CHANNEL EQUALIZATION AND BLIND DECONVOLUTION Introduction Blind-Deconvolution Using Channel Input Power Spectrum Equalization Based on Linear Prediction Models Bayesian Blind Deconvolution and Equalization Blind Equalization for Digital Communication Channels Equalization Based on Higher-Order Statistics Summary lind deconvolution is the process of unravelling two unknown signals that have been convolved. An important application of blind deconvolution is in blind equalization for restoration of a signal distorted in transmission through a communication channel. Blind equalization has a wide range of applications, for example in digital telecommunications for removal of intersymbol interference, in speech recognition for removal of. | Advanced Digital Signal Processing and Noise Reduction Second Edition. Saeed V. Vaseghi Copyright 2000 John Wiley Sons Ltd ISBNs 0-471-62692-9 Hardback 0-470-84162-1 Electronic 15 CHANNEL EQUALIZATION AND BLIND DECONVOLUTION Introduction Blind-Deconvolution Using Channel Input Power Spectrum Equalization Based on Linear Prediction Models Bayesian Blind Deconvolution and Equalization Blind Equalization for Digital Communication Channels Equalization Based on Higher-Order Statistics Summary Blind deconvolution is the process of unravelling two unknown signals that have been convolved. An important application of blind deconvolution is in blind equalization for restoration of a signal distorted in transmission through a communication channel. Blind equalization has a wide range of applications for example in digital telecommunications for removal of intersymbol interference in speech recognition for removal of the effects of microphones and channels in deblurring of distorted images in dereverberation of acoustic recordings in seismic data analysis etc. In practice blind equalization is only feasible if some useful statistics of the channel input and perhaps also of the channel itself are available. The success of a blind equalization method depends on how much is known about the statistics of the channel input and how useful this knowledge is in the channel identification and equalization process. This chapter begins with an introduction to the basic ideas of deconvolution and channel equalization. We study blind equalization based on the channel input power spectrum equalization through separation of the input signal and channel response models Bayesian equalization nonlinear adaptive equalization for digital communication channels and equalization of maximum-phase channels using higher-order statistics. Introduction 417 Introduction In this chapter we consider the recovery of a signal distorted in transmission through a .
