Unsupervised Signal Processing: Channel Equalization and Source Separation

Unsupervised Signal Processing: Channel Equalization and Source Separation provides a unified, systematic, and synthetic presentation of the theory of unsupervised signal processing. Always maintaining the focus on a signal processing-oriented approach, this book describes how the subject has evolved and assumed a wider scope that covers several topics, from well-established blind equalization and source separation methods to novel approaches based on machine learning and bio-inspired algorithms.
From the foundations of statistical and adaptive signal processing, the authors explore and elaborate on emerging tools, such as machine learning-based solutions and bio-inspired methods. With a fresh take on this exciting area of study, this book:


Provides a solid background on the statistical characterization of signals and systems and on linear filtering theory
Emphasizes the link between supervised and unsupervised processing from the perspective of linear prediction and constrained filtering theory
Addresses key issues concerning equilibrium solutions and equivalence relationships in the context of unsupervised equalization criteria
Provides a systematic presentation of source separation and independent component analysis
Discusses some instigating connections between the filtering problem and computational intelligence approaches.

Building on more than a decade of the authors’ work at DSPCom laboratory, this book applies a fresh conceptual treatment and mathematical formalism to important existing topics. The result is perhaps the first unified presentation of unsupervised signal processing techniques—one that addresses areas including digital filters, adaptive methods, and statistical signal processing. With its remarkable synthesis of the field, this book provides a new vision to stimulate progress and contribute to the advent of more useful, efficient, and friendly intelligent systems.

João Marcos Travassos Romano is a professor at the University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil. He received his BS and MS in electrical engineering from UNICAMP in 1981 and 1984, respectively. In 1987, he received his Ph.D from the University of Paris–XI, Orsay. He has been an invited professor at CNAM, Paris; at University of Paris–Descartes; and at ENS, Cachan. He is the coordinator of the DSPCom Laboratory at UNICAMP, and his research interests include adaptive filtering, unsupervised signal processing, and applications in communication systems.

Romis Ribeiro de Faissol Attux is an assistant professor at the University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil. He received his BS, MS, and Ph.D in electrical engineering from UNICAMP in 1999, 2001, and 2005, respectively. He is a researcher in the DSPCom Laboratory. His research interests include blind signal processing, independent component analysis (ICA), nonlinear adaptive filtering, information-theoretic learning, neural networks, bio-inspired computing, dynamical systems, and chaos.

Charles Casimiro Cavalcante is an assistant professor at the Federal University of Ceará (UFC), Fortaleza, Ceara, Brazil. He received his BSc and MSc in electrical engineering from UFC in 1999 and 2001, respectively, and his Ph.D from the University of Campinas, Campinas, Sao Paulo, Brazil, in 2004. He is a researcher in the Wireless Telecommunications Research Group (GTEL), where he leads research on signal processing for communications, blind source separation, wireless communications, and statistical signal processing.

Ricardo Suyama is an assistant professor at the Federal University of ABC (UFABC), Santo Andre, Sao Paulo, Brazil. He received his BS, MS, and Ph.D in electrical engineering from the University of Campinas, Campinas, Sao Paulo, Brazil in 2001, 2003, and 2007, respectively. He is a researcher in the DSPCom Laboratory at UNICAMP. His research interests include adaptive filtering, source separation, and applications in communication systems.

Introduction
Channel Equalization
Source Separation
Organization and Contents

Statistical Characterization of Signals and Systems
Signals and Systems
Digital Signal Processing
Probability Theory and Randomness
Stochastic Processes
Estimation Theory

Linear Optimal and Adaptive Filtering
Supervised Linear Filtering
Wiener Filtering
The Steepest-Descent Algorithm
The Least Mean Square Algorithm
The Method of Least Squares
A Few Remarks Concerning Structural Extensions
Linear Filtering without a Reference Signal
Linear Prediction Revisited

Unsupervised Channel Equalization
The Unsupervised Deconvolution Problem
Fundamental Theorems
Bussgang Algorithms
The Shalvi–Weinstein Algorithm
The Super-Exponential Algorithm
Analysis of the Equilibrium Solutions of Unsupervised Criteria
Relationships between Equalization Criteria

Unsupervised Multichannel Equalization
Systems withMultiple Inputs and/orMultiple Outputs
SIMO Channel Equalization
Methods for Blind SIMO Equalization
MIMO Channels and Multiuser Processing

Blind Source Separation
The Problem of Blind Source Separation
Independent Component Analysis
Algorithms for Independent Component Analysis
Other Approaches for Blind Source Separation
Convolutive Mixtures
Nonlinear Mixtures

Nonlinear Filtering and Machine Learning
Decision-Feedback Equalizers
Volterra Filters
Equalization as a Classification Task
Artificial Neural Network

Bio-Inspired Optimization Methods
Why Bio-Inspired Computing?
Genetic Algorithms
Artificial Immune Systems
Particle Swarm Optimization

Appendix A: Some Properties of the Correlation Matrix
Appendix B: Kalman Filter

References

Index