Deep Learning-Based Bit Reliability Based Decoding for Non-binary LDPC Codes

The bit reliability based (BRB) and weighted bit reliability based (wBRB) algorithms are non-binary low-density parity-check (LDPC) code decoding algorithms with an excellent tradeoff between computational complexity and performance. However, the performance of these algorithms needs further improve...

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Bibliographic Details
Published in2021 IEEE International Symposium on Information Theory (ISIT) pp. 1451 - 1456
Main Authors Watanabe, Taishi, Ohseki, Takeo, Yamazaki, Kosuke
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.07.2021
Subjects
Approximation algorithms
Bit error rate
Decoding
Deep learning
Memory management
Parity check codes
Training
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Summary:The bit reliability based (BRB) and weighted bit reliability based (wBRB) algorithms are non-binary low-density parity-check (LDPC) code decoding algorithms with an excellent tradeoff between computational complexity and performance. However, the performance of these algorithms needs further improvement. We apply deep learning to these algorithms. Weights are assigned to each edge of the Tanner graphs of the non-binary LDPC codes in the proposed algorithms. We demonstrate the effectiveness of applying deep learning to the BRB and wBRB algorithms in terms of implementation and performance. The proposed algorithms achieve an approximately 0.3 dB higher bit error rate performance than the original algorithms in the high SNR region. The increase in computational complexity and memory consumption does not significantly change the implementation of the algorithms.
DOI:10.1109/ISIT45174.2021.9517790