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

Frontiers of Electrical and Electronic Engineering in China

Iterative hybrid decoding algorithm for LDPC codes based on attenuation factor

Minghua Liu, Lijun Zhang

Published May 23, 2012• 2 citations
Publisher
DOI
10.1007/s11460-012-0195-x

Related papers

Attenuated iterative reliability-based decoding algorithm for LDPC codes

Minghua Liu, Lijun Zhang

An attenuated iterative reliability-based majority-logic (AIML) decoding algorithm for low-density parity-check (LDPC) codes is proposed. This novel algorithm is devised based on the orthogonal check-sums of the one-step majority-logic decoding algorithm in conjunction with certain of reliability measures of the received symbols. The computation of reliability measure of the syndrome sum is refined by introducing an attenuation factor. Simulation results show that in binary input-additive white Gaussian noise channel, the AIML algorithm outperforms other popular iterative reliability-based majority-logic decoding algorithms with a slight increase in computational complexity. With maximum iterations five and fifty, the AIML algorithm can achieve almost identical error performance for LDPC codes. No error floor effect can be observed for AIML algorithm down to the bit error rate (BER) of 10−8, while error floor appears for sum product algorithm (SPA) around the BER of 10−7even with maximum iteration 100. The inherent feature of parallel decoding for AIML algorithm enforces the decoding speed in contrast to those serial decoding schemes, such as weighted bit-flipping algorithm.

International Conference on Information Science and Engineering•Dec 1, 2010•0 citations

Hybrid Decoding of LDPC Codes

Ligter, Luca

•Jan 1, 2023•0 citations

Iterative Decoding Algorithm for LDPC Codes based on Difference Map Algorithm

和広 不破, Wadayama Tadashi

IEICE Technical Report; IEICE Tech. Rep.•Sep 14, 2010•0 citations

Iterative hybrid decoding algorithm for LDPC codes based on attenuation factor

Minghua, Liu, Lijun +1 more

为低密度的同等值检查(LDPC ) 译码算法编码的稀释反复的基于可靠性的多数逻辑(AIML ) 被建议，它与译码计划的混血儿相关。算法基于译码算法与一起的一步舞 majoritylogic (OSMLG ) 的直角的校验和被设计收到的符号的可靠性措施肯定。症候群和的可靠性措施的计算被介绍一个变细因素精制。模拟结果显示出那，在二进制输入的添加剂白人 Gaussian 噪音(BI-AWGN ) 隧道，译码算法的 AIML 超过随计算复杂性的细微增加译码算法的另外的流行反复的基于可靠性的多数逻辑(IML ) 。在 5 的最大的重复数字以内， AIML 算法能完成几乎相同的错误性能到和产品算法(矿泉) 。没有错误地板效果能为 AIML 算法被观察击倒到 108 的小点错误率(BER ) ，当错误地板在 BER 附近为矿泉出现时 107 甚至与 100 的最大的重复数字。而且，为 AIML 算法的平行行列的固有的特征与那些连续译码计划相对照强制译码的速度，例如加权扭动小点(WBF ) 算法。

Frontiers of Electrical and Electronic Engineering in China•Jan 1, 2012•0 citations

ON THE ANALYSIS OF SPATIALLY-COUPLED GLDPC CODES AND THE WEIGHTED MIN-SUM ALGORITHM

Yung-Yih Jian

This dissertation studies methods to achieve reliable communication over unreliable channels. Iterative decoding algorithms for low-density parity-check (LDPC) codes and generalized LDPC (GLDPC) codes are analyzed.\n\nA new class of error-correcting codes to enhance the reliability of the communication for high-speed systems, such as optical communication systems, is proposed. The class of spatially-coupled GLDPC codes is studied, and a new iterative hard- decision decoding (HDD) algorithm for GLDPC codes is introduced. The main result is that the minimal redundancy allowed by Shannon’s Channel Coding Theorem can be achieved by using the new iterative HDD algorithm with spatially-coupled GLDPC codes. A variety of low-density parity-check (LDPC) ensembles have now been observed to approach capacity with iterative decoding. However, all of them use soft (i.e., non-binary) messages and a posteriori probability (APP) decoding of their component codes. To the best of our knowledge, this is the first system that can approach the channel capacity using iterative HDD.\n\nThe optimality of a codeword returned by the weighted min-sum (WMS) algorithm, an iterative decoding algorithm which is widely used in practice, is studied as well. The attenuated max-product (AttMP) decoding and weighted min-sum (WMS) decoding for LDPC codes are analyzed. Applying the max-product (and belief- propagation) algorithms to loopy graphs are now quite popular for best assignment problems. This is largely due to their low computational complexity and impressive performance in practice. Still, there is no general understanding of the conditions required for convergence and/or the optimality of converged solutions. This work presents an analysis of both AttMP decoding and WMS decoding for LDPC codes which guarantees convergence to a fixed point when a weight factor, β, is sufficiently small. It also shows that, if the fixed point satisfies some consistency conditions, then it must be both a linear-programming (LP) and maximum-likelihood (ML) decoding solution.

