TITLE:
Performance and Complexity Trade-Off between Short-Length Regular and Irregular LDPC
AUTHORS:
Ziyuan Peng, Ruizhe Yang
KEYWORDS:
Regular LDPC, Irregular LDPC, Near-ML Decoding, List Decoding, Belief Propagation Algorithm, Sum-Product Algorithm, CRC-Aided, Hybrid Decoding
JOURNAL NAME:
Journal of Computer and Communications,
Vol.12 No.9,
September
30,
2024
ABSTRACT: In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-off is shown clearly and demonstrated with the paradigm of hybrid decoding. For regular LDPC code, the SNR-threshold performance and error-floor performance could be improved to the optimal level of ML decoding if the decoding complexity is progressively increased, usually corresponding to the near-ML decoding with progressively increased size of list. For irregular LDPC code, the SNR-threshold performance and error-floor performance could only be improved to a bottle-neck even with unlimited decoding complexity. However, with the technique of CRC-aided hybrid decoding, the ML performance could be greatly improved and approached with reasonable complexity thanks to the improved code-weight distribution from the concatenation of CRC and irregular LDPC code. Finally, CRC-aided 5GNR-LDPC code is evaluated and the capacity-approaching capability is shown.