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List decoding
About: List decoding is a research topic. Over the lifetime, 7251 publications have been published within this topic receiving 151182 citations.
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TL;DR: In this paper, a generalized Dijkstra's algorithm is employed to search through a trellis for an equivalent code of the transmitted code, guided by an evaluation function f defined to take advantage of the information provided by the received vector and the inherent properties of transmitted code.
Abstract: The authors present a novel and efficient maximum-likelihood soft-decision decoding algorithm for linear block codes. The approach used here converts the decoding problem into a search problem through a graph that is a trellis for an equivalent code of the transmitted code. A generalized Dijkstra's algorithm, which uses a priority-first search strategy, is employed to search through this graph. This search is guided by an evaluation function f defined to take advantage of the information provided by the received vector and the inherent properties of the transmitted code. This function f is used to reduce drastically the search space and to make the decoding efforts of this decoding algorithm adaptable to the noise level. For example, for most real channels of the 35 000 samples tried, simulation results for the (128,64) binary extended BCH code show that the proposed decoding algorithm is fifteen orders of magnitude more efficient in time and in space than that proposed by Wolf (1978). Simulation results for the (104, 52) binary extended quadratic residue code are also given. >
145 citations
Book•
29 Apr 2002
TL;DR: Practical examples of MAP and SOVA decoding for turbo codes and infinite field arithematic and algebraic decoding methods for BCH and Reed-Solomon codes are explained.
Abstract: From the Publisher:
Error Control Coding: From Theory to Practice provides a concise introduction to basic coding techniques and their application. The fundamental concepts of coding theory are explained using simple examples with minimum use of complex mathematical tools. The selection of appropriate codes and the design of decoders are discussed.
Bridging the gap between digital communications and information theory, this accessible approach will appeal to students and practising engineers alike.
The clear presentation and practical emphasis make this book an excellent tool for both communications and electronic engineering students. Practitioners new to the field will find this text an essential guide to coding.
Features include:
End of chapter problems to test and develop the readers understanding of the most popular codes and decoding methodsFinite field arithematic and algebraic decoding methods for BCH and Reed-Solomon codesDetailed coverage of Viterbi decoding and related implementation issuesTurbo codes and related code types, including Gallager codes and turbo product codesPractical examples of MAP and SOVA decoding for turbo codes
144 citations
TL;DR: Random codes for the arbitrarily varying channel are investigated, where a quantity called key rate is used as a measure of the rate of increase.
Abstract: Random codes for the arbitrarily varying channel are investigated The code ensemble is restricted to increase only exponentially with codeword length; a quantity called key rate is used as a measure of the rate of increase The reliability function for such codes is related to the reliability function for codes with unlimited key rate Explicit results are obtained in some examples of practical interest
144 citations
Patent•
28 Apr 2005
TL;DR: In this paper, a method and apparatus for decoding a coded data stream of bits using an inner decoder, deinterleaver and an outer decoder is presented. But the decoding is terminated and a decoded word is outputted if the syndromes of the corrected word of the first decoding are all zeros.
Abstract: A method and apparatus for decoding a coded data stream of bits using an inner decoder, deinterleaver and an outer decoder. The outer decoder first decodes by error correction decoding for r errors per word. The decoding is terminated and a decoded word is outputted if the syndromes of the corrected word of the first decoding are all zeros. If the syndromes of the corrected word of the first decoding are not all zeros, a second decoding is performed by error decoding and erasure for the number of errors reduced by one and the number of erasures increased to two. The decoding is terminated and a decoded word is outputted if the syndromes of the corrected word of the second decoding are all zeros. If the syndromes of the corrected word of the second decoding are not all zeros, the second decoding by correcting and erasure decoding is repeated for the number of errors reduced by one and the number of erasures increased by two for each iteration of the second decoding.
141 citations
IBM1
TL;DR: An application of the maximum-likelihood decoding (MLD) algorithm, which was originally proposed by Viterbi in decoding convolutional codes, is discussed and it is shown that a substantial performance gain is attainable by this probabilistic decoding method.
Abstract: Modems for digital communication often adopt the so-called correlative level coding or the partial-response signaling, which attains a desired spectral shaping by introducing controlled intersymbol interference terms. In this paper, a correlative level encoder is treated as a linear finite-state machine and an application of the maximum-likelihood decoding (MLD) algorithm, which was originally proposed by Viterbi in decoding convolutional codes, is discussed. Asymptotic expressions for the probability of decoding error are obtained for a class of correlative level coding systems, and the results are confirmed by computer simulations. It is shown that a substantial performance gain is attainable by this probabilistic decoding method.
141 citations