Channel Coding in Communication Networks: From Theory to - download pdf or read online

By Alain Glavieux

ISBN-10: 0470612428

ISBN-13: 9780470612422

ISBN-10: 190520924X

ISBN-13: 9781905209248

This ebook offers a accomplished review of the topic of channel coding. It starts off with an outline of data conception, targeting the quantitative size 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 interpreting algorithms and recursive systematic convolutional codes, that are utilized in the remainder of the e-book.

Trellis coded modulations, that have their fundamental functions in excessive spectral potency transmissions, are then lined, sooner than the dialogue strikes directly to a complicated coding method known as turbocoding. those codes, invented within the Nineteen Nineties through C. Berrou and A. Glavieux, exhibit extraordinary functionality. the variations among convolutional turbocodes and block turbocodes are defined, and for every family members, the authors current the coding and interpreting options, including their performances. The booklet concludes with a bankruptcy at the implementation of turbocodes in circuits.

As such, somebody interested by the components of channel coding and blunder correcting coding will locate this booklet to be of beneficial 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 pragmatic surroundings (pages 373–414): Patrick Adde and Ramesh Pyndiah

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Extra info for Channel Coding in Communication Networks: From Theory to Turbocodes

Example text

XM }, xi ∈ Xn regardless of i, 1 ≤ i ≤ n. The emission of the mth codeword represents the mth message. e. the decoder associates the number m identifying the decoded message to the received sequence y, if: Pr(y|xm ) > Pr(y|xm ) ∀ m = m, 1 ≤ m ≤ M. 23] An error occurs if for a transmitted m this decision rule leads to m which is different from m. 24] Pr(y|xm )φm (y), y∈Yn where Pem is the probability of an error when m is transmitted. 25] where s is a positive parameter, which is arbitrary for the moment.

Xn be the symbols of the codeword at channel input and y1 , y2 , . . , yn be their corresponding symbols at the output. The independence of the successive transitions in the channel leads to: n Pr(yi |xi ), ∀ x ∈ Xn , ∀ y ∈ Yn . Pr(y|x) = i=1 Restricting ourselves to codes where the successive codeword symbols are chosen by random coding independently of each other, following the same law p(xi ), we have: n P (x) = p(xi ), x = (x1 , x2 , . . 29] can be transformed. e. with all the possible codewords with n symbols in the channel input alphabet: the sum of all the products of n terms is equal to the nth power of the sum of the terms written for all the symbols of this alphabet.

Vol. 44, No. 10, pp. 1261–1271, Oct. 1996.

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

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