Belief propagation (BP) decoding of low-density parity-check (LDPC) codes with various dynamic decoding schedules have been proposed to improve the efficiency of the conventional flooding schedule. As the ultimate goal of an ideal LDPC code decoder is to have correct bit decisions, a dynamic decoding schedule should be variable node (VN)-centric and be able to find the VNs with probable incorrect decisions and having a good chance to be corrected if chosen for update. We propose a novel and effective metric called conditional innovation (CI) which serves this design goal well. To make the most of dynamic scheduling which produces high-reliability bit decisions, we limit our search for the candidate VNs to those related to the latest updated nodes only. Based on the CI metric and the new search guideline separately or in combination, we develop several highly efficient decoding schedules. To reduce decoding latency, we introduce multi-edge updating versions which offer extra latency-performance tradeoffs. Numerical results show that both single-edge and multi-edge algorithms provide better decoding performance against most dynamic schedules and the CI-based algorithms are particularly impressive at the first few decoding iterations.

中文翻译：

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具有可变节点中心动态调度的LDPC码的置信传播解码

已经提出了具有各种动态解码时间表的低密度奇偶校验（LDPC）码的置信传播（BP）解码，以提高常规洪泛时间表的效率。由于理想的LDPC码解码器的最终目标是拥有正确的比特决策，因此动态解码时间表应以可变节点（VN）为中心，并能够找到可能具有错误决策的VN，并且有很好的机会被纠正选择进行更新。我们提出了一种新颖有效的度量，称为条件创新（CI），可以很好地满足此设计目标。为了充分利用产生高可靠性位决策的动态调度，我们将候选VN的搜索限制为仅与最新更新的节点相关。根据CI指标和新的搜索指南分别或结合起来，我们制定了几种高效的解码时间表。为了减少解码延迟，我们引入了多边更新版本，这些版本提供了额外的延迟性能折衷。数值结果表明，单边缘算法和多边缘算法在大多数动态调度中均提供了更好的解码性能，而基于CI的算法在最初的几次解码迭代中尤其令人印象深刻。