scatter plots, bit error rate
• Performance comparison of different baseband digital modulation schemes in stationary and quasi-stationary fading channels; scatter plots, bit error rate (comprehensive comparative simulation 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 (PBRS) 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 Wideband 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 (as a simulation project consisting of three papers)
  
  • Paper (1): Inter-carrier interference mitigation
  • Paper (2): MIMO-OFDM Systems

Optimization of a MATLAB Simulation Project

The objective of this section is to learn how to construct and optimize MATLAB simulation projects to streamline and organize the simulation process. Additionally, attention is given to memory space and processing speed to prevent memory overflow issues in systems with limited storage or to reduce long execution times caused by slow processing.

• Typical structure of small-scale simulation projects
• Extraction of simulation parameters and mapping from theoretical models 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 adequate number of samples per symbol
  • Calculation of the necessary and sufficient number of bits to test a system

GUI Programming

While having a MATLAB code that is free of bugs and produces correct results is a significant achievement, it is often insufficient for user-friendly interaction. Since key parameters in a simulation project control its behavior, an additional lecture on "Graphical User Interface (GUI) Programming" is included. This enables users to control various aspects of the simulation project intuitively rather than navigating through extensive source code. Furthermore, masking the MATLAB code with a GUI facilitates the presentation of work, allowing for the combination of multiple results in a master window and easier data comparison.

• Introduction to 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 listed. Furthermore, the content of specific lectures may be adjusted based on learner needs and research interests.