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 Zhejiang University(Engineering Science)

Improved decoding approach of low-density parity-check codes with finite precision

Kuang Wang

Published Jan 1, 2008• 0 citations
OpenAlex

Abstract

An improved parallel decoding approach of sum-product algorithm for low-density parity-check codes(LDPC) was presented in order to solve the problem of divergence in decoding iteration with finite precision,.In addition,the reason for improving the convergence speed was discussed detailedly and the pipeline structure was provided.By eliminating the unexpected state in which operation data didn't satisfy input rules due to the finite precision effect thus could not be preceded correctly,the iteration convergence was improved efficiently and led to decoding with no error.Compared with the original parallel approach,this improvement don't involve too much control logic and computational complexity.Simulation shows that the proposed decoding approach over additive white Gaussian noise(AWGN) channel achieves good performance,especially at high code rate,it suffers negligible performance loss compared with the forward-backward approach while reducing the operation complexity effectively.

Related papers

Improved Decoding Algorithm of LDPC Codes Based on Integer Operation

MA Huimia

In this paper,several conventional decoding algorithms of LDPC(Low-Density Parity-Check) codes over Additive White Gaussian Noise(AWGN) channels are investigated,and an improved decoding algorithm based on integer operation is proposed. Compared to normalized min-sum algorithm,a modified factor to improve the decoding performance is designed according to the states of hard decision in each iteration. It is convenient for hardware implementation with integer operation.The simulation results show that the algorithm performs better than LLR BP decoding algorithm in 2.1 dB and decodes fast with low complexity.

Video Engineering•Jan 1, 2013•0 citations

Differential Decoding of Low-Density Parity-Check Codes Based on Min Sum Algorithm

Shizhang Peng, Zemao Zhao, Jianrong Bao

To improve performance and reduce computation complexity of the low-density parity-check (LDPC) codes, a min-sum based differential decoding algorithm is proposed. The main principle is that the messages passing in the bipartite graph of the codes are the probability differences and the updates of the check nodes adopt min sum algorithm. Simulation results show that the proposed algorithm can achieve about 0.4dB performance gain over the UMP BP-Based decoding algorithm at bit error rate (BER) of under an additive white Gaussian noise (AWGN) channel. And it can obtain good performance very close to that of the BP decoding algorithm with much lower complexity.

International Symposium on Information Science and Engineering•Dec 1, 2010•2 citations

Improved LDPC decoding algorithms based on min-sum algorithm

Y.V. A. C. Kumara, Chandika Bandara Wavegedara

Low-Density Parity check (LDPC) codes offer high-performance error correction near the Shannon limit which employs large code lengths and some iterations in the decoding process. The conventional decoding algorithm of LDPC is the Log Likelihood Ratio based Belief Propagation (LLR BP) which is also known as the `Sum-Product algorithm' which gives the best decoding performance and requires the most computational complexity and implementations with increased hardware complexity. Another simpler variant of this algorithm is used which is known as `min-sum algorithm' which reduces computational complexity as well as hardware complexity but with reduced accuracy. This paper analyzes the reason min-sum algorithm is more prone to errors when compared to the sum-product algorithm, and puts forward two improved algorithms which improve the performance of the min-sum algorithm with comparable algorithmic complexity.

Moratuwa Engineering Research Conference (MERCon)•Apr 1, 2016•8 citations

Fast Convergence Decoding Scheme for LDPC Codes

Lin Zhi-guo

We consider the convergence problem of the conventional decoding scheme of Low-Density Parity-Check(LDPC) codes over Additive White Gaussian Noise(AWGN) channels.In this correspondence,we propose a fast convergence decoding scheme based on quantization,which is a comprehensive method of taking advantage of the Layered Belief Propagation(LBP) algorithm,the Offset Min-Sum(OMS) algorithm and the quantization method.Compared to the conventional quantized Min-Sum algorithm,the proposed scheme efficiently reduces the decoding complexity and significantly accelerates the decoding convergence with no additional performance loss.We show that with simulations.

Fire Control and Command Control•Jan 1, 2011•0 citations

Improved layered decoding algorithm for LDPC codes based on integer operation

MA Hui-miao

Based on the investigation of the low-density parity-check(LDPC) codes' layered decoding algorithm in additive white Gaussian noise(AWGN) channel,an improved layered decoding algorithm based on integer operation is proposed for decoding LDPC codes.All the variables are indicated as integers in the proposed algorithm,so it is very convenient for implementation in hardware.At the same time,a modified factor is introduced to improve the layered decoding algorithm.With this modified factor,its decoding performance is improved greatly.Simulation results in AWGN channel show that the improved layered decoding algorithm can not only reduce the computation complexity and accelerate the convergence rate,but also have a lower error floor.

Systems engineering and electronics•Jan 1, 2013•0 citations

Düşük yoğunluklu eşlik denetim kodları için kod çözme algoritmalarının karşılaştırılması.

Kolaylı, Mert

Low-density parity-check (LDPC) codes are a subclass of linear block codes. These codes have parity-check matrices in which the ratio of the non-zero elements to all elements is low. This property is exploited in defining low complexity decoding algorithms. Low-density parity-check codes have good distance properties and error correction capability near Shannon limits. In this thesis, the sum-product and the bit-flip decoding algorithms for low-density parity-check codes are implemented on Intel Pentium M 1,86 GHz processor using the software called MATLAB. Simulations for the two decoding algorithms are made over additive white gaussian noise (AWGN) channel changing the code parameters like the information rate, the blocklength of the code and the column weight of the parity-check matrix. Performance comparison of the two decoding algorithms are made according to these simulation results. As expected, the sum-product algorithm, which is based on soft-decision decoding, outperforms the bit-flip algorithm, which depends on hard-decision decoding. Our simulations show that the performance of LDPC codes improves with increasing blocklength and number of iterations for both decoding algorithms. Since the sum-product algorithm has lower error-floor characteristics, increasing the number of iterations is more effective for the sum-product decoder compared to the bit-flip decoder. By having better BER performance for lower information rates, the bit-flip algorithm performs according to the expectations; however, the performance of the sum-product decoder deteriorates for information rates below 0.5 instead of improving. By irregular construction of LDPC codes, a performance improvement is observed especially for low SNR values.

