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

Outcomes of this course

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

• Map and manipulate the complex mathematical expressions frequently encountered in communications engineering literature
• Leverage MATLAB’s programming capabilities to reproduce simulation results from other papers or closely approximate them
• Develop simulation models for self-proposed ideas
• Efficiently apply acquired simulation skills in conjunction with MATLAB’s powerful features to design optimized code, focusing on reducing run time while economizing on memory space
• Identify key simulation parameters for a given communication system, extract them from the system model, and analyze their impact on overall system performance

Course Structure

The material presented in this course is highly interconnected. It is not recommended for students to skip a level unless they have attended and deeply understood its preceding level, ensuring the continuity of acquired knowledge. The course is structured into three progressive levels, starting from an introduction to MATLAB programming and advancing to complete system simulation, as detailed below.

Communications Mathematics with MATLAB
Sessions 01-06

After completing this section, students will be able to evaluate complicated mathematical expressions and effortlessly construct 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 importance 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
• Work space
• 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)
• Locating 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 matrix 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 Jo (.)
• The Gamma function
• Diff and mod commands

Polynomials in MATLAB

• 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 – 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, conditions, 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 main objectives of this section include the following:

• Generate random test signals necessary for evaluating the performance of various communication systems
• Integrate multiple elementary signal operations to implement single communication processing functions, such as encoders, randomizers, interleavers, and spreading code generators, at both the transmitter and receiving terminal
• Interconnect these blocks correctly to achieve a complete communications function
• Simulate deterministic, statistical, and semi-random indoor and outdoor narrowband channel models

Generation of communications test signals

• Generating a random binary sequence
• Generating random integer sequences
• Importing and reading text files
• Reading and playing back audio files
• Importing and exporting images
• Images as 3D matrices
• 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, rarefaction)
• Time shift (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
• Calculating the adequate pulse width
• Selecting the number of samples per pulse
• Convolution using the conv and filter commands
• Autocorrelation and cross-correlation of time-limited signals
• Fast Fourier Transform (FFT) and IFFT operations
• Viewing a baseband signal spectrum
• Effect of sampling rate and the 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 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 a uniformly distributed RV
• Generation of a real-valued Gaussian distributed RV
• Generation of a complex Gaussian distributed RV
• Generation of a Rayleigh distributed RV
• Generation of a Ricean distributed RV
• Generation of a Lognormally distributed RV
• Generation of an arbitrarily distributed RV
• Approximation of an unknown probability density function (PDF) of an RV by 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 the channel by its power delay profile
• Power normalization of the PDP
• Extracting the channel impulse response from the PDP
• Sampling the channel impulse response with an arbitrary sampling rate, including mismatched sampling and delay
• Quantization
• Addressing the problem of mismatched sampling of the channel impulse response in narrowband channels
• Sampling a PDP with an arbitrary sampling rate and 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 Comm. Systems
Sessions 15-24

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

Bit Error Rate Performance of Baseband Digital Modulation Schemes

• 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
• 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)
• 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 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 (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 a MATLAB simulation project to simplify and organize the overall process. Additionally, memory space and processing speed are considered to avoid memory overflow issues in limited storage systems or prolonged run times caused by slow processing.

• Typical structure of small-scale simulation projects
• Extraction of simulation parameters and mapping theoretical values to simulation inputs
• 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
  • Calculating the adequate pulse width for truncated arbitrary pulse shapes
  • Calculating the adequate number of samples per symbol
  • Determining the necessary and sufficient number of bits to test a system

GUI programming

Having a MATLAB code that is free from debugs and produces correct results is a significant achievement. However, key parameters within a simulation project often require flexible control. For this reason and others, an additional lecture on "Graphical User Interface (GUI) Programming" is included to place control over various parts of the simulation project at your fingertips, rather than diving into long source codes filled with commands. Furthermore, wrapping MATLAB code in a GUI aids in presenting work in a way that facilitates combining multiple results in one master window and simplifies data comparison.

• 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 herein. Moreover, the items in each particular lecture are subject to change depending on the needs of the learners and their research interests.