•Aug 7, 2013•5 citations

Iterative Decoding with Bias Terms for LDPC Codes

Hiroshi Kamabe, Shusaku Kobota

IEICE Technical Report; IEICE Tech. Rep.•Dec 31, 2009•0 citations
An attenuated iterative reliability-based majority-logic (AIML) decoding algorithm for low-density parity-check (LDPC) codes is proposed. This novel algorithm is devised based on the orthogonal check-sums of the one-step majority-logic decoding algorithm in conjunction with certain of reliability measures of the received symbols. The computation of reliability measure of the syndrome sum is refined by introducing an attenuation factor. Simulation results show that in binary input-additive white Gaussian noise channel, the AIML algorithm outperforms other popular iterative reliability-based majority-logic decoding algorithms with a slight increase in computational complexity. With maximum iterations five and fifty, the AIML algorithm can achieve almost identical error performance for LDPC codes. No error floor effect can be observed for AIML algorithm down to the bit error rate (BER) of 10−8, while error floor appears for sum product algorithm (SPA) around the BER of 10−7even with maximum iteration 100. The inherent feature of parallel decoding for AIML algorithm enforces the decoding speed in contrast to those serial decoding schemes, such as weighted bit-flipping algorithm.
为低密度的同等值检查(LDPC ) 译码算法编码的稀释反复的基于可靠性的多数逻辑(AIML ) 被建议，它与译码计划的混血儿相关。算法基于译码算法与一起的一步舞 majoritylogic (OSMLG ) 的直角的校验和被设计收到的符号的可靠性措施肯定。症候群和的可靠性措施的计算被介绍一个变细因素精制。模拟结果显示出那，在二进制输入的添加剂白人 Gaussian 噪音(BI-AWGN ) 隧道，译码算法的 AIML 超过随计算复杂性的细微增加译码算法的另外的流行反复的基于可靠性的多数逻辑(IML ) 。在 5 的最大的重复数字以内， AIML 算法能完成几乎相同的错误性能到和产品算法(矿泉) 。没有错误地板效果能为 AIML 算法被观察击倒到 108 的小点错误率(BER ) ，当错误地板在 BER 附近为矿泉出现时 107 甚至与 100 的最大的重复数字。而且，为 AIML 算法的平行行列的固有的特征与那些连续译码计划相对照强制译码的速度，例如加权扭动小点(WBF ) 算法。
This dissertation studies methods to achieve reliable communication over unreliable channels. Iterative decoding algorithms for low-density parity-check (LDPC) codes and generalized LDPC (GLDPC) codes are analyzed.\n\nA new class of error-correcting codes to enhance the reliability of the communication for high-speed systems, such as optical communication systems, is proposed. The class of spatially-coupled GLDPC codes is studied, and a new iterative hard- decision decoding (HDD) algorithm for GLDPC codes is introduced. The main result is that the minimal redundancy allowed by Shannon’s Channel Coding Theorem can be achieved by using the new iterative HDD algorithm with spatially-coupled GLDPC codes. A variety of low-density parity-check (LDPC) ensembles have now been observed to approach capacity with iterative decoding. However, all of them use soft (i.e., non-binary) messages and a posteriori probability (APP) decoding of their component codes. To the best of our knowledge, this is the first system that can approach the channel capacity using iterative HDD.\n\nThe optimality of a codeword returned by the weighted min-sum (WMS) algorithm, an iterative decoding algorithm which is widely used in practice, is studied as well. The attenuated max-product (AttMP) decoding and weighted min-sum (WMS) decoding for LDPC codes are analyzed. Applying the max-product (and belief- propagation) algorithms to loopy graphs are now quite popular for best assignment problems. This is largely due to their low computational complexity and impressive performance in practice. Still, there is no general understanding of the conditions required for convergence and/or the optimality of converged solutions. This work presents an analysis of both AttMP decoding and WMS decoding for LDPC codes which guarantees convergence to a fixed point when a weight factor, β, is sufficiently small. It also shows that, if the fixed point satisfies some consistency conditions, then it must be both a linear-programming (LP) and maximum-likelihood (ML) decoding solution.