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

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY

An Improvement on the Min-sum Algorithm for Low-rate Protograph LDPC Codes

Ming Jiang, Chen Wang

Published Dec 10, 2010• 0 citations
Publisher

Abstract

低密度奇偶校验(LDPC)码的译码硬件实现方案大多采用计算复杂度较低的修正最小和(NMS)算法,然而对于低码率LDPC码,由于校验节点度数低,NMS算法的修正误差较大,导致其译码性能和标准的置信传播(BP)算法相比有较大差异。该文针对基于原图构造的一类低码率LDPC码,提出了在NMS迭代译码中结合震荡抵消(OSC)处理和多系数(MF)修正技术的方案。结合低码率原型图LDPC码行重分布差异较大的特点,MF修正算法可以有效地减少计算误差,从而改善译码性能。另外低码率原型图LDPC码的收敛较慢,而OSC处理则可以较好地抑制正反馈信息,进一步提高NMS算法的性能增益。仿真结果表明,对于此类低码率LDPC码, MF-OSC-NMS算法可以达到接近BP算法的性能。OSC处理和MF修正技术硬件实现简单,与NMS算法相比几乎没有增加计算复杂度,因此MF-OSC-NMS算法是译码算法复杂度和性能之间一个较好的折中处理方案。

DOI
10.3724/sp.j.1146.2009.01652

Related papers

An improved normalized min-sum algorithm for LDPC codes

Jinlei Chen, Yan Zhang, Ruiyi Sun

An improved normalized min-sum (IN-MS) algorithm is proposed for decoding low-density parity-check (LDPC) codes. In this algorithm, two normalized factors are used in check node computing, one for the minimum data and another for the second minimum data, and this algorithm achieves a better approximation to the BP algorithm. Simulation results show that the decoding performance of IN-MS algorithm is better than that of the normalized min-sum (NMS) algorithm.

IEEE/ACIS International Conference on Computer and Information Science (ICIS)•Jun 1, 2013•9 citations

Novel modified min-sum decoding algorithm for low-density parity-check codes

Liu, Haiyang, Qu +7 more

改进低密度的同等值检查(LDPC ) 译码编码的 min 和的表演的问题在这份报纸被考虑。把算法基于 min 和,为 LDPC 代码译码算法的新奇修改 min 和被建议。如果他们的符号是不同的,建议算法由平均新消息和以前的消息在重复进程修改可变节点消息。与标准 min 和算法相比,修正随仅仅复杂性的小增加被完成,但是显著地改善为常规、不规则的 LDPC 代码译码性能。模拟结果证明我们译码算法修改的性能离为中等长度 LDPC 代码的标准和产品算法的很靠近。

中国邮电高校学报:英文版•Jan 1, 2010•1 citation

IMPROVED MIN-SUM DECODING ALGORITHM FOR IRREGULAR LDPC CODES

А В Башкиров, Свиридова Ирина Владимировна, Т.Д. Ижокина +3 more

Aналитический подход к определению оптимальной функции постобработки для минимальной операции в алгоритме MIN-SUM, ранее полученный для обычных кодов проверки на четность с низкой плотностью (LDPC-коды), распространяется на нерегулярные коды LDPC. Оптимальное выражение постобработки для нестандартного случая варьируется от одного контрольного узла к другому, а также от одной итерации к следующей. Для практического использования необходимо аппроксимировать эту оптимальную функцию. В отличие от обычного случая, когда можно было бы использовать уникальную функцию постобработки на протяжении всего процесса декодирования без потери производительности битовых ошибок, для нерегулярных кодов критически важно варьировать постобработку от одной итерации к следующей, чтобы добиться хорошей производительности. С использованием этого подхода было выявлено, что качество битовых ошибок от алгоритма распространения доверия соответствует улучшению на 1 дБ по сравнению с MIN-SUM алгоритмом без постобработки. Сначала будет представлен обзор подхода и представлена аналитическая основа для оптимальной постобработки. Далее будет представлена оптимальная функция постобработки для нерегулярных кодов и обсуждены возможные упрощения. И наконец, показаны результаты моделирования и преимущества аппроксимации We extended an analytical approach to determining the optimal post-processing function for the minimum operation in the MIN-SUM algorithm, previously obtained for conventional low density parity check codes (LDPC codes), to irregular LDPC codes. The optimal post-processing expression for the non-standard case varies from one control node to another, as well as from one iteration to the next. For practical use, it is necessary to approximate this optimal function. Unlike the usual case where one could use a unique post-processing function throughout the entire decoding process without sacrificing bit code performance, it is critical for irregular codes to distinguish post-processing from one iteration to the next in order to achieve good performance. Using this approach, we found that the quality of bit errors from the trust algorithm corresponds to an improvement of 1 level compared to the MIN-SUM algorithm without post-processing. First, we provide an overview and analytical framework for optimal post-processing. Then, we present the optimal post-processing function for irregular codes and discuss possible simplifications. Finally, we show the simulation results and the benefits of the approximation

