Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications provides fundamental background knowledge of convex optimization, while striking a balance between mathematical theory and applications in signal processing and communications. In addition to comprehensive proofs and perspective interpretations for core convex optimization theory, this book also provides many insightful figures, remarks, illustrative examples, and guided journeys from theory to cutting-edge research explorations, for efficient and in-depth learning, especially for engineering students and professionals. With the powerful convex optimization theory and tools, this book provides you with a new degree of freedom and the capability of solving challenging real-world scientific and engineering problems.

Discrete-time signal processing has had a momentous impact on advances in engineering and science over recent decades. The rapid progress of digital and mixed-signal integrated circuits in processing speed, functionality and cost-effectiveness has led to their ubiquitous employment in signal processing and transmission in diverse milieux.

The absence of training or pilot signals from many kinds of transmission a" in, for example, speech analysis, seismic exploration and texture image analysis a" necessitates the widespread use of blind equalization and system identification. There have been a great many algorithms developed for these purposes, working with one- or two-dimensional (2-d) signals and with single-input single-output (SISO) or multiple-input multiple-output (MIMO), real or complex systems. It is now time for a unified treatment of this subject, pointing out the common characteristics and the sometimes close relations of these algorithms as well as learning from their different perspectives. Blind Equalization and System Identification provides such a unified treatment presenting theory, performance analysis, simulation, implementation and applications.

Topics covered include:

a [ SISO, MIMO and 2-d non-blind equalization (deconvolution) algorithms;

a [ SISO, MIMO and 2-d blind equalization (deconvolution) algorithms;

a [ SISO, MIMO and 2-d blind system identification algorithms;

a [ algorithm analyses and improvements;

a [ applications of SISO, MIMO and 2-d blind equalization/identification algorithms.

Each chapter is completed by exercises and computer assignments designed to further understanding and to give practical experiencewith the algorithms discussed.

This is a textbook for graduate-level courses in discrete-time random processes, statistical signal processing, and blind equalization and system identification. It contains material which will also interest researchers and practicing engineers working in digital communications, source separation, speech processing, image processing, seismic exploration, sonar, radar and other, similar applications.

Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications provides fundamental background knowledge of convex optimization, while striking a balance between mathematical theory and applications in signal processing and communications. In addition to comprehensive proofs and perspective interpretations for core convex optimization theory, this book also provides many insightful figures, remarks, illustrative examples, and guided journeys from theory to cutting-edge research explorations, for efficient and in-depth learning, especially for engineering students and professionals. With the powerful convex optimization theory and tools, this book provides you with a new degree of freedom and the capability of solving challenging real-world scientific and engineering problems.