Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE International Symposium on Information Theory (ISIT)

On maximum likelihood soft decision syndrome decoding

M.P.C. Fossorier, Shu Hwa Lin, J. Synders

Published Nov 19, 2002• 2 citations
Publisher

Abstract

Maximum likelihood decoding of binary linear block codes is addressed by combining the approaches of processing the generator matrix G and parity-check matrix H.
DOI
10.1109/isit.1995.550402

Related papers

On maximum likelihood soft-decision decoding of binary linear codes

N.J.C. Lous, P.A.H. Bours, Henk C. A. van Tilborg

Two new implementations of a maximum-likelihood soft-decision decoding algorithm for binary linear codes are derived. Instead of checking only error patterns yielding a codeword when added to the rounded-off version of the received vector, these implementations principally consider all possible column patterns. Compared to known implementations, this allows a more efficient use of branching and bounding techniques.>

IEEE Transactions on Information Theory•Jan 1, 1993•28 citations

Improvements in two soft decision maximum likelihood decoding algorithms for linear block codes

W. Stieritz, Jack K. Wolf

•Jan 1, 1977•0 citations

On maximum likelihood soft decision decoding of binary Hamming codes

P.A.H. Bours, N.J.C. Lous, Henk C. A. van Tilborg

•Jan 1, 1991•0 citations

Soft-Decision Decoding for Linear Codes : The Quest to Maximum Likelihood Decoding

Takuya Koumoto

IEICE technical report. Information theory•Nov 20, 2007•0 citations

Ellipsoidal coverings and error-free search in maximum likelihood decoding

Ilya I. Dumer

For general linear (n, k) codes, maximum likelihood decoding is performed by random search of error-free k-subsets. The lowest known complexity of soft-decision decoding is obtained.

IEEE International Symposium on Information Theory (ISIT)•Nov 27, 2002•0 citations

Maximum-likelihood Soft-decision Decoding for Binary Linear Block Codes Based on Their Supercodes

Yunghsiang Sam Han, Hung-Ta Pai, Po‐Ning Chen +1 more

Based on the notion of supercodes, we propose a two-phase maximum-likelihood soft-decision decoding (tpMLSD) algorithm for binary linear block codes in this work. The first phase applies the Viterbi algorithm backwardly to a trellis derived from the parity-check matrix of the supercode of the linear block code. Using the information retained from the first phase, the second phase employs the priority-first search algorithm to the trellis corresponding to the linear block code itself, which guarantees finding the ML decision. Simulations on Reed-Muller codes show that the proposed two-phase scheme is an order of magnitude more efficient in average decoding complexity than the recursive maximum-likelihood decoding (RMLD) [1] when the signal-to-noise ratio per information bit is 4.5 dB.

arXiv:1408.1310•Aug 6, 2014•0 citations
Based on the notion of supercodes, we propose a two-phase maximum-likelihood soft-decision decoding (tpMLSD) algorithm for binary linear block codes in this work. The first phase applies the Viterbi algorithm backwardly to a trellis derived from the parity-check matrix of the supercode of the linear block code. Using the information retained from the first phase, the second phase employs the priority-first search algorithm to the trellis corresponding to the linear block code itself, which guarantees finding the ML decision. Simulations on Reed-Muller codes show that the proposed two-phase scheme is an order of magnitude more efficient in average decoding complexity than the recursive maximum-likelihood decoding (RMLD) [1] when the signal-to-noise ratio per information bit is 4.5 dB.