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

Learning Outcomes

Upon completing this course, students will be equipped to tackle numerous open research problems in communications engineering. They will have acquired essential skills including, but not limited to, the following:

• Mapping and manipulating complex mathematical expressions frequently encountered in communications engineering literature
• Leveraging MATLAB's programming features to reproduce simulation results from existing papers or approach them closely
• Developing simulation models for independently proposed ideas
• Efficiently utilizing simulation skills alongside MATLAB's powerful capabilities to design optimized code that balances execution time and memory efficiency
• Identifying key simulation parameters in specific communication systems, extracting them from system models, and analyzing their impact on overall system performance

Course Structure

The course material is highly interconnected. To ensure continuity in knowledge acquisition, it is strongly advised that students progress sequentially through the levels, ensuring a deep understanding of each prior stage before advancing. The curriculum is divided into three levels, progressing from introductory MATLAB programming to complete system simulation as follows.

Communications Mathematics with MATLAB
Sessions 01-06

By the end of this section, students will be capable of evaluating complex mathematical expressions and constructing appropriate graphs for various data representations, such as time and frequency domain plots, BER plots, and antenna radiation patterns.

Foundational 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 Interface

• 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 Operations

• Accessing specific elements
• Modifying elements
• Selective element elimination (Matrix truncation)
• Adding elements, vectors, or matrices (Matrix concatenation)
• Locating 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: 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 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 Jo (.)
• The Gamma function
• Diff and mod commands

Polynomials in MATLAB

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

Linear Scale Plots

• Visualizing continuous time-continuous amplitude signals
• Visualizing stair case approximated signals
• Visualizing 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 (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

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 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 for evaluating the performance of various communication systems
• Integrating 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

• Generating random binary sequences
• 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 shifting (delay, 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 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 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 IFFT operations
• Viewing baseband signal spectra
• Effect of sampling rate and proper frequency window selection
• 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

• Generating uniformly distributed random variables
• Generating real-valued Gaussian distributed random variables
• Generating complex Gaussian distributed random variables
• Generating Rayleigh distributed random variables
• Generating Ricean distributed random variables
• Generating Lognormally distributed random variables
• Generating arbitrary distributed random variables
• Approximating unknown probability density functions (PDF) of random variables using histograms
• Numerical calculation of the cumulative distribution function (CDF) of a random variable
• Real and complex additive white Gaussian noise (AWGN) channels

Channel Characterization via Power Delay Profile

• Characterizing channels by their 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
• The challenge of mismatched sampling for narrowband channel impulse responses
• Sampling a PDP at arbitrary rates with fractional delay compensation
• Implementing 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 issue for research students: how to reproduce the simulation results of published papers.

Bit Error Rate Performance of Baseband Digital Modulation Schemes

• Performance comparison of various 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 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 baseband digital modulation scheme performance; 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 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 MATLAB Simulation Projects

This section focuses on building and optimizing MATLAB simulation projects to simplify and organize the overall process. It also addresses memory space and processing speed to prevent 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 theoretical to simulation mapping
• Building a simulation project
• Monte Carlo Simulation Technique
• Standard procedures for testing simulation projects
• Memory Space Management and Simulation Time Reduction Techniques
  
  • 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 to test a system

GUI Programming

Writing debug-free MATLAB code that produces correct results is a significant achievement. However, since key parameters in a simulation project often require adjustment, an additional lecture on "Graphical User Interface (GUI) Programming" is included. This allows users to control various aspects of their simulation project intuitively rather than navigating complex source code. Furthermore, masking MATLAB code with a GUI facilitates presenting work by combining multiple results in a master window and simplifies data comparison.

• Introduction to MATLAB GUIs
• Structure of MATLAB GUI function files
• Main GUI components (key properties and values)
• Local and global variables

Note: The topics covered in each level include, but are not limited to, those listed. The specific items in each lecture may vary based on learner needs and research interests.