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

Course Outcomes

Upon completing this course, students will be equipped to tackle many of the currently unresolved research problems in communications engineering. Specifically, they will have acquired at least the following capabilities:

• The ability to map and manipulate complex mathematical expressions frequently encountered in communications engineering literature.
• The capacity to leverage MATLAB’s programming features to replicate or closely approximate simulation results from existing literature.
• The skill to develop simulation models for self-proposed ideas.
• The competence to apply simulation skills efficiently, utilizing MATLAB’s power to design optimized code that balances run time and memory usage.
• The ability to identify key simulation parameters in a given communication system, extract them from the system model, and evaluate their impact on system performance.

Course Structure

The content of this course is highly interconnected. It is strongly advised that students progress sequentially through the levels, ensuring they deeply understand each prior level before moving on, to maintain continuity in their learning. The course is organized into three levels, progressing from an introduction to MATLAB programming 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 generate appropriate graphs for various data representations, including 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 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 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
• 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 (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, 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 are as follows:

• 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 receiver.
• Properly interconnect these blocks to achieve specific communication functions.
• Simulate deterministic, statistical, and semi-random indoor and outdoor narrowband channel models.

Generation of Communications Test Signals

• Generation of random binary sequences
• Generation of random integer sequences
• Importing and reading text files
• Reading and playing back audio files
• Importing and exporting images
• Image representation as a 3D matrix
• RGB to grayscale transformation
• Serial bit stream representation 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 (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 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
• Effects of sampling rate and appropriate frequency windowing
• Relationships 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 Modeling 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 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 Rician-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 via histograms
• Numerical calculation of the cumulative distribution function (CDF) of random variables
• Real and complex Additive White Gaussian Noise (AWGN) channels

Channel Characterization by Power Delay Profile

• Characterizing channels by their power delay profile
• Power normalization of the PDP
• Extracting channel impulse response from the PDP
• Sampling channel impulse response at arbitrary rates, including mismatched sampling and delay
• Quantization
• Addressing mismatched sampling issues in narrowband channels
• Sampling PDPs at arbitrary rates with fractional delay compensation
• Implementation of IEEE-standardized indoor and outdoor channel models
• Examples include COST, SUI, and Ultra-Wideband Channel Models

Link Level Simulation of Practical Communication Systems
Sessions 15-24

This section of the course addresses a critical concern for research students: how to reproduce the simulation results from published papers.

Bit Error Rate Performance of Baseband Digital Modulation Schemes

• Performance comparison of different baseband digital modulation schemes in AWGN channels, including a comprehensive comparative study to verify theoretical expressions, scatter plots, and bit error rates.
• Performance comparison of different baseband digital modulation schemes in stationary and quasi-stationary fading channels, including scatter plots and bit error rates, with a comprehensive comparative study to verify theoretical expressions.
• Impact of Doppler shift channels on baseband digital modulation scheme performance, including scatter plots and bit error rates.
• 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 (PRBS) 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, including BER performance of synchronous and asynchronous OCDMA systems

Ultra-Wideband 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 part is to learn how to construct and optimize a MATLAB simulation project to simplify and organize the overall process. Additionally, considerations for memory space and processing speed are included to prevent memory overflow in limited storage systems and to avoid long run times caused by slow processing.

• Typical structure of small-scale simulation projects.
• Extraction of simulation parameters and mapping from theoretical to simulation domains.
• 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 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

Developing a MATLAB code that is free of bugs and produces correct results is a significant achievement. However, key parameters in a simulation project require control. For this reason, and others, an additional lecture on "Graphical User Interface (GUI) Programming" is included. This allows for direct control over various aspects of the simulation project, rather than navigating extensive 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 a 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. Moreover, the specific items in each lecture are subject to change based on the needs of the learners and their research interests.