Viswanathan Mathuranathan. Wireless Communication Systems in MatLAB

Second Edition. — Independently published, 2020. — 382 p. — ISBN-13 9798648350779.A learner-friendly, practical and example driven book, Wireless Communication Systems in MatLAB gives you a solid background in building simulation models for wireless systems in MatLAB. This book, an essential guide for understanding the basic implementation aspects of a wireless system, shows how to simulate and model such a system from scratch. The implemented simulation models shown in this book, provide an opportunity for an engineer to understand the basic implementation aspects of modeling various building blocks of a wireless communication system. It presents the following key topics with the required theoretical background, along with the implementation details in the form of MatLAB scripts.
Random variables for simulating probabilistic systems and applications like Jakes filter design and colored noise generation.
Models for Shannon's channel capacity, unconstrained awgn channel, binary symmetric channel (BSC), binary erasure channel (BEC), constellation constrained capacities and ergodic capacity over fading channel. The theory of linear block codes, decoding techniques using soft-decisions and hard-decisions, and their performance simulations.
Monte Carlo simulation for ascertaining performance of digital modulation techniques in AWGN and fading channels - Eb/N0 Vs BER curves. Pulse shaping techniques, matched filtering and partial response signaling, Design and implementation of linear equalizers - zero forcing and MMSE equalizers, using them in a communication link and modulation systems with receiver impairments.
Large-scale propagation models like Friis free space model, log distance model, two ray ground reflection model, single knife-edge diffraction model, Hata Okumura model.
Essentials of small-scale propagation models for wireless channels, such as, power delay profile, Doppler power spectrum, Rayleigh and Rice processes. Modeling flat fading and frequency selective channels.
Diversity techniques for multiple antenna systems: Alamouti space-time coding, maximum ratio combining, equal gain combining and selection combining.
Simulation models for direct sequence spread spectrum, frequency hopping spread spectrum and OFDM.Fundamental Concepts
Essentials of Signal Processing
Generating standard test signals
Sinusoidal signals
Square wave
Rectangular pulse
Gaussian pulse
Chirp signal
Interpreting FFT results – complex DFT, frequency bins and FFTShift
Real and complex DFT
Fast Fourier Transform (FFT)
Interpreting the FFT results
FFTShift
IFFTShift
Some observations on FFTShift and IFFTShift
Obtaining magnitude and phase information from FFT
Discrete-time domain representation
Representing the signal in frequency domain using FFT
Reconstructing the time domain signal from the frequency domain samples
Plotting Magnitude and Phase Spectrum
Power Spectral Density
Power and Energy of a signal
Energy of a signal
Power of a signal
Classification of signals
Computation of power of a signal – simulation and verification
Polynomials, Convolution and Toeplitz matrices
Polynomial functions
Representing single variable polynomial functions
Multiplication of polynomials and linear convolution
Toeplitz Matrix and Convolution
Methods to compute convolution
Method 1 – Brute-Force Method
Method 2 – Using Toeplitz Matrix
Method 3 – Using FFT to compute convolution
Miscellaneous methods
Analytic signal and its applications
Analytic signal and Fourier Transform
Applications of analytic signal
Choosing a filter : FIR or IIR : Understanding the design perspective
Design specification
General considerations in design
Random variables – simulating probabilistic systems
Introduction
Plotting the estimated PDF
Univariate random variables
Uniform random variable
Bernoulli random variable
Binomial random variable
Exponential random variable
Poisson process
Gaussian random variable
Chi-squared random variable
Non-central Chi-Squared random variable
Chi distributed random variable
Rayleigh random variable
Ricean random variable
Nakagami-m distributed random variable
Central limit theorem – a demonstration
Generating correlated random variables
Generating two sequences of correlated random variables
Generating multiple sequences of correlated random variables using Cholesky decomposition
Generating correlated Gaussian sequences
Spectral factorization method
Auto-Regressive (AR) model
Channel Capacity and Coding Theory
Channel Capacity
Introduction
Shannon’s noisy channel coding theorem
Unconstrained capacity for bandlimited AWGN channe
Shannon’s limit on spectral efficiency
Shannon’s limit on power efficiency
Generic capacity equation for Discrete Memoryless Channel (DMC)l
Capacity over Binary Symmetric Channel
Capacity over Binary Erasure Channel
Constrained Capacity of Discrete input Continuous output Memoryless AWGN Channel
Ergodic capacity over a fading channel
Linear Block Coding
Introduction to error control coding
Error Control Schemes
Channel Coding Metrics
Overview of block codes
Error-detection and error-correction capability
Decoders for block codes
Classification of block codes
Theory of Linear Block Codes
Optimum Soft-Decision Decoding of Linear Block Codes for AWGN channel
Sub-optimal Hard-Decision Decoding of Linear Block Codes for AWGN channel
Standard Array Decoder
Syndrome decoding
Some classes of linear block codes
Repetition codes
Hamming codes
Maximum-length codes
Hadamard codes
Performance Simulation of Soft and Hard Decision Decoding of Hamming Codes
Digital Modulations
Digital Modulators and Demodulators – complex baseband equivalent models
Passband and complex baseband equivalent model
Complex Baseband representation of modulated signal
Complex baseband representation of channel response
Modulators for Amplitude and Phase modulations
Pulse Amplitude Modulation (M-PAM)
Phase Shift Keying Modulation (M-PSK)
Quadrature Amplitude Modulation (M-QAM)
Demodulators for Amplitude and Phase modulations
M-PAM detection
M-PSK detection
M-QAM detection
Optimum Detector on IQ plane using minimum Euclidean distance
M-ary FSK modulation and detection
Modulator for M orthogonal signals
M-FSK detection
Performance of Digital Modulations overWireless Channels
AWGN channel
Signal to Noise Ratio (SNR) definitions
AWGN channel model
Theoretical Symbol Error Rates
Unified Simulation model for performance simulation
Fading channels
Linear Time Invariant channel model and FIR filters
Simulation model for detection in flat Fading Channel
Rayleigh flat-fading channel
Ricean flat-fading channel
Intersymbol interference and Equalizers
Pulse Shaping, Matched Filtering and Partial Response Signaling
Introduction
Nyquist Criterion for zero ISI
Discrete-time model for a system with pulse shaping and matched filtering
Rectangular pulse shaping
Sinc pulse shaping
Raised-cosine pulse shaping
Square-root raised-cosine pulse shaping
Eye Diagram
Implementing a Matched Filter system with SRRC filtering
Plotting the eye diagram
Performance simulation
Partial Response Signaling Models
Impulse response and frequency response of PR signaling schemes
Precoding
Implementing a modulo-M precoder
Simulation and results
Linear Equalizers
Introduction
Linear Equalizers
Zero-Forcing Symbol Spaced Linear Equalizer
Design and simulation of Zero Forcing equalizer
Drawbacks of Zero Forcing Equalizer
Minimum Mean Squared Error (MMSE) Equalizer
Design and simulation of MMSE equalizer
Equalizer Delay Optimization
BPSK Modulation with ZF and MMSE equalizers
Receiver Impairments and Compensation
Introduction
DC offsets and compensation
IQ imbalance model
IQ imbalance estimation and compensation
Blind estimation and compensation
Pilot based estimation and compensation
Visualizing the effect of receiver impairments
Performance of M-QAM modulation with receiver impairments