Author(s)

Ziyuan Peng, Ruizhe Yang

School of Information Science and Technology, Beijing University of Technology, Beijing, China.

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.

Regular LDPC, Irregular LDPC, Near-ML Decoding, List Decoding, Belief Propagation Algorithm, Sum-Product Algorithm, CRC-Aided, Hybrid Decoding

Peng, Z. and Yang, R. (2024) Performance and Complexity Trade-Off between Short-Length Regular and Irregular LDPC. Journal of Computer and Communications, 12, 208-215. doi: 10.4236/jcc.2024.129012.