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

Learning Outcomes

Upon completing this course, students will be equipped to tackle many of the open research problems in communications engineering. They will have acquired at least the following competencies:

• Mapping and manipulating complex mathematical expressions commonly found in communications engineering literature
• Utilizing MATLAB’s programming features to replicate simulation results from other studies or approximate them effectively
• Developing simulation models for self-proposed ideas
• Applying simulation skills efficiently in conjunction with MATLAB’s powerful capabilities to design optimized code that balances execution time and memory usage
• Identifying key simulation parameters within a communication system, extracting them from the system model, and analyzing their impact on overall system performance

Course Structure

The course material is highly interrelated. It is recommended that students progress through each level only after thoroughly understanding the preceding one, ensuring a continuous buildup of knowledge. The curriculum is structured into three levels, starting from introductory MATLAB programming and advancing to complete system simulation:

Communications Mathematics with MATLAB
Sessions 01-06

By the end of this section, students will be able to evaluate complex mathematical expressions and construct appropriate graphs for various data representations, such as 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
• Representation of complex baseband signals using matrices and vectors 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 rules

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 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 (available upon 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, 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 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
• Interconnecting these functional blocks to achieve specific communication tasks
• 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 shift (time delay, time advance, 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
• 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 window selection
• Relationship between convolution, correlation, and 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 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 arbitrarily distributed random variables
• Approximating an unknown probability density function (PDF) of a random variable via 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 its 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 with arbitrary sampling rates, addressing mismatched sampling and delay
• Quantization
• Addressing the issue of mismatched sampling for narrowband channel impulse responses
• Sampling a PDP with arbitrary sampling rates and fractional delay compensation
• Implementing 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 concern for research students: how to reproduce simulation results from published papers.

Bit Error Rate Performance of Baseband Digital Modulation Schemes

•  Performance comparison of various baseband digital modulation schemes in AWGN channels (comprehensive simulation-based study to verify theoretical expressions); including scatter plots and BER
• Performance comparison of different baseband digital modulation schemes in stationary and quasi-stationary fading channels; scatter plots and BER (comprehensive simulation-based study to verify theoretical expressions)
• Impact of Doppler shift channels on the performance of baseband digital modulation schemes; scatter plots and BER
• 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
  
  • Generating maximal length sequences
  • Generating gold codes
  • Generating 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

• Implementing 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

This section focuses on learning how to build and optimize a MATLAB simulation project to simplify and organize the overall simulation process. It also addresses memory space and processing speed to prevent memory overflow in limited storage systems or long run times caused by slow processing.

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

GUI programming

Writing a MATLAB code free of bugs that correctly produces results is a significant achievement. However, key parameters in a simulation project control its behavior. For this reason, among others, an additional lecture on "Graphical User Interface (GUI) Programming" is included to provide control over various parts of the simulation project without navigating through lengthy source code. Furthermore, encapsulating MATLAB code within a GUI facilitates presenting work by combining multiple results in a master window, making data comparison easier.

• 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 listed. Additionally, specific lecture items may change based on learner needs and research interests.