Вестник Воронежского государственного технического университета•Nov 19, 2021•0 citations

Analysis of QC-LDPC codes in IEEE 802.16e

Xin Lu

Quasi Cyclic Low Density Parity Check(QC-LDPC) code is an important branch of LDPC code research.It is a kind of constructed LDPC code with check matrix H constructed by quasi cyclic method.IEEE 802.16e proposes a parity check matrix with quasi cyclic character which makes the encoding and hardware easy to be implemented.This article will mainly introduce the encoding process and analyze the decoding performance of various decoding algorithms.Simulations show that Min-Sum(MS) algorithm compares to the Log-BP performance has approximately 0.5dB loss for high-speed rate coding.And the performance of Normalized Min-Sum(NMS) and Row Weight Matched Min-Sum(Rwm-MS) with relatively low complexity is nearly the same as that of Log-BP algorithm.

Journal of Shenzhen Institute of Information Technology•Jan 1, 2009•0 citations

Improving Performance of the Min Sum Algorithm for LDPC Codes

Abdelilah Kadi, Saïd Najah, Mostafa Mrabti +1 more

Lecture notes in electrical engineering•Jan 1, 2016•0 citations

Low Complexity Neural Network-Aided NMS LDPC Decoder

Honghao Shi, Mingyang Zhong, Zhiyong Luo +1 more

For recent years, deep learning has been studied and utilized among decoders in wireless and optical communication. For low density parity check (LDPC) codes, most works use neural network (NN) to implement the belief propagation (BP) decoder and unfold its iterations into layers activated by different functions. However, the whole decoder composed by neural network performs lots of non-linear operations, which are time consuming and unfriendly to hardware implementation. In this work, a neural network-aided normalized min-sum (NN-aided NMS) decoder for LDPC code is proposed, in which the normalization factors are inferred from input log likelihood ratio (LLR) by apre-trained NN, while maintaining check and variable nodes processing the same as in min-sum (MS) decoder. In order to test the proposed decoder with irregular LDPC code, a rate-7/8 one was constructed using progressive edge growth (PEG) algorithm, showing better error correction capability than the standard of consultative committee for space data systems (CCSDS) LDPC (8176,7154) code by 0.08 dB with the same rate. Simulation results show that, NN-aided NMS decoder outperforms MS decoder by 0.3 dB, and its performance approaches BP decoder with a gap of 0.02 dB, which is 50% closer than the best NMS decoder using 0.7 as the normalization factor. The proposed decoder has good performance for both regular and irregular LDPC codes, and needs much less computation complexity than BP decoder. It introduces more linear computation than NMS decoder but achieves better bit error rate (BER) performance, thus becoming a good complement between BP and NMS decoder when trading off among complexity, error correction performance and hardware implementation for wireless and satellite communication.

