Neural Min-Sum Decoding for Generalized LDPC Codes
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초록

In this letter, we investigate the min-sum (MS) and neural MS (NMS) decoding algorithms for generalized low-density parity-check (GLDPC) codes. Although the MS decoder is much simpler than the a posteriori probability (APP) decoder commonly used for GLDPC codes, the MS decoder has not been considered mainly due to its inferior decoding performance. However, we show that the performance can be improved by i) employing the NMS decoding algorithm and ii) optimizing the component parity check matrix (PCM). For the four representative short GLDPC codes in the literature, experimental results show that the NMS decoding performance with the optimized component PCM significantly outperforms the MS decoding performance and even outperforms the APP decoding performance for some cases. IEEE

키워드

CodesDecodingGeneralized low-density parity-check (GLDPC) codeLinear codesMessage passingmin-sum (MS) decodingneural min-sum (NMS) decodingOptimizationPhase change materialsStandards
제목
Neural Min-Sum Decoding for Generalized LDPC Codes
저자
Kwak, H.Kim, J.Kim, Y.Kim, S.No, J.
DOI
10.1109/LCOMM.2022.3208834
발행일
2022-12
유형
Article
저널명
IEEE Communications Letters
26
12
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1 ~ 1