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Sequential decoding

About: Sequential decoding is a research topic. Over the lifetime, 8667 publications have been published within this topic receiving 204271 citations.


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Journal ArticleDOI
TL;DR: This work considers a new class of random codes which have the following advantages: (i) the overhead is constant (in the range of 5 to 10), independent of the number of data symbols being encoded (ii) the probability of completing decoding for such an overhead is essentially one (iii) the codes are effective for a number of information symbols as low as a few tens (iv) the only probability distribution required is the uniform distribution.
Abstract: The design of erasure correcting codes and their decoding algorithms is now at the point where capacity achieving codes are available with decoding algorithms that have complexity that is linear in the number of information symbols. One aspect of these codes is that the overhead (number of coded symbols beyond the number of information symbols required to achieve decoding completion with high probability) is linear in k. This work considers a new class of random codes which have the following advantages: (i) the overhead is constant (in the range of 5 to 10), independent of the number of data symbols being encoded (ii) the probability of completing decoding for such an overhead is essentially one (iii) the codes are effective for a number of information symbols as low as a few tens (iv) the only probability distribution required is the uniform distribution. The price for these properties is that the decoding complexity is greater, on the order of k 3/2. However, for the lower values of k where these codes are of particular interest, this increase in complexity might be outweighed by their advantages. The parity check matrices of these codes are chosen at random as windowed matrices, i.e. for each column an initial starting position of a window of length w is chosen and the succeeding w positions are chosen at random as zero or one. It can be shown that it is necessary that w=O(k 1/2) for the probabilistic matrix rank properties to behave as a non-windowed random matrix. The sufficiency of the condition has so far been established by extensive simulation, although other arguments strongly support this conclusion. The properties of the codes described depend heavily on the rank properties of random matrices over finite fields. Known results on such matrices are briefly reviewed and several conjectures needed in the discussion of the code properties, are stated. The likelihood of the validity of the conjectures is supported through extensive experimentation. Mathematical proof of the conjectures would be of great value for their own interest as well of the particular coding application described here.

50 citations

Journal ArticleDOI
TL;DR: A tutorial on the A* algorithm is given, the decoding complexity is compared with that of exhaustive search and Viterbi decoding algorithms, and performance curves obtained for several codes are presented.
Abstract: The A* algorithm is applied to maximum-likelihood soft-decision decoding of binary linear block codes. This paper gives a tutorial on the A* algorithm, compares the decoding complexity with that of exhaustive search and Viterbi decoding algorithms, and presents performance curves obtained for several codes.

50 citations

Proceedings ArticleDOI
01 Oct 2008
TL;DR: A parallel belief propagation algorithm for decoding low-density parity-check (LDPC) codes is presented in this paper, which provides an efficient and fast way for implementing the decoder.
Abstract: A parallel belief propagation algorithm for decoding low-density parity-check (LDPC) codes is presented in this paper based on Compute Unified Device Architecture (CUDA). As a new hardware and software architecture for addressing and managing computations, CUDA offers parallel data computing using the highly multithreaded coprocessor driven by very high memory bandwidth GPU. The parallel decoding algorithm, based on CUDA, allows that all bit-nodes or check-nodes work simultaneously, thus provides an efficient and fast way for implementing the decoder.

50 citations

Journal ArticleDOI
TL;DR: A novel iterative error control technique based on the threshold decoding algorithm and new convolutional self-doubly orthogonal codes is proposed, providing good tradeoff between complexity, latency and error performance.
Abstract: A novel iterative error control technique based on the threshold decoding algorithm and new convolutional self-doubly orthogonal codes is proposed. It differs from parallel concatenated turbo decoding as it uses a single convolutional encoder, a single decoder and hence no interleaver, neither at encoding nor at decoding. Decoding is performed iteratively using a single threshold decoder at each iteration, thereby providing good tradeoff between complexity, latency and error performance.

49 citations

Proceedings ArticleDOI
Ponani S. Gopalakrishnan1, Dimitri Kanevsky1, A. Nadas1, David Nahamoo1, Michael Picheny1 
11 Apr 1988
TL;DR: The authors generalize the maximum likelihood and related optimization criteria for training and decoding with a speech recognizer by considering weighted linear combinations of the logarithms of the likelihoods of words, of acoustics, and of (word, acoustic) pairs.
Abstract: The authors generalize the maximum likelihood and related optimization criteria for training and decoding with a speech recognizer. The generalizations are constructed by considering weighted linear combinations of the logarithms of the likelihoods of words, of acoustics, and of (word, acoustic) pairs. The utility of various patterns of weights are examined. >

49 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202351
2022112
202124
202026
201922
201832