Computing, Communications and IoT Applications (ComComAp)•Nov 26, 2021•8 citations
低密度奇偶校验(LDPC)码的译码硬件实现方案大多采用计算复杂度较低的修正最小和(NMS)算法,然而对于低码率LDPC码,由于校验节点度数低,NMS算法的修正误差较大,导致其译码性能和标准的置信传播(BP)算法相比有较大差异。该文针对基于原图构造的一类低码率LDPC码,提出了在NMS迭代译码中结合震荡抵消(OSC)处理和多系数(MF)修正技术的方案。结合低码率原型图LDPC码行重分布差异较大的特点,MF修正算法可以有效地减少计算误差,从而改善译码性能。另外低码率原型图LDPC码的收敛较慢,而OSC处理则可以较好地抑制正反馈信息,进一步提高NMS算法的性能增益。仿真结果表明,对于此类低码率LDPC码, MF-OSC-NMS算法可以达到接近BP算法的性能。OSC处理和MF修正技术硬件实现简单,与NMS算法相比几乎没有增加计算复杂度,因此MF-OSC-NMS算法是译码算法复杂度和性能之间一个较好的折中处理方案。
改进低密度的同等值检查(LDPC ) 译码编码的 min 和的表演的问题在这份报纸被考虑。把算法基于 min 和,为 LDPC 代码译码算法的新奇修改 min 和被建议。如果他们的符号是不同的,建议算法由平均新消息和以前的消息在重复进程修改可变节点消息。与标准 min 和算法相比,修正随仅仅复杂性的小增加被完成,但是显著地改善为常规、不规则的 LDPC 代码译码性能。模拟结果证明我们译码算法修改的性能离为中等长度 LDPC 代码的标准和产品算法的很靠近。
Aналитический подход к определению оптимальной функции постобработки для минимальной операции в алгоритме MIN-SUM, ранее полученный для обычных кодов проверки на четность с низкой плотностью (LDPC-коды), распространяется на нерегулярные коды LDPC. Оптимальное выражение постобработки для нестандартного случая варьируется от одного контрольного узла к другому, а также от одной итерации к следующей. Для практического использования необходимо аппроксимировать эту оптимальную функцию. В отличие от обычного случая, когда можно было бы использовать уникальную функцию постобработки на протяжении всего процесса декодирования без потери производительности битовых ошибок, для нерегулярных кодов критически важно варьировать постобработку от одной итерации к следующей, чтобы добиться хорошей производительности. С использованием этого подхода было выявлено, что качество битовых ошибок от алгоритма распространения доверия соответствует улучшению на 1 дБ по сравнению с MIN-SUM алгоритмом без постобработки. Сначала будет представлен обзор подхода и представлена аналитическая основа для оптимальной постобработки. Далее будет представлена оптимальная функция постобработки для нерегулярных кодов и обсуждены возможные упрощения. И наконец, показаны результаты моделирования и преимущества аппроксимации We extended an analytical approach to determining the optimal post-processing function for the minimum operation in the MIN-SUM algorithm, previously obtained for conventional low density parity check codes (LDPC codes), to irregular LDPC codes. The optimal post-processing expression for the non-standard case varies from one control node to another, as well as from one iteration to the next. For practical use, it is necessary to approximate this optimal function. Unlike the usual case where one could use a unique post-processing function throughout the entire decoding process without sacrificing bit code performance, it is critical for irregular codes to distinguish post-processing from one iteration to the next in order to achieve good performance. Using this approach, we found that the quality of bit errors from the trust algorithm corresponds to an improvement of 1 level compared to the MIN-SUM algorithm without post-processing. First, we provide an overview and analytical framework for optimal post-processing. Then, we present the optimal post-processing function for irregular codes and discuss possible simplifications. Finally, we show the simulation results and the benefits of the approximation
Quasi Cyclic Low Density Parity Check(QC-LDPC) code is an important branch of LDPC code research.It is a kind of constructed LDPC code with check matrix H constructed by quasi cyclic method.IEEE 802.16e proposes a parity check matrix with quasi cyclic character which makes the encoding and hardware easy to be implemented.This article will mainly introduce the encoding process and analyze the decoding performance of various decoding algorithms.Simulations show that Min-Sum(MS) algorithm compares to the Log-BP performance has approximately 0.5dB loss for high-speed rate coding.And the performance of Normalized Min-Sum(NMS) and Row Weight Matched Min-Sum(Rwm-MS) with relatively low complexity is nearly the same as that of Log-BP algorithm.
For recent years, deep learning has been studied and utilized among decoders in wireless and optical communication. For low density parity check (LDPC) codes, most works use neural network (NN) to implement the belief propagation (BP) decoder and unfold its iterations into layers activated by different functions. However, the whole decoder composed by neural network performs lots of non-linear operations, which are time consuming and unfriendly to hardware implementation. In this work, a neural network-aided normalized min-sum (NN-aided NMS) decoder for LDPC code is proposed, in which the normalization factors are inferred from input log likelihood ratio (LLR) by apre-trained NN, while maintaining check and variable nodes processing the same as in min-sum (MS) decoder. In order to test the proposed decoder with irregular LDPC code, a rate-7/8 one was constructed using progressive edge growth (PEG) algorithm, showing better error correction capability than the standard of consultative committee for space data systems (CCSDS) LDPC (8176,7154) code by 0.08 dB with the same rate. Simulation results show that, NN-aided NMS decoder outperforms MS decoder by 0.3 dB, and its performance approaches BP decoder with a gap of 0.02 dB, which is 50% closer than the best NMS decoder using 0.7 as the normalization factor. The proposed decoder has good performance for both regular and irregular LDPC codes, and needs much less computation complexity than BP decoder. It introduces more linear computation than NMS decoder but achieves better bit error rate (BER) performance, thus becoming a good complement between BP and NMS decoder when trading off among complexity, error correction performance and hardware implementation for wireless and satellite communication.