Channel Equalization and Blind Deconvolution
Saeed V. Vaseghi · 2001
15.1 Introduction 15.2 Blind-deconvolution using channel input power spectrum 15.3 Equalization based on linear prediction models 15.4 Bayesian blind deconvolution and equalization 15.5 Blind equalization for digital communication channels 15.6 Equalization based on higher-order statistics 15.7 Summary Blind deconvolution is the 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.