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

Course Outcomes

Upon completion of this course, students will be equipped to tackle current open research problems in communications engineering. Specifically, they will acquire the following core competencies:

• The ability to map and manipulate complex mathematical expressions commonly found in communications engineering literature.
• Proficiency in leveraging MATLAB’s programming features to replicate or closely approximate the simulation results found in existing research papers.
• The skill to develop simulation models for self-proposed ideas and concepts.
• The capability to design optimized MATLAB code that balances execution speed with efficient memory usage, utilizing the full power of MATLAB’s capabilities.
• The expertise to identify key simulation parameters within a given communication system, extract them from the system model, and evaluate their impact on overall system performance.

Course Structure

The content of this course is highly interdependent. It is strongly advised that students progress sequentially, ensuring a deep understanding of each level before advancing to the next, to maintain the continuity of knowledge. The course is divided into three levels, progressing from foundational MATLAB programming to complete system simulation, as detailed below.

Communications Mathematics with MATLAB
Sessions 01-06

Upon completing this section, students will be able to evaluate complex mathematical expressions and easily generate 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 representations of complex baseband signals in MATLAB

MATLAB Desktop Interface

• 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 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 (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 operations
•  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 manipulation 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 resources

MATLAB File Types

• MATLAB script files
• MATLAB function files
• MATLAB data files
• Local and global variables

Loops, 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:

• Generating random test signals necessary for evaluating the performance of various communication systems.
• Integrating multiple elementary signal operations to implement specific 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 a complete communication system.
• 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 audio files
• Importing and exporting images
• Representing images as 3D matrices
• RGB to grayscale transformation
• Serial bit stream representation of 2D grayscale images
• 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, circular shifts left and right)
• Measuring signal energy
• Energy and power normalization
• Energy and power scaling
• Serial-to-parallel and parallel-to-serial conversion
• Multiplexing and demultiplexing

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 Inverse FFT operations
• Viewing baseband signal spectra
• Effects of sampling rate and proper frequency window selection
• Relationships between convolution, correlation, and FFT operations
• Frequency-domain filtering (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 (RV)
• 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 (PDF) via histograms
• Numerical calculation of Cumulative Distribution Functions (CDF)
• Real and complex Additive White Gaussian Noise (AWGN) Channels

Channel Characterization by its Power Delay Profile

• Characterizing channels using their power delay profile (PDP)
• Power normalization of the PDP
• Extracting channel impulse response from the PDP
• Sampling channel impulse responses with arbitrary rates, mismatched sampling, and delay
• Quantization
• Addressing mismatched sampling issues in narrowband channel impulse responses
• Sampling a PDP with arbitrary rates and fractional delay compensation
• Implementing various IEEE standardized indoor and outdoor channel models
• (COST – SUI – Ultra-Wideband Channel Models, etc.)

Link Level Simulation of Practical Communication Systems
Sessions 15-24

This section addresses a critical challenge for researchers: how to accurately reproduce the simulation results of published papers.

Bit Error Rate Performance of Baseband Digital Modulation Schemes

•  Performance comparison of different baseband digital modulation schemes in AWGN channels (Comprehensive comparative simulation study 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 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 (DSSS) systems
• Pseudo Random Binary Sequence (PRBS) generators:
  • Generation of Maximal Length Sequences
  • Generation of Gold Codes
  • Generation of Walsh Codes
• Time-Hopping Spread Spectrum systems
• Bit Error Rate performance of spread-spectrum 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 systems in multipath slow Rayleigh fading channels with zero Doppler shift
• Bit error rate performance analysis of spread-spectrum systems in high-mobility fading environments
• Bit error rate performance analysis of spread-spectrum 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-Wideband Spread Spectrum 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 Carrier Frequency Offset (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 learning how to build and optimize MATLAB simulation projects to simplify and organize the overall simulation process. Additionally, it addresses memory space management and processing speed optimization to prevent memory overflow in limited storage systems or excessive run times caused by slow processing.

• Typical structure of small-scale simulation projects
• Extraction of simulation parameters and mapping theoretical models to simulations
• 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

Developing a MATLAB code that is free of bugs and produces correct results is a significant achievement. However, having key parameters controlled within a simulation project is crucial for flexibility. For this reason, and among others, an additional lecture on "Graphical User Interface (GUI) Programming" is included to place control over various simulation aspects directly at the user's fingertips, rather than requiring navigation through lengthy source code. Furthermore, wrapping MATLAB code in a GUI facilitates the presentation of work by allowing multiple results to be combined in a single master window, making data comparison easier.

• Introduction to MATLAB GUI
• Structure of MATLAB GUI function files
• 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 stated in each section. Additionally, the specific items in each lecture may be adjusted based on the needs of the learners and their research interests.