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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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Proceedings ArticleDOI
29 Nov 1993
TL;DR: This paper tries to give a unified presentation of soft-output decoding algorithms, which includes the list-output Viterbi algorithm (LOVA), the soft (symbol)-output SOVA, the optimal symbol- by-symbol detector (OSSD), the optimal subblock-by-subblock detector (OBBD, "vector MAP"), and finally a SOVA based on likelihood post-processing.
Abstract: This paper tries to give a unified presentation of soft-output decoding algorithms. It is intended to be tutorial on one hand, but on the other hand it covers some recent advances as well. The presentation includes the list-output Viterbi algorithm (LOVA), the soft (symbol)-output Viterbi algorithm (SOVA), the optimal symbol-by-symbol detector (OSSD, "symbol-by-symbol MAP"), the optimal subblock-by-subblock detector (OBBD, "vector MAP"), and finally a SOVA based on likelihood post-processing. The relation between these algorithms is derived in a structured manner. The average mutual information for i.i.d. input bits is chosen as a measure for the quality of the reliability outputs. >

39 citations

Journal ArticleDOI
Dan Raphaeli1
TL;DR: Two novel suboptimal algorithms are presented, for which the number of states is the same as the original code, yielding a complexity depending weakly on L, and both algorithms are substantially less complex than the optimal algorithm.
Abstract: Noncoherent decoding of trellis codes using multiple-symbol overlapped observations was shown previously to achieve close to the coherent performance. Optimal decoding by the Viterbi algorithm for L-symbol observations requires a number of states which grows exponentially with L. Two novel suboptimal algorithms are presented, for which the number of states is the same as the original code, yielding a complexity depending weakly on L. For practical values of L, both algorithms are substantially less complex than the optimal algorithm. The first algorithm, the basic decision feedback algorithm (BDFA), is a low complexity feedback decoding scheme, based on the Viterbi algorithm. This algorithm is shown to suffer from increased error probability and from error propagation. A slight modification to this algorithm can, in most cases, reduce these effects significantly. The second algorithm uses the BDFA as a basic building block. This algorithm is based on a novel concept called "estimated future" and its performance is very close to optimum for most practical eases with some additional complexity and memory requirements as compared to the first algorithm. Performance analysis and simulation results are also given.

39 citations

Journal ArticleDOI
TL;DR: A low complexity decoding algorithm for majority-logic decodable nonbinary low-density parity-check (LDPC) codes that requires only integer operations and finite field operations and can be implemented with simple combinational logic circuits in practical systems.
Abstract: In this letter, we propose a low complexity decoding algorithm for majority-logic decodable nonbinary low-density parity-check (LDPC) codes. The proposed algorithm is initialized with the quantized squared Euclidean distances between the constellation points and the received signals. Like most of the existing reliability-based decoding algorithms, the proposed algorithm requires only integer operations and finite field operations and (hence) can be implemented with simple combinational logic circuits in practical systems. Simulation results show that the proposed algorithm suffers from a little performance degradation compared with FFT-QSPA. The algorithm provides a candidate for trade-offs between performance and complexity.

39 citations

Patent
20 Jun 1980
TL;DR: In this paper, the decoding of the first stage is made to the 2nd error correction block, and the PCM data series of a plurality of channels and the 1st check word series are delayed for the different time from channel.
Abstract: PURPOSE: To prevent the mistake of the decoding error detection at the next stage, by adding the pointer indicating the decoding error of the 1st stage and discriminating the number of pointers within one block at the decoding of the next stage. CONSTITUTION: The decoding of the 1st stage is made to the 2nd error correction block, and the PCM data series of a plurality of channels and the 1st check word series are delayed for the different time from channel. After that, the decoding of the next stage is made to the 1st error correction block, and at the decoding of the 1st stage, the pointer representing the presence of error for every word is added according to the result of decoding relating to the 2nd error correction block. At the decoding of the next stage, if the number of pointers representing the error within the 1st error correction block exceeds a given number, when the result of discrimination for absence of errors is obtained through the decoding at the next stage, the pointer added at the decoding of the 1st stage is remained the same, allowing to avoid the missing of error detection. COPYRIGHT: (C)1982,JPO&Japio

39 citations

Journal ArticleDOI
TL;DR: A general approach for constructing high-rate cycle codes having girth 12 which achieve the lower bound on the block length is presented and an efficient encoding algorithm is proposed for this class of codes.
Abstract: In this paper, we present a general approach for constructing high-rate cycle codes having girth 12 which achieve the lower bound on the block length. An efficient encoding algorithm is also proposed for this class of codes. We estimate the word error rate of the cycle codes for additive white Gaussian noise (AWGN) channels under maximum likelihood decoding, using the minimum distance and its multiplicity. These estimates are compared to simulation results with iterative soft decoding. The performance of these codes for partial response channels is studied under iterative soft decoding using computer simulations.

39 citations


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