In this study, an optimization model for offline scheduling policy of low-density parity-check (LDPC) codes is proposed to improve the decoding efficiency of the belief propagation (BP). The optimization model uses the number of messages passed (NMP) as a metric to evaluate complexity, and two metrics, average entropy (AE), and gap to maximum a posteriori (GAP), to evaluate BP decoding performance. Based on this model, an algorithm is proposed to optimize the scheduling sequence for reduced decoding complexity and superior performance compared to layered BP. Furthermore, this proposed algorithm does not add the extra complexity of determining the scheduling sequence to the decoding process.
翻译:本研究提出了一种用于低密度奇偶校验(LDPC)码离线调度策略优化的模型,以提高置信传播(BP)的译码效率。该优化模型使用消息数量(NMP)作为评估复杂度的指标,使用平均熵(AE)和到最大后验概率的差值(GAP)这两个指标来评估BP译码性能。基于该模型,提出了一种算法用于优化调度序列,以减少译码复杂度并比分层BP具有更优越的性能。此外,所提出的算法不会增加调度序列决策对译码过程的额外复杂性。