Enhanced QuasiMaximum Likelihood Decoding of Short LDPC Codes Based on Saturation In this paper, we propose an enhanced quasimaximum 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 VNsxe2x80x99 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.

Simplified Variable Node Unit Architecture for Nonbinary LDPC Decoder Nonbinary lowdensityparitycheck (NBLDPC) code outperforms their binary counterpart in terms of errorcorrecting performance and errorfloor property when the code length is moderate. However, the drawback of NBLDPC decoders is high complexity and the complexity increases considerably when increasing the Galoisfield order. In this paper, a simplified basicset trellis minmax (sBSTMM) algorithm that is especially efficient for highorder Galois Fields, is proposed for the variable node processing to reduce the complexity of the variable node unit (VNU) as well as the whole decoder. The decoder architecture corresponding to the proposed algorithm is designed for the (837, 726) NBLDPC code over GF(32). The implementation results using 90nm CMOS technology show that the proposed decoder architecture reduces the gate count by 21.35% and 9.4% with almost similar errorcorrecting performance, compared to the uptodate works.