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International Conference on Communications and Broadband Networking (ICCBN)

A modified Self-Corrected Min-Sum LDPC Decoding Algorithm

Ruizhen Wu, Lin Wang, Mingming Wang, Xiaoyong Zhang

Published Apr 12, 2019• 1 citation
Publisher

Abstract

A modified self-corrected min-sum LDPC decoding is proposed in this paper. The proposed method introduces parameters to improve BER performance and few modifications to reduce the iteration number. The simulation results show that the proposed method can achieve at most 93.6% improvement of BER compare with the original self-corrected min-sum decoding algorithm and 99% improvement compare with the min-sum decoding algorithm. The iteration number can be reduced by up to 102.6% compare with the min-sum decoding algorithm and 67.5% compare with the original self-corrected min-sum decoding algorithm. The proposed method has advantages in both BER and hardware implementation, which suits for different working environments.

DOI
10.1145/3330180.3330183

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A modified self-corrected min-sum LDPC decoding is proposed in this paper. The proposed method introduces parameters to improve BER performance and few modifications to reduce the iteration number. The simulation results show that the proposed method can achieve at most 93.6% improvement of BER compare with the original self-corrected min-sum decoding algorithm and 99% improvement compare with the min-sum decoding algorithm. The iteration number can be reduced by up to 102.6% compare with the min-sum decoding algorithm and 67.5% compare with the original self-corrected min-sum decoding algorithm. The proposed method has advantages in both BER and hardware implementation, which suits for different working environments.
Min-sum algorithm(MSA) and its modification are used in LDPC decoder implementations because of its error performance and low computation complexity. In this paper a modified min-sum LDPC decoding algorithm through approximation enhancement using the smallest three minimum values is presented. The proposed algorithm improves error performance and reduces decoder iterations. From the simulation results for 5G NR LDPC codes, we show that the proposed algorithm can achieve about 1.6dB improvement of FER compared to the min-sum decoding algorithm and provide about 10% reduction of the average iteration numbers compare to NMSA with optimum normalization factor.
In this paper a modified Offset Min-Sum decoding algorithm for Low-Density Parity Check Codes is presented. In this modified algorithm, the offset factor in the original Offset Min-Sum algorithm is adjusted iteratively with the assistance of the check-node computation outputs of the Normalized Min-Sum algorithm. As a result, the offset factor in our modified algorithm can be calculated adaptively and more efficiently according to the previous decoding procedure, which can minimize the decoding degradation compared with the Belief Propagation decoding algorithm. The simulation results show that, compared with the original Offset Min-Sum decoding algorithm, our modified algorithm can achieve noticeable performance improvement with minor extra hardware complexity. For example, when BER is 10−5, our algorithm can achieve 0.1dB and 0.2dB decoding gain over Offset Min-Sum algorithm for regular and irregular LDPC codes respectively.
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.
A check nodes correction approximate Min-sum decoding algorithm for LDPC is proposed in this paper. The degradation factor of BP to MS is found to do expansion and optimization based on hardware working manner. The decoding algorithm summarized a correction approximate algorithm based on different SNR situations and hardware implementation. The simulation is based on LDPC NR 3GPP 38.212 release and the comparison results showed the proposed check nodes correction approximate Min-sum decoding algorithm can have a better BER and iteration time performance. The hardware of this proposed algorithm is based on Min-sum decoder and the extra cost is only a shifter and an adder besides the memory.
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.
Decoding algorithms with strong practical value not only have good decoding performance, but also have the computation complexity as low as possible. For this purpose, the paper points out the modified min-sum decoding algorithm(M-MSA). On the condition of no increasing in the decoding complexity, it makes the error-correcting performance improved by adding the appropriate scaling factor based on the min-sum algorithm(MSA), and it is very suitable for hardware implementation. Simulation results show that this algorithm has good BER performance, low complexity and low hardware resource utilization, and it would be well applied in the future.