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

arXiv:1804.00274 · IEEE Communications Letters

Adaptive Group Shuffled Decoding for LDPC Codes

Tofar C.-Y. Chang, Yu T. Su

Published Jun 20, 2017• 7 citations
arXivHTMLPDF

Abstract

We propose new grouping methods for group shuffled (GS) decoding of both regular and irregular low-density parity check cods. These methods are applicable for the belief-propagation as well as the min-sum-based GS decoders. Integer-valued metrics for measuring the reliability of each tentative variable node (VN) decision and the associated likelihood of being corrected are developed. The metrics are used to determine the VN updating priority, so the grouping may vary in each iteration. We estimate the computation complexity needed to adaptively regroup VNs. Numerical results show that our GS algorithms improve the performance of some existing GS belief-propagation decoders.

DOI
10.1109/lcomm.2017.2717405

Related papers

A new optimization of group shuffled LDPC decoding

Nassib Laouini, Larbi Ben Hadj Slama, Ammar Bouallègue

Shuffled decoding is known to provide efficient and high-throughput implementation of LDPC decoders. In this paper, a new grouping technique of variable nodes to accelerate the message-passing rate is presented. In this algorithm, unlike other group-shuffled decoding methods, we consider for the first sub-group a set of variable nodes that has a low value of the intrinsic information, where the variable nodes of the code graph are divided into sub-groups called layers to perform group-by-group message-passing decoding and each iteration is broken into multiple sub-iterations. This paper proposes a simplification of the Variable Node group-shuffled Belief Propagation (Optimized VN group-shuffled BP) to lower the complexity of the check node update rule. Simulation results, verify that the Optimized VN group-shuffled BP does yield a faster convergence rate than both Belief Propagation (BP) and Check Node group-shuffled Belief Propagation (CN group-shuffled BP) decoder.

International Conference on Communications and Networking (ComNet)•Mar 1, 2014•0 citations

New group shuffled BP decoding algorithms for LDPC codes

Chi-Yuan Chang, Yu‐Liang Chen, Chang‐Ming Lee +1 more

Implementing a belief propagation (BP) based LDPC decoder requires high degrees of parallelism using many component soft-in soft-output (SISO) decoding units to perform message passing from variable nodes to check nodes or vice versa. An obvious complexity-reduction solution is to serialize the decoding process, i.e., dividing a decoding iteration into several serial sub-iterations in which a sub-iteration performs only part of the complete parallel message-passing operation. The group horizontal shuffled BP (GHSBP) and vertical shuffled BP (GVSBP) algorithms respectively partition the check and variable nodes of the code graph into groups to perform group-by-group message-passing decoding. This paper proposes new techniques to improve three key elements of a GHSBP decoding algorithm, namely, the grouping method, the decoding schedule and the log-likelihood updating formulae. The (check nodes) grouping method and decoding schedule optimize certain design criterion. The new normalized min-sum updating formula with a self-adjustable correction (scaling) factor offers better nonlinear approximation. Numerical performance of new GHSBP algorithms that include part or all three new techniques indicate that the combination of the proposed grouping and decoding schedule yields a faster convergence rate and our modified min-sum algorithm gives performance superior to that of the conventional min-sum and normalized min-sum algorithm and is very close to that of the sum-product algorithm.

IEEE International Symposium on Information Theory (ISIT)•Jun 1, 2009•4 citations

Shuffled belief propagation decoding based on variable nodes reliability

Wang Haiqua

This paper proposes a new group shuffled belief propagation(BP)decoding of low-density parity-check(LDPC)codes,which divides check nodes or variable nodes into groups based on the reliability of the initial channel message.The bit error rate and the average number of iterations are analyzed with additive white Gaussian noise(AWGN)channel.Simulations verify that the proposed decoding strategy greatly improves the decoding performance.

