Enhanced Quasi-Maximum Likelihood Decoding of Short LDPC Codes Based on Saturation
Peng Kang, Yixuan Xie, Lei Yang, Zheng Chen, Jinhong Yuan, Yuejun Wei · 2019
In this paper, we propose an enhanced quasi-maximum likelihood (EQML) decoder for short LDPC codes. The proposed EQML decoder selects unreliable variable nodes (VNs) and performs the reprocessing if the first belief propagation (BP) decoding attempt fails. To improve the decoding error rate performance, we propose a novel node selection method based on the sign fluctuation of VNs' extrinsic messages. We also present a partial pruning stopping (PPS) rule to reduce the decoding complexity by deactivating part of the decoding tests once a valid codeword is found. Simulation results show that the proposed PPS rule achieves 20% lower decoding complexity compared to the full list decoding without sacrificing the error rate performance. In addition, the proposed EQML decoder outperforms the augmented BP decoder for short LDPC codes and approaches the performance of the ML decoder within 0.3 dB in terms of the frame error rate.