Tree Expectation Propagation for ML Decoding of LDPC Codes over the BEC
Tree Expectation Propagation for ML Decoding of LDPC Codes over the BEC
复制标题
BEC 上 LDPC 码的 ML 解码的树期望传播
DOI：
10.1109/tcomm.2012.120512.110419
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发表时间：
2013
影响因子：
8.3
通讯作者：
F. Pérez
中科院分区：
文献类型：
--
作者：
Luis Salamanca;P. Olmos;J. J. Murillo;F. Pérez
我们提出了一种LDPC码的译码算法，实现了二进制擦除信道（BEC）上的最大似然（ML）的解决方案。在该信道中，树结构的期望传播（TEP）解码器通过处理度1和度2的校验节点来改进剥离解码器（PD）。然而，它没有实现ML解决方案，因为TEP的树结构只允许近似推理。在本文中，我们提供的过程来构造精确推理所需的结构。该算法被称为广义树结构期望传播（GTEP），通过递归消除任何校验节点并将此信息合并到剩余图中来修改代码图。GTEP解码器在完成时提供唯一ML解决方案或树图，其中父节点的数量指示ML解决方案的多重性。我们还将该算法解释为高斯消除方法，将GTEP与其他ML解决方案联系起来。与以前的方法相比，它提出了一个等效的复杂性，它表现出一个更简单的图形消息传递过程，最有趣的是，该算法可以推广到其他渠道。
We propose a decoding algorithm for LDPC codes that achieves the maximum likelihood (ML) solution over the binary erasure channel (BEC). In this channel, the tree-structured expectation propagation (TEP) decoder improves the peeling decoder (PD) by processing check nodes of degree one and two. However, it does not achieve the ML solution, as the tree structure of the TEP allows only for approximate inference. In this paper, we provide the procedure to construct the structure needed for exact inference. This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), modifies the code graph by recursively eliminating any check node and merging this information in the remaining graph. The GTEP decoder upon completion either provides the unique ML solution or a tree graph in which the number of parent nodes indicates the multiplicity of the ML solution. We also explain the algorithm as a Gaussian elimination method, relating the GTEP to other ML solutions. Compared to previous approaches, it presents an equivalent complexity, it exhibits a simpler graphical message-passing procedure and, most interesting, the algorithm can be generalized to other channels.
影响因子：
32.8
作者：
Wainwright, Martin J.;Jordan, Michael I.
通讯作者：
Jordan, Michael I.
影响因子：
2.5
作者：
Richardson, TJ;Urbanke, RL
通讯作者：
Urbanke, RL