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IEEE International Symposium on Information Theory (ISIT)

Improved Redundant Parity-Check Based BP Decoding of LDPC Codes

Irina E. Bocharova, Boris D. Kudryashov, Vitaly Skachek, Yauhen Yakimenka

Published Jun 1, 2018• 3 citations
Publisher

Abstract

A new decoding algorithm for LDPC codes on the AWGN channel is proposed. The algorithm is based on the idea of using redundant parity checks and additional variable nodes. The new key element in the proposed algorithm is the use of the orthogonal subsets of parity checks for computing soft decisions for different variable nodes. This allows for significant improvement in the decoding error performance of the algorithm compared to the known counterparts. Furthermore, new bounds on the error performance of the BP decoding applied to the parity-check matrices with redundant parity-checks are obtained.

DOI
10.1109/isit.2018.8437588

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A new decoding algorithm for LDPC codes on the AWGN channel is proposed. The algorithm is based on the idea of using redundant parity checks and additional variable nodes. The new key element in the proposed algorithm is the use of the orthogonal subsets of parity checks for computing soft decisions for different variable nodes. This allows for significant improvement in the decoding error performance of the algorithm compared to the known counterparts. Furthermore, new bounds on the error performance of the BP decoding applied to the parity-check matrices with redundant parity-checks are obtained.
At present, Low-Density Parity-Check (LDPC) codes widely used in many fields of communications have the best performance of all the Error Correcting Codes (ECC). This paper mainly studies the decoding algorithms of LDPC. It proposes an improved algorithm which is named Check-Variable nodes Hybrid(CVH) algorithm on the basis of the existing algorithms. The CVH algorithm can reduce the computational complexity during the check-node update while overcome with the correlation between the variable-node news in a code with circles. As well as, comparing with the original algorithms the performance of the new one saves 0.1 and 0.3 dB than Log-likelihood Ratios (LLR) Belief Propagation (BP) and BP - based algorithms under Additive White Gaussian Noise (AWGN) channel when the Bit Error Rate (BER) falls to through the simulation. This point shows that this algorithm can increase the decoding performance and reduce the error rate effectively.
Under the condition of the Additive White Gaussian Noise(AWGN) channel,both the hard and soft decision decoding algorithms suitable for LDPC codes are simulated and comparatively analyzed based on analyzing the decoding algorithm for LDPC codes.The variable message correlation for Log-Likelihood-Ratio Belief-Propagation(LLR-BP) algorithm in soft decision algorithm is decreased by introducing a multiplicative correction factor.The simulation results show that the decoding error-correction performance of the modified LLR-BP algorithm is greatly improved with little increase of computing complexity.
To solve the problem of high average running time,an improved decoding algorithm was proposed which was combined by BP algorithm and WBF algorithm.The parity check was added in each BP iterative decoding,and flipping criterions which were passed from check nodes to information nodes were weighted by the reliable information of received sequence.Then a bit flipping measure was to be taken according to specific rules in order to obtain correct codes,and a parity check was added again.Simulation results on Additive White Gaussian Noise(AWGN) channel show that,the improved algorithm can decrease the average running time effectively for LDPC codes while maintaining the excellent decoding performance as BP algorithm.And compared with classical BP algorithm,the effect of the different maximum numbers of iterations,different lengths of codes and different code rates for the improved decoding algorithm are analyzed.
Summary The simplicity of decoding is one of the most important characteristics of the low density parity check (LDPC) codes. Belief propagation (BP) decoding algorithm is a well‐known decoding algorithm for LDPC codes. Most LDPC codes with long lengths have short cycles in their Tanner graphs, which reduce the performance of the BP algorithm. In this paper, we present 2 methods to improve the BP decoding algorithm for LDPC codes. In these methods, the calculation of the variable nodes is controlled by using “multiplicative correction factor” and “additive correction factor.” These factors are obtained for 2 separate channels, namely additive white Gaussian noise (AWGN) and binary symmetric channel (BSC), as 2 functions of code and channel parameters. Moreover, we use the BP‐based method in the calculation of the check nodes, which reduces the required resources. Simulation results show the proposed algorithm has better performance and lower decoding error as compared to BP and similar methods like normalized‐BP and offset‐BP algorithms.