Improved Algorithm Based on Confidence Propagation Decoding Algorithm

Low-density parity-check codes(LDPC) have the advantages of high performance, flexibility, parallelized processing and iterative decoding, which make them one of the widely used error correction code schemes in communication and storage systems. In order to improve the decoding convergence speed of...

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Bibliographic Details
Published in2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) pp. 604 - 607
Main Authors Li, Xiuyang, Tang, Yanfeng, Zhang, Cheng, Wang, Xiuzhuo
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.06.2023
Subjects
component
Decoding
Error correction
Error correction codes
Information technology
iterative decoding
layered decoding
Low-density parity-check codes
Modulation
Seminars
signal-to-noise ratio
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Summary:Low-density parity-check codes(LDPC) have the advantages of high performance, flexibility, parallelized processing and iterative decoding, which make them one of the widely used error correction code schemes in communication and storage systems. In order to improve the decoding convergence speed of low-density parity-check codes, the idea of layered decoding is added to the log-likelihood ratio confidence propagation decoding algorithm, and an improved algorithm based on layered decoding strategy is proposed, which solves the problem of low decoding rate caused by vertical iteration after the completion of horizontal iteration by using the log-likelihood ratio confidence propagation decoding algorithm in each layer, at the same time. The results show that the average decoding rate of the algorithm is improved by about 25.5% compared with that before the improvement without losing or almost losing the error correction performance, and the performance of the improved layered decoding algorithm with log-likelihood ratio confidence propagation decoding is optimal when the signal-to-noise ratio is greater than 4, and the decoding performance is improved.
DOI:10.1109/AINIT59027.2023.10212630