Informed shuffled belief- propagation decoding for low-density parity-check codes
Informed shuffled belief- propagation decoding for low-density parity-check codes
复制标题
低密度奇偶校验码的知情混洗置信传播解码
DOI：
10.1049/iet-com.2014.1169
复制
发表时间：
2015
影响因子：
1.6
通讯作者：
Bin Wu
中科院分区：
文献类型：
--
作者：
Yi Gong;Xingcheng Liu;Guojun Han;Bin Wu
混洗置信传播（Shuffled Belief Propagation，SBP）算法作为一种顺序置信传播（Sequential Belief Propagation，BP）算法，加快了BP译码的收敛速度，并保持了洪泛BP算法的最小复杂度。然而，它的性能显着低于通知动态调度（IDS）BP算法。作者设计了一种基于变量节点对数似然比值残差的动态定位方法，对SBP变量节点进行重新排序。该定位方法从两个方面显着加速了SBP算法的收敛：首先更新具有最大残差的不稳定变量节点，并在本地选择最大残差。仿真结果表明，该算法的性能接近IDS BP算法的最佳性能，并在高信噪比下表现突出。
Shuffled belief propagation (SBP), as a sequential belief propagation (BP) algorithm, speeds up the convergence of BP decoding, and maintains the least complexity of flooding BP. However, its performance is remarkably inferior toinformed dynamic scheduling(IDS) BP algorithms. The authors design an informed dynamic location method, based on the residuals of variable node log‐likelihood ratio values, to reorder variable nodes of SBP to be updated. The location method significantly accelerates the convergence of SBP algorithm from two aspects: the unstable variable node with the largest residual to be updated first, and selecting the largest residual locally. Simulation results show that the proposed algorithm performs nearly the same as the best performance of IDS BP algorithms, and behaves prominently at high signal‐to‐noise ratios.