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

IET Communications

Extending the Dorsch decoder towards achieving maximum-likelihood decoding for linear codes

Martin Tomlinson, Cen Jung Tjhai, Marcel Ambroze

Published Jun 1, 2007• 7 citations
Publisher

Abstract

It is shown that the relatively unknown Dorsch decoder may be extended to produce a decoder that is capable of maximum-likelihood decoding. The extension involves a technique for any linear (n, k) code that ensures that n−k less reliable, soft decisions of each received vector may be treated as erasures in determining candidate codewords. These codewords are derived from low information weight codewords and it is shown that an upper bound of this information weight may be calculated from each received vector in order to guarantee that the decoder will achieve maximum-likelihood decoding. Using the cross-correlation function, it is shown that the most likely codeword may be derived from a partial correlation function of these low information weight codewords, which leads to an efficient fast decoder. For a practical implementation, this decoder may be further simplified into a concatenation of a hard-decision decoder and a partial correlation decoder with insignificant performance degradation. Results are presented for some powerful, known codes, including a GF(4) non-binary BCH code. It is shown that maximum-likelihood decoding is realised for a high percentage of decoded codewords and that performance close to the sphere packing bound is attainable for codeword lengths up to 1000 bits.

DOI
10.1049/iet-com:20060338

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

Soft Decision and Quantised Soft Decision Decoding

Martin Tomlinson, Cen Jung Tjhai, Marcel Ambroze +2 more

In many modern communication systems, the full benefits of error-correction coding are only realised if the decoder is able to utilise soft decision decoding. In this chapter, we give both approximate and exact bounds on the performance of soft decision decoding compared to hard decision decoding. The effects of soft decision quantisation are explored as a function of the number of quantisation levels. Examples are given of the different performance achieved using soft and quantised soft decision decoding for (100,50) and (200,100) binary codes. A modified version of the near maximum likelihood decoder, the Dorsch decoder described in Chap. 15 , is presented for hard decision decoding. The performance improvements achieved by the decoder, compared to bounded distance decoding, are evaluated for some BCH codes. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Signals and communication technology•Jan 1, 2017•1 citation

Applications of linear programming to coding theory

Nathan Axvig

Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.

•Jan 1, 2010•3 citations

Design of optimization techniques for soft-decision decoding of linear block codes

Ching-Cheng Shih

This dissertation deals with maximum-likelihood soft-decision decoding as well as suboptimal soft-decision decoding of linear block codes. We present three new decoding algorithms: $MA\sp*D,\ H\sp*,$ and Directed Search. The first algorithm, $MA\sp*D,$ is an instance of Algorithm $A\sp*$ that conducts heuristic search through a code tree of the transmitted code when the decoding problem is transformed into a problem of graph-search through a code tree. $MA\sp*D$ takes into consideration more properties of the code and is considerably more efficient than the original $A\sp*$ decoding algorithm presented by Han, Hartmann, and Chen in 1993. The second algorithm, $H\sp*,$ is a new decoding algorithm that determines the value of every component of a minimum-cost codeword by estimating the cost of the minimum-cost codeword, which has a fixed value at one of the k most reliable, linearly independent bit positions. The suboptimal version of this algorithm is combined with other decoding algorithms to reduce the search space during the decoding process. The third algorithm, Directed Search, is designed to enhance the performance of soft-decision decoding by searching in continuous space. This approach explores the search space between a given vector and the received vector and finds the codeword closest to the received vector in the space explored. We also present a novel and efficient hybrid decoding algorithm for (n, k) linear block codes. It hybridizes $MA\sp*D,\ H\sp*,$ and Directed Search algorithms, utilizing their strengths to make the decoding more efficient. Simulation results for this hybrid algorithm are presented for the (104,52) binary-extended quadratic residue code and for the (128,64), the (256, 131), and the (256, 139) binary-extended BCH codes. This hybrid algorithm can efficiently decode the (104,52) binary extended quadratic residue code and the (128,64) code for any signal-to-noise ratio and has near-optimal to optimal performance. Previously, no practical decoder could decode these two codes with such a performance for all signal-to-noise ratio ranges.

•Jan 1, 1996•0 citations

Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial

Igal Sason, Shlomo Shitz Shamai

Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial focuses on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding. Though the ML decoding algorithm is prohibitively complex for most practical codes, their performance analysis under ML decoding allows to predict their performance without resorting to computer simulations. It also provides a benchmark for testing the sub-optimality of iterative (or other practical) decoding algorithms. This analysis also establishes the goodness of linear codes (or ensembles), determined by the gap between their achievable rates under optimal ML decoding and information theoretical limits. In Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial, upper and lower bounds on the error probability of linear codes under ML decoding are surveyed and applied to codes and ensembles of codes on graphs. For upper bounds, we discuss various bounds where focus is put on Gallager bounding techniques and their relation to a variety of other reported bounds. Within the class of lower bounds, we address de Caen's based bounds and their improvements, and also consider sphere-packing bounds with their recent improvements targeting codes of moderate block lengths. Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial is a comprehensive introduction to this important topic for students, practitioners and researchers working in communications and information theory.

now publishers, Inc. eBooks•Jan 1, 2006•47 citations

Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial

Igal Sason, Shlomo Shitz Shamai

This article is focused on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding. Though the ML decoding algorithm is prohibitively complex for most practical codes, their performance analysis under ML decoding allows to predict their performance without resorting to computer simulations. It also provides a benchmark for testing the sub-optimality of iterative (or other practical) decoding algorithms. This analysis also establishes the goodness of linear codes (or ensembles), determined by the gap between their achievable rates under optimal ML decoding and information theoretical limits. In this article, upper and lower bounds on the error probability of linear codes under ML decoding are surveyed and applied to codes and ensembles of codes on graphs. For upper bounds, we discuss various bounds where focus is put on Gallager bounding techniques and their relation to a variety of other reported bounds. Within the class of lower bounds, we address de Caen’s based bounds and their improvements, and also consider sphere-packing bounds with their recent improvements targeting codes of moderate block lengths.

