The decoding performance of conventional belief propagation decoders is seriously confined by the existence of message dependence in the code structure for short or moderate LDPC codes. In spite of the similarity of the external performance, we found the corresponding decoding failures of varied decoders, symbolized by the cross-entropy metric, will leave differed room for improvement for the postprocessing of ordered statistical decoding. Bearing in mind the postprocessor of higher order ensures better performance and incurs more expensive complexity, we propose a dynamic assignment of searching scope with respect to each decoding pattern for the order statistical decoding. Furthermore, the segmentation of decoding patterns, determined on the fly by the number of swaps in reducing the code check matrix into its systematic form via Gaussian elimination operation. will also benefit reducing complexity. Compared with the existing methods, our adapted strategy is justified by saving most memory consumption and inefficient searching of code-word candidates in extensive simulation especially for longer codes, at the cost of marginal performance loss.

翻译：传统置信传播译码器的译码性能严重受限于短或中等长度LDPC码中码结构存在的消息依赖性。尽管外部性能相似，我们发现以交叉熵度量为标志的不同译码器的相应译码失败，将为有序统计译码的后处理留下不同的改进空间。考虑到高阶后处理器性能更优但复杂度更高，我们提出一种针对有序统计译码的、根据每个译码模式动态分配搜索范围的方案。此外，通过高斯消元操作将码校验矩阵化为系统形式时，以交换次数实时确定的译码模式分割，也将有助于降低复杂度。与现有方法相比，我们提出的自适应策略通过大量仿真验证，在牺牲轻微性能损失的情况下，能够显著节省内存消耗并减少码字候选集的低效搜索，尤其适用于较长码字。