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Adaptive Signal Processing for Wireless Communication: Theory, Algorithms and MATLAB Applications
Paperback
Publisher: Independently Published
Published: Nov 6 2026
Pages: 216
Weight: 0.84
Height: 0.46 Width: 7.00 Depth: 10.00
Language: English
Wireless communication systems face persistent challenges from noise, interference, and channel distortions that can degrade signal quality and transmission reliability. Adaptive Signal Processing for Wireless Communication: Theory, Algorithms and MATLAB Applications provides a structured introduction to the principles, algorithms, and practical applications used to address these challenges.
The book begins with the history and fundamentals of adaptive signal processing, communication channels, noise characteristics, and digital signal processing. It introduces essential concepts such as sampling theory, Fourier analysis, Z-transforms, digital filters, and frequency response, building the foundation needed to understand adaptive filtering systems.
A major focus is placed on adaptive filter architectures and algorithms, including the Wiener filter, Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS), and Recursive Least Squares (RLS). Readers explore their mathematical foundations, convergence behaviour, computational complexity, advantages, limitations, and performance characteristics.
The book also presents practical MATLAB and Simulink applications, including signal generation, amplitude modulation, additive white Gaussian noise (AWGN) channel modelling, band-pass filtering, automatic gain control, envelope detection, and adaptive noise reduction. An AM radio receiver serves as a practical example for examining signal recovery and evaluating adaptive filtering performance.
Performance evaluation is explored through Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), convergence analysis, filter-order selection, step-size optimization, and comparisons between conventional and adaptive filtering techniques. The later chapters discuss research applications, possible hardware implementation, Software Defined Radio (SDR), real-time processing, intelligent adaptive filtering, and future research directions.
Key topics covered include:
Fundamentals of adaptive signal processing and wireless communication
Digital signal processing, sampling, Fourier analysis, and digital filters
Adaptive filter structures and design considerations
Wiener, LMS, NLMS, and RLS algorithms
MATLAB and Simulink modelling and simulation
Noise reduction and AM radio receiver applications
SNR, MSE, convergence, and computational performance analysis
Software Defined Radio, real-time processing, and future developments
Designed for undergraduate and postgraduate students, researchers, and communication engineering professionals, this book offers a practical learning resource for understanding adaptive algorithms and exploring their implementation in wireless communication systems. It connects theoretical concepts with MATLAB/Simulink-based modelling and performance analysis to support coursework, laboratory exercises, and project-based learning.
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