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

International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

An Improved Self-Corrected Min-Sum Decoding for LDPC Codes

Xingjin Li, Liguo Zhang, Jingxuan Tan, Jiahui Ma, Yingzhao Hui, Shengfei Bai

Published Nov 26, 2021• 0 citations
Publisher

Abstract

Low-Density Parity-Check (LDPC) code is a linear block code approaching the Shannon limit. The improvement of the decoding performance of LDPC codes is a hot research direction today. To improve the decoding performance of the self-correcting min-sum (SCMS) algorithm, an improved self-correcting min-sum (ISCMS) algorithm is proposed based on this algorithm. In the iterative decoding process of the ISCMS algorithm, the update rule of the check node (CN) is optimized to the update rule of the CN of the IOMS algorithm, which improves the accuracy of decoding from the processing of the CN. Simulation results show that the proposed ISCMS algorithm has better error performance and convergence performance than the SCMS algorithm with a small increase in computational complexity.

DOI
10.1109/iske54062.2021.9755394

Related papers

Optimized Min-Sum Decoding Algorithm for Low Density Parity Check Codes

Mohammad Rakibul Islam, Dewan Siam Shafiullah, Muhammad Mostafa +2 more

Abstract ― Low Density Parity Check (LDPC) code approaches Shannon–limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.

•Oct 31, 2015•2 citations

Optimized Min-Sum decoding algorithm for Low Density PC codes

Dewan Siam Shafiullah, Mohammad Rakibul Islam, Muhammad Mostafa Amir Faisal +1 more

Low Density Parity Check (LDPC) approaches Shannon -limit performance for binary field and long code lengths. However performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding, while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.

International Conference on Advanced Communication Technology•Jan 1, 2012•5 citations

Improved Extended Min-Sum Algorithm for Non-Binary LDPC Codes Based on Node Reliability

Yibing Li, Sitong Zhang, Fang Ye +3 more

Low Density Parity Check (LDPC) codes have been widely applied in recent years, and Extended Min-Sum (EMS) decoding algorithm is the most practical algorithm, which has a fairly low computational cost. Due to the great loss in error correction performance of EMS algorithm, in this paper, we establish constraints to obtain reliable nodes while decoding and evaluate the reliability of the nodes. The nodes and information transferred between nodes are processed and optimized according to the reliability of the nodes. High reliability nodes will not be updated in the decoding process while the information of the sub-reliability nodes will be optimized in different situations, thereby improving the decoding accuracy. This algorithm can accelerate the convergence of the algorithm, while the reduced number of update nodes can control the increase of the computation to a certain extent, and maintain the advantage of the low computation quantity of EMS algorithm. Through simulation results and computational analysis, we prove that the improved algorithm has significantly improved error correction performance compared with the traditional EMS algorithm. Under the same conditions, it has completely entered the waterfall area at 3.6dB, and the EMS algorithm has not entered the waterfall area. By comparing the average iteration times of decoding of the two algorithms, the convergence speed of the algorithm can be reflected. Compared with EMS algorithm, the improved algorithm has significantly improved the convergence speed in most areas, which indicates that the processing of sub-reliable nodes accelerates the convergence of the algorithm. Finally, the average update times of the check nodes and variable nodes of the two algorithms are compared respectively. Although the improved algorithm introduces extra processing flow, it speeds up the convergence of the algorithm, and the computational cost of decoding is less expensive.

IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC)•Sep 1, 2019•0 citations

Improved Min-sum Algorithm with Low Complexity for Decoding LDPC Codes

Wu Ju

In order to reduce the performance loss of SMMS( Single-Minimum,Min-Sum) algorithm for decoding LDPC( LowDensity Parity-Check) codes,the ACMS( adaptive configurable Min-Sum) algorithm is proposed in this paper. Only the absolute minimum is used in this algorithm to update the check-node message and the second minimum is determined by a configurable estimation weight factor based on the iteration and a correction factor based on the number of error bits in each hard decision. Compared to the original SMMS and VWMS algorithm,the simulation result shows the proposed ACMS algorithm reduce the performance loss in general by introducing only a little additional complexity.

Video Engineering•Jan 1, 2015•0 citations

An Improved Offset Min-Sum LDPC Decoding Algorithm for 5G New Radio

Bich Ngoc Tran-Thi, Truong Nguyen, Hoa Nguyen Hong +1 more

This paper presents an Improved Offset Min-Sum (IOMS) algorithm for Low-Density Parity-Check (LDPC) decoders, used in 5thgeneration (5G) mobile communication. The proposed algorithm IOMS applies a multiplication factor to check-node update information messages. The simulation results show that the proposed IOMS algorithm offers a better error correction performance than the existing Min-Sum-based algorithms at the expense of a slight increase in complexity. At the BER of 10–5, the IOMS achieves a decoding gain of 0.1dB with respect to the OMS algorithm.

