Download Channel Coding in Communication Networks: From Theory to by Alain Glavieux PDF

By Alain Glavieux

This publication offers a accomplished review of the topic of channel coding. It starts off with an outline of data thought, concentrating on the quantitative dimension of knowledge and introducing primary theorems on resource and channel coding. the fundamentals of channel coding in chapters, block codes and convolutional codes, are then mentioned, and for those the authors introduce weighted enter and output deciphering algorithms and recursive systematic convolutional codes, that are utilized in the remainder of the ebook.

Trellis coded modulations, that have their basic functions in excessive spectral potency transmissions, are then lined, ahead of the dialogue strikes directly to a complicated coding strategy referred to as turbocoding. those codes, invented within the Nineteen Nineties by way of C. Berrou and A. Glavieux, convey unheard of functionality. the variations among convolutional turbocodes and block turbocodes are defined, and for every relations, the authors current the coding and interpreting suggestions, including their performances. The publication concludes with a bankruptcy at the implementation of turbocodes in circuits.

As such, a person concerned about the parts of channel coding and blunder correcting coding will locate this ebook to be of valuable assistance.Content:
Chapter 1 info concept (pages 1–40): Gerard Battail
Chapter 2 Block Codes (pages 41–128): Alain Poli
Chapter three Convolutional Codes (pages 129–196): Alian Glavieux and Sandrine Vaton
Chapter four Coded Modulations (pages 197–253): Ezio Biglieri
Chapter five Turbocodes (pages 255–306): Claude Berrou, Catherine Douillard, Michel Jezequel and Annie Picart
Chapter 6 Block Turbocodes (pages 307–371): Ramesh Pyndiah and Patrick Adde
Chapter 7 Block Turbocodes in a realistic atmosphere (pages 373–414): Patrick Adde and Ramesh Pyndiah

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Additional resources for Channel Coding in Communication Networks: From Theory to Turbocodes

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The entropy H(U ) is therefore smaller than H(X). Let X be the variable at the encoder output. The inequality H(U ) ≥ H(X ) where the equality is true if information is preserved in the encoder involves H(X ) < H(X), which expresses the need for the redundancy. 4. Example of the binary symmetric channel We will now develop certain consequences of the necessarily redundant nature of channel coding in the simple, but important, case of a binary symmetric channel. Furthermore, the main conclusions reached for this channel can be generalized to almost any stationary channel.

34 Channel Coding in Communication Networks The input samples have a variance of P/2B, those of additive noise N0 /2 and the output samples the sum of these variances or (P + N0 B)/2B. The capacity of this channel is thus equal to: C= 1 1 P + N0 B P P 1 log2 = log2 (1 + ) = log2 (1 + ). 37] In this expression, the signal to noise ratio P/N appears in the argument of the logarithm since N = N0 B is the total noise power in the band B. e. a quantity of information per unit of time. 38] of the capacity of an additive Gaussian channel is justly famous but sometimes erroneously interpreted.

The “modulation gain” brought by certain systems, such as frequency modulation, at the cost of widening the band, in fact, results from a form of channel coding, misunderstood by radio-electricians prior to the birth of information theory. Information Theory 35 by a horizontal, which we could obtain by pouring a liquid whose volume would represent the total power of the signal (a result introduced by Shannon, often called water-ſlling). 3. 38] is ſnite, although the alphabet of the channel is continuous.

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