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

Learning Outcomes

Upon successfully completing this course, students will be equipped to tackle a wide range of contemporary research challenges in communications engineering. Specifically, they should have developed the following core competencies:

• Translate and manipulate complex mathematical expressions commonly found in communications engineering literature.
• Leverage MATLAB’s programming features to replicate simulation results from published studies or approach these benchmarks with high fidelity.
• Develop simulation models for novel, self-proposed concepts.
• Apply acquired simulation skills in tandem with MATLAB’s power to design optimized code that balances execution time and memory usage effectively.
• Identify critical simulation parameters within a communication system, extract them from the system model, and analyze their influence on overall system performance.

Course Structure

The content presented in this course is highly interconnected. It is strongly advised that students proceed sequentially through the levels, ensuring a deep understanding of each stage before advancing to the next. This approach guarantees the continuity and consolidation of knowledge. The course is organized into three progressive levels, evolving from foundational MATLAB programming to full-scale system simulation as detailed below.

Communications Mathematics with MATLAB
Sessions 01-06

By the end of this section, students will be capable of evaluating complex mathematical expressions and generating appropriate visualizations for various data representations, including time and frequency domain plots, BER curves, and antenna radiation patterns, among others.

Fundamental concepts

• The theoretical 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
• 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 matrix
• 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, 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 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, 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, 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 principal objectives of this section include the following:

• Generating random test signals essential 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 ends.
• Interconnecting these functional blocks effectively to achieve comprehensive communications functions.
• Simulating deterministic, statistical, and semi-random indoor and outdoor narrowband channel models.

Generation of communications test signals

• Generation of a random binary sequence
• Generation of a random integer sequence
• Importing and reading text files
• Reading and playback of audio files
• Importing and exporting images
• Image as a 3D matrix
• RGB to gray scale transformation
• Serial bit stream of a 2D gray scale 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 the signal energy
• Energy and power normalization
• Energy and power scaling
• Serial-to-parallel and parallel-to-serial conversion
• Multiplexing and de-multiplexing

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 the adequate pulse width
• Selection of the number of samples per pulse
• Convolution using the conv and filter commands
• The autocorrelation and cross-correlation of time limited signals
• The Fast Fourier Transform (FFT) and IFFT operations
• Viewing a baseband signal spectrum
• Effect of sampling rate and the proper frequency window
• Relation between the convolution, correlation, and the FFT operations
• Frequency domain filtering, low pass filtering only

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 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

• 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 arbitrary 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

• Channel characterization by its power delay profile
• Power normalization of the PDP
• Extracting the channel impulse response from the PDP
• Sampling the channel impulse response by an arbitrary sampling rate, mismatched sampling and delay
• quantization
• The problem of mismatched sampling of the channel impulse response of narrow band channels
• Sampling a PDP by 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 of the course addresses a critical concern for research students: how to replicate the simulation results of other published papers through simulation.

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 different 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 the BER performance
  • Impact of the code length on the 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 the BER performance
  • Impact of the cyclic prefix on the BER performance
  • Impact of the FFT size and subcarrier spacing on the 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 streamline and organize the overall simulation workflow. Additionally, it addresses memory space and processing speed to prevent memory overflow issues in systems with limited storage or prolonged execution times due to slow processing.

• Typical Structure of a small scale simulation projects
• Extraction of simulation parameters and theoretical to simulation mapping
• 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 the 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

Achieving a MATLAB code that is free from bugs and produces accurate results is a significant milestone. However, since a set of key parameters within a simulation project controls its behavior, an additional lecture on "Graphical User Interface (GUI) Programming" is included. This allows for direct control over various aspects of the simulation project, eliminating the need to navigate through lengthy source code. Furthermore, encapsulating MATLAB code within a GUI facilitates the presentation of work, enabling the combination of multiple results in a single master window and simplifying data comparison.

• What is a MATLAB GUI
• Structure of MATLAB GUI function file
• 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 above. Moreover, the specific items in each lecture may be adjusted to align with the needs of the learners and their research interests.