Modern Coding Theory / Edition 1

Modern Coding Theory / Edition 1

by Tom Richardson, Ruediger Urbanke
     
 

ISBN-10: 0521852293

ISBN-13: 9780521852296

Pub. Date: 03/17/2008

Publisher: Cambridge University Press

Having trouble deciding which coding scheme to employ, how to design a new scheme, or how to improve an existing system? This summary of the state-of-the-art in iterative coding makes this decision more straightforward. With emphasis on the underlying theory, techniques to analyse and design practical iterative coding systems are presented. Using Gallager's original

Overview

Having trouble deciding which coding scheme to employ, how to design a new scheme, or how to improve an existing system? This summary of the state-of-the-art in iterative coding makes this decision more straightforward. With emphasis on the underlying theory, techniques to analyse and design practical iterative coding systems are presented. Using Gallager's original ensemble of LDPC codes, the basic concepts are extended for several general codes, including the practically important class of turbo codes. The simplicity of the binary erasure channel is exploited to develop analytical techniques and intuition, which are then applied to general channel models. A chapter on factor graphs helps to unify the important topics of information theory, coding and communication theory. Covering the most recent advances, this text is ideal for graduate students in electrical engineering and computer science, and practitioners. Additional resources, including instructor's solutions and figures, available online: www.cambridge.org/9780521852296.

Product Details

ISBN-13:
9780521852296
Publisher:
Cambridge University Press
Publication date:
03/17/2008
Edition description:
New Edition
Pages:
592
Product dimensions:
6.85(w) x 9.72(h) x 1.26(d)

Table of Contents

Preface; 1. Introduction; 2. Factor graphs; 3. Binary erasure channel; 4. Binary memoryless symmetric channels; 5. General channels ; 6. Convolutional codes and turbo codes; 7. General ensembles; 8. Expander codes and the flipping algorithm; Appendices: A. Encoding low-density parity-check codes; B. Efficient implementation of density evolution; C. Concentration inequalities; D. Formal power sums.

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