Chinese Journal of Radio Science•Jan 1, 2015•0 citations

A Non-Disjoint Group Shuffled Decoding for LDPC Codes

Yen-Cheng Hsu, Tofar C.-Y. Chang, Yu T. Su +1 more

To reduce the implementation complexity of a belief propagation (BP) based low-density parity-check (LDPC) decoder, shuffled BP decoding schedules, which serialize the decoding process by dividing a complete parallel message-passing iteration into a sequence of sub-iterations, have been proposed. The so-called group horizontal shuffled BP algorithm partitions the check nodes of the code graph into groups to perform group-by-group message-passing decoding. This paper proposes a new grouping technique to accelerate the message-passing rate. Performance of the proposed algorithm is analyzed by a Gaussian approximation approach. Both analysis and numerical experiments verify that the new algorithm does yield a convergence rate faster than that of existing conventional or group shuffled BP decoder with the same computing complexity constraint.

arXiv:1202.1060•Feb 6, 2012•0 citations

A Method for Grouping Symbol Nodes of Group Shuffled BP Decoding Algorithm

Y. Sato, Gou Hosoya, Hideki Yagi +1 more

In this paper, we propose a method for enhancing performance of a sequential version of the belief-propagation (BP) decoding algorithm, the group shuffled BP decoding algorithm for low-density parity-check (LDPC) codes. An improved BP decoding algorithm, called the shuffled BP decoding algorithm, decodes each symbol node in serial at each iteration. To reduce the decoding delay of the shuffled BP decoding algorithm, the group shuffled BP decoding algorithm divides all symbol nodes into several groups. In contrast to the original group shuffled BP, which automatically generates groups according to symbol positions, in this paper we propose a method for grouping symbol nodes which generates groups according to the structure of a Tanner graph of the codes. The proposed method can accelerate the convergence of the group shuffled BP algorithm and obtain a lower error rate in a small number of iterations. We show by simulation results that the decoding performance of the proposed method is improved compared with those of the shuffled BP decoding algorithm and the group shuffled BP decoding algorithm.

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences•Oct 1, 2008•1 citation

Belief-Propagation Decoding of LDPC Codes With Variable Node–Centric Dynamic Schedules

Tofar C.-Y. Chang, Pin-Han Wang, Jian-Jia Weng +2 more

Belief propagation (BP) decoding of low-density parity-check (LDPC) codes with various dynamic decoding schedules have been proposed to improve the efficiency of the conventional flooding schedule. As the ultimate goal of an ideal LDPC code decoder is to have correct bit decisions, a dynamic decoding schedule should be variable node (VN)-centric and be able to find the VNs with probable incorrect decisions and having a good chance to be corrected if chosen for update. We propose a novel and effective metric called conditional innovation (CI) which serves this design goal well. To make the most of dynamic scheduling which produces high-reliability bit decisions, we limit our search for the candidate VNs to those related to the latest updated nodes only. Based on the CI metric and the new search guideline separately or in combination, we develop several highly efficient decoding schedules. To reduce decoding latency, we introduce multi-edge updating versions which offer extra latency-performance tradeoffs. Numerical results show that both single-edge and multi-edge algorithms provide better decoding performance against most dynamic schedules and the CI-based algorithms are particularly impressive at the first few decoding iterations.

