By Alain Glavieux
This e-book presents a accomplished review of the topic of channel coding. It starts off with an outline of knowledge conception, targeting the quantitative size of data 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 ebook.
Trellis coded modulations, that have their fundamental functions in excessive spectral potency transmissions, are then lined, earlier than the dialogue strikes directly to a sophisticated coding process known as turbocoding. those codes, invented within the Nineties by way of C. Berrou and A. Glavieux, exhibit extraordinary functionality. the variations among convolutional turbocodes and block turbocodes are defined, and for every kin, the authors current the coding and interpreting recommendations, including their performances. The ebook concludes with a bankruptcy at the implementation of turbocodes in circuits.
As such, a person serious about the parts of channel coding and blunder correcting coding will locate this ebook to be of precious assistance.Content:
Chapter 1 details 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 surroundings (pages 373–414): Patrick Adde and Ramesh Pyndiah
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Extra resources for Channel Coding in Communication Networks: From Theory to Turbocodes
32] for the majority of channels, but only for the smallest values of R, by operating a selection of the codes resulting from random coding to preserve only the best (we are said to “expurgate” the set of codes). Then the curve representing the function E(R) obtained is still decreasing and convex ∪. It does not deviate from the straight line of the equation E(R) = R0 − R apart from beyond a certain point where it is tangential to it and grows quicker than this straight line when R decreases to, ſnally, tangentially reach the y-axis at R = 0.
Each symbol of the alphabet is separately represented by a speciſc signal, which is the modulation operation3 . It makes it possible to use a code built on the basis of a ſnite alphabet in a channel receiving continuous signals. The capacity of such a channel is obviously limited to 3. Even if the signal thus obtained does not rigorously conform with the assumption of band limitation, the geometrical representation of signals in an Euclidean space with a ſnite number of dimensions remains, at the cost of a redeſnition of the bandwidth; see, for example , p.
General comments The fundamental theorem of channel coding is undoubtedly the most original and the most important result of information theory: original in that it implies the paradoxical possibility of a transmission without error via a disturbed channel, so contrary to apparent common sense that engineers had not even imagined it before Shannon; important in theory, but also in practice, because a transmission without error is a highly desirable result. The absence of explicit means to carry it out efſciently, just as the importance of the stake, were powerful incentives to perform research in the ſeld.