Foundations and Trends® in Communications and Information Theory•Jul 7, 2006•153 citations
It is shown that the relatively unknown Dorsch decoder may be extended to produce a decoder that is capable of maximum-likelihood decoding. The extension involves a technique for any linear (n, k) code that ensures that n−k less reliable, soft decisions of each received vector may be treated as erasures in determining candidate codewords. These codewords are derived from low information weight codewords and it is shown that an upper bound of this information weight may be calculated from each received vector in order to guarantee that the decoder will achieve maximum-likelihood decoding. Using the cross-correlation function, it is shown that the most likely codeword may be derived from a partial correlation function of these low information weight codewords, which leads to an efficient fast decoder. For a practical implementation, this decoder may be further simplified into a concatenation of a hard-decision decoder and a partial correlation decoder with insignificant performance degradation. Results are presented for some powerful, known codes, including a GF(4) non-binary BCH code. It is shown that maximum-likelihood decoding is realised for a high percentage of decoded codewords and that performance close to the sphere packing bound is attainable for codeword lengths up to 1000 bits.
In many modern communication systems, the full benefits of error-correction coding are only realised if the decoder is able to utilise soft decision decoding. In this chapter, we give both approximate and exact bounds on the performance of soft decision decoding compared to hard decision decoding. The effects of soft decision quantisation are explored as a function of the number of quantisation levels. Examples are given of the different performance achieved using soft and quantised soft decision decoding for (100,50) and (200,100) binary codes. A modified version of the near maximum likelihood decoder, the Dorsch decoder described in Chap. 15 , is presented for hard decision decoding. The performance improvements achieved by the decoder, compared to bounded distance decoding, are evaluated for some BCH codes. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.
This dissertation deals with maximum-likelihood soft-decision decoding as well as suboptimal soft-decision decoding of linear block codes. We present three new decoding algorithms: $MA\sp*D,\ H\sp*,$ and Directed Search. The first algorithm, $MA\sp*D,$ is an instance of Algorithm $A\sp*$ that conducts heuristic search through a code tree of the transmitted code when the decoding problem is transformed into a problem of graph-search through a code tree. $MA\sp*D$ takes into consideration more properties of the code and is considerably more efficient than the original $A\sp*$ decoding algorithm presented by Han, Hartmann, and Chen in 1993. The second algorithm, $H\sp*,$ is a new decoding algorithm that determines the value of every component of a minimum-cost codeword by estimating the cost of the minimum-cost codeword, which has a fixed value at one of the k most reliable, linearly independent bit positions. The suboptimal version of this algorithm is combined with other decoding algorithms to reduce the search space during the decoding process. The third algorithm, Directed Search, is designed to enhance the performance of soft-decision decoding by searching in continuous space. This approach explores the search space between a given vector and the received vector and finds the codeword closest to the received vector in the space explored. We also present a novel and efficient hybrid decoding algorithm for (n, k) linear block codes. It hybridizes $MA\sp*D,\ H\sp*,$ and Directed Search algorithms, utilizing their strengths to make the decoding more efficient. Simulation results for this hybrid algorithm are presented for the (104,52) binary-extended quadratic residue code and for the (128,64), the (256, 131), and the (256, 139) binary-extended BCH codes. This hybrid algorithm can efficiently decode the (104,52) binary extended quadratic residue code and the (128,64) code for any signal-to-noise ratio and has near-optimal to optimal performance. Previously, no practical decoder could decode these two codes with such a performance for all signal-to-noise ratio ranges.
Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial focuses on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding. Though the ML decoding algorithm is prohibitively complex for most practical codes, their performance analysis under ML decoding allows to predict their performance without resorting to computer simulations. It also provides a benchmark for testing the sub-optimality of iterative (or other practical) decoding algorithms. This analysis also establishes the goodness of linear codes (or ensembles), determined by the gap between their achievable rates under optimal ML decoding and information theoretical limits. In Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial, upper and lower bounds on the error probability of linear codes under ML decoding are surveyed and applied to codes and ensembles of codes on graphs. For upper bounds, we discuss various bounds where focus is put on Gallager bounding techniques and their relation to a variety of other reported bounds. Within the class of lower bounds, we address de Caen's based bounds and their improvements, and also consider sphere-packing bounds with their recent improvements targeting codes of moderate block lengths. Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial is a comprehensive introduction to this important topic for students, practitioners and researchers working in communications and information theory.
This article is focused on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding. Though the ML decoding algorithm is prohibitively complex for most practical codes, their performance analysis under ML decoding allows to predict their performance without resorting to computer simulations. It also provides a benchmark for testing the sub-optimality of iterative (or other practical) decoding algorithms. This analysis also establishes the goodness of linear codes (or ensembles), determined by the gap between their achievable rates under optimal ML decoding and information theoretical limits. In this article, upper and lower bounds on the error probability of linear codes under ML decoding are surveyed and applied to codes and ensembles of codes on graphs. For upper bounds, we discuss various bounds where focus is put on Gallager bounding techniques and their relation to a variety of other reported bounds. Within the class of lower bounds, we address de Caen’s based bounds and their improvements, and also consider sphere-packing bounds with their recent improvements targeting codes of moderate block lengths.