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International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

A Layered Improved Multi-Parameter Compensated Min-Sum Decoding Algorithm for LDPC Codes

Xuan Liu, Dongkui Chen

Published Apr 11, 2025• 0 citations
Publisher

Abstract

In modern communication technology, low-density parity-check codes (LDPC) are widely used as an extremely important channel coding method, and it has significant performance advantages when dealing with long codes. However, the complexity of LDPC decoding algorithms is high. In response to this phenomenon, based on MCMS decoding algorithm, this paper proposes an Improved Multi-parameter Compensated MinSum (IMCMS) decoding algorithm. This algorithm adds a threshold near zero at the minimum value and chooses a locally optimal algorithm when the minimum value is less than the threshold to avoid the zero return loss of MCMS decoding algorithm. On this basis, a layered scheduling strategy is applied to propose a Layered Improved Multi-parameter Compensated Min-Sum (LIMCMS) decoding algorithm. Simulation results show that the decoding algorithm proposed in this paper not only effectively improves bit error performance but also maintains low computational complexity and significantly accelerates the convergence speed of decoding in academic papers.

DOI
10.1109/ainit65432.2025.11035794

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In modern communication technology, low-density parity-check codes (LDPC) are widely used as an extremely important channel coding method, and it has significant performance advantages when dealing with long codes. However, the complexity of LDPC decoding algorithms is high. In response to this phenomenon, based on MCMS decoding algorithm, this paper proposes an Improved Multi-parameter Compensated MinSum (IMCMS) decoding algorithm. This algorithm adds a threshold near zero at the minimum value and chooses a locally optimal algorithm when the minimum value is less than the threshold to avoid the zero return loss of MCMS decoding algorithm. On this basis, a layered scheduling strategy is applied to propose a Layered Improved Multi-parameter Compensated Min-Sum (LIMCMS) decoding algorithm. Simulation results show that the decoding algorithm proposed in this paper not only effectively improves bit error performance but also maintains low computational complexity and significantly accelerates the convergence speed of decoding in academic papers.
The Minimum Sum (MS) algorithm is a standard Low-Density Parity-Check Codes (LDPC) decoding algorithm. Compared with traditional algorithms, it uses approximate calculations to reduce the computational complexity of the algorithm when calculating parity nodes, resulting in performance loss issues. To improve the LDPC decoding performance, the LDPC decoding algorithm of Multi-parameter Compensated Min-Sum (MCMS) is proposed. This algorithm introduces relevant information, such as channel and code length, into calculating update verification nodes. Introducing multiplication and correction factors to reduce errors in validation node calculations through multi-parameter compensation. Simulation results show that the MCMS algorithm has superior error rate performance compared with the MS algorithm.
Quasi-cyclic low-density parity-check (QC-LDPC) codes are linear block codes with performance close to the Shannon limit. The layered minimum sum (LMS) decoding algorithm speeds up the decoding convergence by layering the check matrix and updating the nodes according to the layers. However, the MS algorithm has the problem of over estimation and affects the decoding performance due to its simplified strategy of MS algorithm updating the message of check nodes. Therefore, this paper proposes a layered minimum sum decoding algorithm based on overestimation. By setting the correction threshold and updating the check nodes by the conditions, the decoding performance is improved. When the code length is 2048 and the bit error rate is , the proposed algorithm can improve the decoding convergence speed by about 25% and obtain a coding gain of about 0.1 dB.
Low-density parity-check (LDPC) codes have attracted tremendous attention for their excellent error-correction performance and inherent fitness for high-parallelism implementation. With great complexity reduction and friendly for hardware implementation, the min-sum (MS) decoding algorithm for LDPC codes gains widespread applications in practical. In the MS decoding algorithm, finding the first two minima among input variable-to-check messages is the most complex part. To further reduce the complexity of the MS decoding algorithm, an efficient single-minimum (single-min) based layered MS decoding for quasi-cyclic LDPC (QC-LDPC) codes is proposed in this paper. It is found that statistically the difference between the first minimum and second minimum increases as the decoding becomes more convergent. Accordingly, in the proposed single-min scheme, the second minimum is approximately derived based on the first minimum and an index indicating the convergence degree. Besides, some other metrics that are easily obtained are used to refine the second minimum. Simulation results show that the proposed method can significantly improve the error-correction performance and lower the average number of iterations, compared with the state-of-the-art single-min based MS decoding algorithms.
Offset Min-Sum Algorithm (OMSA) and Normalized Min-Sum Algorithm (NMSA) are widely used in commercial LDPC decoders due to low complexity and reasonable performance. In this paper we proposes an Adaptive Layered Offset min- sum algorithm for the decoding of LDPC codes, which utilizes an adaptive offset factor to improve the accuracy of the soft information transferred during the iterative decoding process, and it provides good performance accordingly. The performed simulations showed that the layered offset min-sum decoding for LDPC codes can provide significant improvement compared to existing MS based algorithm, OMSA and NMSA.
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.