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

Course Outcomes

Upon completing this course, students will be equipped to tackle current open research challenges in communications engineering. They will have 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 published papers or closely approach them
• Develop simulation models for original, self-proposed ideas
• Efficiently apply simulation skills and MATLAB's capabilities to design optimized code that minimizes execution time while conserving memory resources
• Identify key simulation parameters for a given communication system, extract them from the system model, and evaluate their impact on overall system performance

Course Structure

The content in this course is highly interconnected. To ensure a continuous build-up of knowledge, it is strongly recommended that students progress through each level only after mastering the preceding one. The course is organized into three levels, progressing from introductory MATLAB programming to complete system simulation:

Communications Mathematics with MATLAB
Sessions 01-06

Upon finishing this section, students will be capable of evaluating complex mathematical expressions and generating appropriate visualizations 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 representation of complex baseband signals in MATLAB

MATLAB Desktop

• Tool bar
• Command window
• Work space
• 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 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 element removal (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
• Creating 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 representation of continuous time and continuous amplitude signals
• Visual representation of stair-case approximated signals
• Visual representation 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, 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 required for assessing the performance of various communication systems
• Integrating basic signal operations to implement complex communication processing functions, such as encoders, randomizers, interleavers, and spreading code generators, at both transmitter and receiver ends
• Properly interconnecting these functional blocks to achieve specific communication tasks
• Simulating deterministic, statistical, and semi-random indoor and outdoor narrowband channel models

Generation of Communication Test Signals

• Generating random binary sequences
• Generating random integer sequences
• Importing and reading text files
• Reading and playing 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, expansion)
• Time shifting (delay, advance, left and right circular shifts)
• Measuring 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
• Calculating appropriate 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 IFFT operations
• Viewing baseband signal spectra
• Effects of sampling rate and appropriate frequency windowing
• Relationships between convolution, correlation, and FFT operations
• Frequency-domain filtering (low-pass filtering only)

Auxiliary Communication 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 Modeling and Simulation

• Mathematical modeling of channel effects on transmitted signals
  • Addition – Additive White Gaussian Noise (AWGN) channels
  • Time-domain multiplication – Slow fading channels and 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 unknown probability density functions (PDF) of random variables using histograms
• Numerical calculation of cumulative distribution functions (CDF) of random variables
• Real and complex Additive White Gaussian Noise (AWGN) channels

Channel Characterization by 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
• Addressing mismatched sampling issues in narrowband channel impulse responses
• Sampling a PDP at arbitrary rates with fractional delay compensation
• Implementing IEEE-standardized indoor and outdoor channel models
• Models such as COST, SUI, and Ultra-Wide Band Channel Models

Link Level Simulation of Practical Communication Systems
Sessions 15-24

This section addresses a critical challenge for research students: how to replicate simulation results from published papers through simulation.

Bit Error Rate Performance of Baseband Digital Modulation Schemes

• Performance comparison of various baseband digital modulation schemes in AWGN channels (Comprehensive comparative simulation study to verify theoretical expressions); scatter plots, bit error rate
• Performance comparison of various baseband digital modulation schemes in 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 (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 MATLAB Simulation Projects

The goal of this section is to demonstrate how to build and optimize MATLAB simulation projects to simplify and organize the overall process. It also considers memory space and processing speed to prevent memory overflow in systems with limited storage or prolonged execution times due to 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 techniques for reducing simulation time
  • Baseband vs. Passband Simulation
  • Calculating adequate pulse width for truncated arbitrary pulse shapes
  • Calculating the adequate number of samples per symbol
  • Determining the necessary and sufficient number of bits for system testing

GUI Programming

Creating a debug-free MATLAB code that produces correct results is a significant achievement. However, a set of key parameters governs the simulation. For this reason, and to simplify control, an additional lecture on "Graphical User Interface (GUI) Programming" is included. This allows users to manage various aspects of the simulation project with ease, rather than navigating long source code. Furthermore, a GUI facilitates presenting work by combining multiple results in a master window, making data comparison more straightforward.

• What is a MATLAB GUI
• Structure of MATLAB GUI function files
• Main GUI components (key 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. The specific items in each lecture may be adjusted based on learner needs and research interests.