John L. Fan's Constrained Coding and Soft Iterative Decoding PDF

By John L. Fan

ISBN-10: 1461355974

ISBN-13: 9781461355977

ISBN-10: 1461515254

ISBN-13: 9781461515258

Constrained Coding and gentle Iterative Decoding is the 1st paintings to mix the problems of restricted coding and tender iterative interpreting (e.g., faster and LDPC codes) from a unified viewpoint. due to the fact that limited coding is prevalent in magnetic and optical garage, it is vital to exploit a few distinctive recommendations (modified concatenation scheme or bit insertion) so as to follow gentle iterative deciphering.
fresh breakthroughs within the layout and deciphering of error-control codes (ECCs) exhibit major capability for bettering the functionality of many communications structures. ECCs equivalent to rapid codes and low-density parity money (LDPC) codes may be represented via graphs and decoded through passing probabilistic (a.k.a. `soft') messages alongside the sides of the graph. This message-passing algorithm yields robust decoders whose functionality can process the theoretical limits on ability. This exposition makes use of `normal graphs,' brought via Forney, which expand in a normal demeanour to dam diagram representations of the process and supply an easy unified framework for the deciphering of ECCs, restricted codes, and channels with reminiscence. delicate iterative interpreting is illustrated through the appliance of rapid codes and LDPC codes to magnetic recording channels.
For magnetic and optical garage, a subject arises within the use of limited coding, which locations regulations at the sequences that may be transmitted in the course of the channel; using restricted coding together with gentle ECC decoders is addressed via the modifiedconcatenation scheme sometimes called `reverse concatenation.' in addition, a gentle constraint decoder yields extra coding achieve from the redundancy within the constraint, that could be of useful curiosity relating to optical garage. furthermore, this monograph provides numerous different examine effects (including the layout of sliding-block lossless compression codes, and the interpreting of array codes as LDPC codes).
Constrained Coding and smooth Iterative Decoding will turn out valuable to scholars, researchers engineers who're attracted to figuring out this new gentle iterative deciphering paradigm and employing it in communications and garage systems.

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Extra info for Constrained Coding and Soft Iterative Decoding

Example text

9). 3, to represent P (x = a) for some value a E A, which can be determined from the context. On the other hand, an arbitrary vector of non-negative real numbers (that do not necessarily sum to 1) is called a message vector and is denoted by the symbol ti. If the variable x belongs to the alphabet A, then the message vector would have length IAI, where mk 2: 0, and it is assumed that the entries of the message vector are not all simultaneously zero. The entries of the vector can also be written as j,L (x = ak) = mk, in analogy to the probability notation P (x = ak).

K P(E) = LP(Fk,E). 4) for P (E) > O. 5) so that conditioning does not change the probability if the events are independent. 7) Chapter 2: Message-passing algorithm 25 which is known as the chain rule for conditional probabilities. 8) in which case the three events E, F and G (in that order) are said to satisfy the Markov property. These events then form a Markov chain, which is denoted by E f--+ F f--+ G. 8) are P (E I F, G) = P (E I F) P (G IE, F) = P (G I F) . 3), and they are said to be conditionally independent.

OVERVIEW The two key topics of this monograph are error-control codes with soft iterative decoding, and a modified concatenation scheme for constrained coding. Chapter 2 gives an elementary tutorial on "normal graphs" and the message-passing algorithm and introduces the concept of a module as a way to represent soft iterative decoding systems using block diagrams. Low-density parity check (LDPC) codes and their decoding are explained. Chapter 3 continues the discussion with the forward-backward algorithm for channel decoding and for turbo codes.

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Constrained Coding and Soft Iterative Decoding by John L. Fan


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