Издательство Prentice Hall, 1994, -289 pp.
System identification and channel equalization are important examples of deconvolution. Indeed, the literature devoted to the study of deconvolution in one form or another is very expensive, which testifies to the importance of the subject. In the usual formulation of deconvolution problem, it is assumed that the system input and output are both known. There are, however, many important physical situations where it is impractical to assume the availability of the system input. It is in situations of this kind that we speak of blind deconvolution. .
This book is the first of its kind to be devoted completely to the study of the blind deconvolution problem. It is made up of six chapters, written by leading researchers in the field. A variety of blind deconvolution/equalization algorithms are considered in the book, with computer simulation experiments included to support the theory. Extensive lists of references are included at the end of each chapter, making it possible for the reader to probe the literature more deeply.
The blind deconvolution problem.
Bussgang techniques for blind deconvolution and equalization.
Global convergence issues with linear blind adaptive equalizers.
Universal methods for blind deconvolutions.
Blind equalization based on higher-order statistics.
Joint data and channel estimation using blind trellis search techniques.
System identification and channel equalization are important examples of deconvolution. Indeed, the literature devoted to the study of deconvolution in one form or another is very expensive, which testifies to the importance of the subject. In the usual formulation of deconvolution problem, it is assumed that the system input and output are both known. There are, however, many important physical situations where it is impractical to assume the availability of the system input. It is in situations of this kind that we speak of blind deconvolution. .
This book is the first of its kind to be devoted completely to the study of the blind deconvolution problem. It is made up of six chapters, written by leading researchers in the field. A variety of blind deconvolution/equalization algorithms are considered in the book, with computer simulation experiments included to support the theory. Extensive lists of references are included at the end of each chapter, making it possible for the reader to probe the literature more deeply.
The blind deconvolution problem.
Bussgang techniques for blind deconvolution and equalization.
Global convergence issues with linear blind adaptive equalizers.
Universal methods for blind deconvolutions.
Blind equalization based on higher-order statistics.
Joint data and channel estimation using blind trellis search techniques.