arXiv:2102.11089•IEEE Transactions on Communications•May 10, 2021•19 citations
We propose new grouping methods for group shuffled (GS) decoding of both regular and irregular low-density parity check cods. These methods are applicable for the belief-propagation as well as the min-sum-based GS decoders. Integer-valued metrics for measuring the reliability of each tentative variable node (VN) decision and the associated likelihood of being corrected are developed. The metrics are used to determine the VN updating priority, so the grouping may vary in each iteration. We estimate the computation complexity needed to adaptively regroup VNs. Numerical results show that our GS algorithms improve the performance of some existing GS belief-propagation decoders.
Shuffled decoding is known to provide efficient and high-throughput implementation of LDPC decoders. In this paper, a new grouping technique of variable nodes to accelerate the message-passing rate is presented. In this algorithm, unlike other group-shuffled decoding methods, we consider for the first sub-group a set of variable nodes that has a low value of the intrinsic information, where the variable nodes of the code graph are divided into sub-groups called layers to perform group-by-group message-passing decoding and each iteration is broken into multiple sub-iterations. This paper proposes a simplification of the Variable Node group-shuffled Belief Propagation (Optimized VN group-shuffled BP) to lower the complexity of the check node update rule. Simulation results, verify that the Optimized VN group-shuffled BP does yield a faster convergence rate than both Belief Propagation (BP) and Check Node group-shuffled Belief Propagation (CN group-shuffled BP) decoder.
Implementing a belief propagation (BP) based LDPC decoder requires high degrees of parallelism using many component soft-in soft-output (SISO) decoding units to perform message passing from variable nodes to check nodes or vice versa. An obvious complexity-reduction solution is to serialize the decoding process, i.e., dividing a decoding iteration into several serial sub-iterations in which a sub-iteration performs only part of the complete parallel message-passing operation. The group horizontal shuffled BP (GHSBP) and vertical shuffled BP (GVSBP) algorithms respectively partition the check and variable nodes of the code graph into groups to perform group-by-group message-passing decoding. This paper proposes new techniques to improve three key elements of a GHSBP decoding algorithm, namely, the grouping method, the decoding schedule and the log-likelihood updating formulae. The (check nodes) grouping method and decoding schedule optimize certain design criterion. The new normalized min-sum updating formula with a self-adjustable correction (scaling) factor offers better nonlinear approximation. Numerical performance of new GHSBP algorithms that include part or all three new techniques indicate that the combination of the proposed grouping and decoding schedule yields a faster convergence rate and our modified min-sum algorithm gives performance superior to that of the conventional min-sum and normalized min-sum algorithm and is very close to that of the sum-product algorithm.
To reduce the implementation complexity of a belief propagation (BP) based low-density parity-check (LDPC) decoder, shuffled BP decoding schedules, which serialize the decoding process by dividing a complete parallel message-passing iteration into a sequence of sub-iterations, have been proposed. The so-called group horizontal shuffled BP algorithm partitions the check nodes of the code graph into groups to perform group-by-group message-passing decoding. This paper proposes a new grouping technique to accelerate the message-passing rate. Performance of the proposed algorithm is analyzed by a Gaussian approximation approach. Both analysis and numerical experiments verify that the new algorithm does yield a convergence rate faster than that of existing conventional or group shuffled BP decoder with the same computing complexity constraint.
In this paper, we propose a method for enhancing performance of a sequential version of the belief-propagation (BP) decoding algorithm, the group shuffled BP decoding algorithm for low-density parity-check (LDPC) codes. An improved BP decoding algorithm, called the shuffled BP decoding algorithm, decodes each symbol node in serial at each iteration. To reduce the decoding delay of the shuffled BP decoding algorithm, the group shuffled BP decoding algorithm divides all symbol nodes into several groups. In contrast to the original group shuffled BP, which automatically generates groups according to symbol positions, in this paper we propose a method for grouping symbol nodes which generates groups according to the structure of a Tanner graph of the codes. The proposed method can accelerate the convergence of the group shuffled BP algorithm and obtain a lower error rate in a small number of iterations. We show by simulation results that the decoding performance of the proposed method is improved compared with those of the shuffled BP decoding algorithm and the group shuffled BP decoding algorithm.
Belief propagation (BP) decoding of low-density parity-check (LDPC) codes with various dynamic decoding schedules have been proposed to improve the efficiency of the conventional flooding schedule. As the ultimate goal of an ideal LDPC code decoder is to have correct bit decisions, a dynamic decoding schedule should be variable node (VN)-centric and be able to find the VNs with probable incorrect decisions and having a good chance to be corrected if chosen for update. We propose a novel and effective metric called conditional innovation (CI) which serves this design goal well. To make the most of dynamic scheduling which produces high-reliability bit decisions, we limit our search for the candidate VNs to those related to the latest updated nodes only. Based on the CI metric and the new search guideline separately or in combination, we develop several highly efficient decoding schedules. To reduce decoding latency, we introduce multi-edge updating versions which offer extra latency-performance tradeoffs. Numerical results show that both single-edge and multi-edge algorithms provide better decoding performance against most dynamic schedules and the CI-based algorithms are particularly impressive at the first few decoding iterations.