•Jan 1, 2006•0 citations
An improved parallel decoding approach of sum-product algorithm for low-density parity-check codes(LDPC) was presented in order to solve the problem of divergence in decoding iteration with finite precision,.In addition,the reason for improving the convergence speed was discussed detailedly and the pipeline structure was provided.By eliminating the unexpected state in which operation data didn't satisfy input rules due to the finite precision effect thus could not be preceded correctly,the iteration convergence was improved efficiently and led to decoding with no error.Compared with the original parallel approach,this improvement don't involve too much control logic and computational complexity.Simulation shows that the proposed decoding approach over additive white Gaussian noise(AWGN) channel achieves good performance,especially at high code rate,it suffers negligible performance loss compared with the forward-backward approach while reducing the operation complexity effectively.
In this paper,several conventional decoding algorithms of LDPC(Low-Density Parity-Check) codes over Additive White Gaussian Noise(AWGN) channels are investigated,and an improved decoding algorithm based on integer operation is proposed. Compared to normalized min-sum algorithm,a modified factor to improve the decoding performance is designed according to the states of hard decision in each iteration. It is convenient for hardware implementation with integer operation.The simulation results show that the algorithm performs better than LLR BP decoding algorithm in 2.1 dB and decodes fast with low complexity.
To improve performance and reduce computation complexity of the low-density parity-check (LDPC) codes, a min-sum based differential decoding algorithm is proposed. The main principle is that the messages passing in the bipartite graph of the codes are the probability differences and the updates of the check nodes adopt min sum algorithm. Simulation results show that the proposed algorithm can achieve about 0.4dB performance gain over the UMP BP-Based decoding algorithm at bit error rate (BER) of under an additive white Gaussian noise (AWGN) channel. And it can obtain good performance very close to that of the BP decoding algorithm with much lower complexity.
Low-Density Parity check (LDPC) codes offer high-performance error correction near the Shannon limit which employs large code lengths and some iterations in the decoding process. The conventional decoding algorithm of LDPC is the Log Likelihood Ratio based Belief Propagation (LLR BP) which is also known as the `Sum-Product algorithm' which gives the best decoding performance and requires the most computational complexity and implementations with increased hardware complexity. Another simpler variant of this algorithm is used which is known as `min-sum algorithm' which reduces computational complexity as well as hardware complexity but with reduced accuracy. This paper analyzes the reason min-sum algorithm is more prone to errors when compared to the sum-product algorithm, and puts forward two improved algorithms which improve the performance of the min-sum algorithm with comparable algorithmic complexity.
We consider the convergence problem of the conventional decoding scheme of Low-Density Parity-Check(LDPC) codes over Additive White Gaussian Noise(AWGN) channels.In this correspondence,we propose a fast convergence decoding scheme based on quantization,which is a comprehensive method of taking advantage of the Layered Belief Propagation(LBP) algorithm,the Offset Min-Sum(OMS) algorithm and the quantization method.Compared to the conventional quantized Min-Sum algorithm,the proposed scheme efficiently reduces the decoding complexity and significantly accelerates the decoding convergence with no additional performance loss.We show that with simulations.
Based on the investigation of the low-density parity-check(LDPC) codes' layered decoding algorithm in additive white Gaussian noise(AWGN) channel,an improved layered decoding algorithm based on integer operation is proposed for decoding LDPC codes.All the variables are indicated as integers in the proposed algorithm,so it is very convenient for implementation in hardware.At the same time,a modified factor is introduced to improve the layered decoding algorithm.With this modified factor,its decoding performance is improved greatly.Simulation results in AWGN channel show that the improved layered decoding algorithm can not only reduce the computation complexity and accelerate the convergence rate,but also have a lower error floor.
Low-density parity-check (LDPC) codes are a subclass of linear block codes. These codes have parity-check matrices in which the ratio of the non-zero elements to all elements is low. This property is exploited in defining low complexity decoding algorithms. Low-density parity-check codes have good distance properties and error correction capability near Shannon limits. In this thesis, the sum-product and the bit-flip decoding algorithms for low-density parity-check codes are implemented on Intel Pentium M 1,86 GHz processor using the software called MATLAB. Simulations for the two decoding algorithms are made over additive white gaussian noise (AWGN) channel changing the code parameters like the information rate, the blocklength of the code and the column weight of the parity-check matrix. Performance comparison of the two decoding algorithms are made according to these simulation results. As expected, the sum-product algorithm, which is based on soft-decision decoding, outperforms the bit-flip algorithm, which depends on hard-decision decoding. Our simulations show that the performance of LDPC codes improves with increasing blocklength and number of iterations for both decoding algorithms. Since the sum-product algorithm has lower error-floor characteristics, increasing the number of iterations is more effective for the sum-product decoder compared to the bit-flip decoder. By having better BER performance for lower information rates, the bit-flip algorithm performs according to the expectations; however, the performance of the sum-product decoder deteriorates for information rates below 0.5 instead of improving. By irregular construction of LDPC codes, a performance improvement is observed especially for low SNR values.