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

Course Outcomes

Upon completing this course, students should be equipped to tackle current research challenges in communications engineering by acquiring the following core competencies:

• Mapping and manipulating complex mathematical expressions frequently found in communications engineering literature.
• Utilising MATLAB’s programming features to replicate simulation results from published papers or closely approximate them.
• Developing simulation models for original, self-proposed ideas.
• Applying simulation skills efficiently within MATLAB to design optimised code that minimises runtime while conserving memory space.
• Identifying critical simulation parameters for specific communication systems, extracting them from system models, and analysing their impact on overall system performance.

Course Structure

The material in this course is highly interconnected. It is not advisable to skip levels, as each builds upon the previous one to ensure a continuous acquisition of knowledge. The course is structured into three progressive levels, moving from introductory 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 importance 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
• Workspace
• 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)
• Finding 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 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, 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 staircase-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 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, 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, 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 necessary for assessing 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 blocks effectively to achieve specific communication 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 greyscale transformation
• Serial bit streams of 2D greyscale images
• Sub-framing of image signals and reconstruction

Signal Conditioning and Manipulation

• Amplitude scaling (gain, attenuation, amplitude normalisation, etc.)
• DC level shifting
• Time scaling (time compression, rarefaction)
• Time shift (time delay, time advance, left and right circular time shift)
• Measuring signal energy
• Energy and power normalisation
• 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 the 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 appropriate frequency windows
• 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 Modelling and Simulation

• Mathematical modelling 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 (RVs)
• Generating real-valued Gaussian distributed RVs
• Generating complex Gaussian distributed RVs
• Generating Rayleigh distributed RVs
• Generating Ricean distributed RVs
• Generating Lognormally distributed RVs
• Generating arbitrarily distributed RVs
• Approximating unknown probability density functions (PDFs) of RVs using histograms
• Numerical calculation of cumulative distribution functions (CDFs) for RVs
• Real and complex additive white Gaussian noise (AWGN) channels

Channel Characterization by its Power Delay Profile

• Characterising channels via their power delay profile
• Power normalisation 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 the issue of mismatched sampling for narrowband channel impulse responses
• Sampling a PDP at arbitrary rates with fractional delay compensation
• Implementing various IEEE-standardised indoor and outdoor channel models
• (e.g., COST – SUI - Ultra Wide Band Channel Models)

Link Level Simulation of Practical Comm. Systems
Sessions 15-24

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

Bit Error Rate Performance of Baseband Digital Modulation Schemes

• Comparing the performance of different baseband digital modulation schemes in AWGN channels (a comprehensive comparative study via simulation to verify theoretical expressions); includes scatter plots and BER analysis.
• Comparing the performance of different baseband digital modulation schemes in various stationary and quasi-stationary fading channels; includes scatter plots and BER analysis (a comprehensive comparative study via simulation to verify theoretical expressions).
• Assessing the impact of Doppler shift channels on the performance of baseband digital modulation schemes; includes scatter plots and BER analysis.
• 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; BER 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 (e.g., Peak-to-Average Power Ratio, Carrier-to-Interference Ratio).
• Performance analysis of OFDM-based systems in high-mobility fading environments (a simulation project comprising 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 optimise a MATLAB simulation project to simplify and organise the overall simulation process. It also considers memory space and processing speed to prevent memory overflow issues in systems with limited storage or to mitigate long run times caused by slow processing.

• Typical structure of small-scale simulation projects.
• Extracting simulation parameters and mapping theoretical values to simulation parameters.
• 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

Having a MATLAB code free from bugs that produces correct results is a significant achievement. However, a set of key parameters controls the simulation outcome. For this reason, and others, an additional lecture on "Graphical User Interface (GUI) Programming" is provided to place control over various parts of the simulation project at the user's fingertips, rather than requiring navigation through lengthy source code. Furthermore, masking MATLAB code with a GUI facilitates presenting work in a way that combines multiple results in a single master window, making data comparison easier.

• 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 at each level of this course include, but are not limited to, those listed. Additionally, the items in each particular lecture may change depending on the needs of the learners and their research interests.