•Apr 15, 2021•10 citations

A Self-Correcting Minimum Sum Algorithm with Dual Reliability

Zhaochuan Wei, Xiaoqian Zhou, Yuanfa Ji +1 more

In order to improve the performance and reliability of Low-Density Parity-Check (LDPC) code decoding algorithms, this paper proposes a Self-Correcting Minimum Sum algorithm with Dual Reliability Determination (DRD-SCMS) to address the limitations of the Self-Correcting Minimum Sum (SCMS) algorithm in determining the reliability of variable nodes. Unlike the traditional SCMS algorithm, which relies solely on the symbols from the previous and current iterations to assess the reliability of variable nodes, the DRD-SCMS algorithm combines the number of connections between variable nodes and check nodes with check failures, along with the results of symbol iterations, to provide a more accurate reliability evaluation. By categorizing variable nodes into three groups and prioritizing the updating of the most unreliable nodes, the algorithm optimizes the iteration order in the decoding process. Simulation results demonstrate that the DRD-SCMS algorithm significantly reduces the number of iterations and improves decoding efficiency compared to the SCMS algorithm, offering better applicability and reliability.

International Conference on Sensors and Information Technology (ICSI)•Mar 21, 2025•0 citations
Low-Density Parity-Check (LDPC) code is a linear block code approaching the Shannon limit. The improvement of the decoding performance of LDPC codes is a hot research direction today. To improve the decoding performance of the self-correcting min-sum (SCMS) algorithm, an improved self-correcting min-sum (ISCMS) algorithm is proposed based on this algorithm. In the iterative decoding process of the ISCMS algorithm, the update rule of the check node (CN) is optimized to the update rule of the CN of the IOMS algorithm, which improves the accuracy of decoding from the processing of the CN. Simulation results show that the proposed ISCMS algorithm has better error performance and convergence performance than the SCMS algorithm with a small increase in computational complexity.
Abstract ― Low Density Parity Check (LDPC) code approaches Shannon–limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.
Low Density Parity Check (LDPC) approaches Shannon -limit performance for binary field and long code lengths. However performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding, while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.
Low Density Parity Check (LDPC) codes have been widely applied in recent years, and Extended Min-Sum (EMS) decoding algorithm is the most practical algorithm, which has a fairly low computational cost. Due to the great loss in error correction performance of EMS algorithm, in this paper, we establish constraints to obtain reliable nodes while decoding and evaluate the reliability of the nodes. The nodes and information transferred between nodes are processed and optimized according to the reliability of the nodes. High reliability nodes will not be updated in the decoding process while the information of the sub-reliability nodes will be optimized in different situations, thereby improving the decoding accuracy. This algorithm can accelerate the convergence of the algorithm, while the reduced number of update nodes can control the increase of the computation to a certain extent, and maintain the advantage of the low computation quantity of EMS algorithm. Through simulation results and computational analysis, we prove that the improved algorithm has significantly improved error correction performance compared with the traditional EMS algorithm. Under the same conditions, it has completely entered the waterfall area at 3.6dB, and the EMS algorithm has not entered the waterfall area. By comparing the average iteration times of decoding of the two algorithms, the convergence speed of the algorithm can be reflected. Compared with EMS algorithm, the improved algorithm has significantly improved the convergence speed in most areas, which indicates that the processing of sub-reliable nodes accelerates the convergence of the algorithm. Finally, the average update times of the check nodes and variable nodes of the two algorithms are compared respectively. Although the improved algorithm introduces extra processing flow, it speeds up the convergence of the algorithm, and the computational cost of decoding is less expensive.
In order to reduce the performance loss of SMMS( Single-Minimum,Min-Sum) algorithm for decoding LDPC( LowDensity Parity-Check) codes,the ACMS( adaptive configurable Min-Sum) algorithm is proposed in this paper. Only the absolute minimum is used in this algorithm to update the check-node message and the second minimum is determined by a configurable estimation weight factor based on the iteration and a correction factor based on the number of error bits in each hard decision. Compared to the original SMMS and VWMS algorithm,the simulation result shows the proposed ACMS algorithm reduce the performance loss in general by introducing only a little additional complexity.
This paper presents an Improved Offset Min-Sum (IOMS) algorithm for Low-Density Parity-Check (LDPC) decoders, used in 5thgeneration (5G) mobile communication. The proposed algorithm IOMS applies a multiplication factor to check-node update information messages. The simulation results show that the proposed IOMS algorithm offers a better error correction performance than the existing Min-Sum-based algorithms at the expense of a slight increase in complexity. At the BER of 10–5, the IOMS achieves a decoding gain of 0.1dB with respect to the OMS algorithm.
In order to improve the performance and reliability of Low-Density Parity-Check (LDPC) code decoding algorithms, this paper proposes a Self-Correcting Minimum Sum algorithm with Dual Reliability Determination (DRD-SCMS) to address the limitations of the Self-Correcting Minimum Sum (SCMS) algorithm in determining the reliability of variable nodes. Unlike the traditional SCMS algorithm, which relies solely on the symbols from the previous and current iterations to assess the reliability of variable nodes, the DRD-SCMS algorithm combines the number of connections between variable nodes and check nodes with check failures, along with the results of symbol iterations, to provide a more accurate reliability evaluation. By categorizing variable nodes into three groups and prioritizing the updating of the most unreliable nodes, the algorithm optimizes the iteration order in the decoding process. Simulation results demonstrate that the DRD-SCMS algorithm significantly reduces the number of iterations and improves decoding efficiency compared to the SCMS algorithm, offering better applicability and reliability.