Iterative decoding of compound codes by probability propagation in graphical models
Iterative decoding of compound codes by probability propagation in graphical models
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DOI：
10.1109/49.661110
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发表时间：
1998-02-01
影响因子：
16.4
通讯作者：
Frey, BJ
中科院分区：
文献类型：
--
作者：
Kschischang, FR;Frey, BJ
本文提出了一个统一的描述复合码的图模型框架，并推导了迭代译码算法。在回顾了各种图模型（Markov随机场、坦纳图和贝叶斯网络）之后，我们推导了一个通用的因子图描述函数的分布式边缘化算法。Pearl的置信度传播算法很容易从这个一般算法中推导出来，作为一个特例。我们指出，最近开发的各种码的迭代译码算法，包括并行级联卷积码的“turbo译码”，可以被看作是码的图形模型中的概率传播。我们专注于码的贝叶斯网络描述，给出了一个自然的输入/状态/输出/信道描述的代码和信道，我们指出如何迭代解码器可以开发并行和串行级联编码系统，产品代码，低密度奇偶校验码。
We present a unified graphical model framework for describing compound codes and deriving iterative decoding algorithms, After reviewing a variety of graphical models (Markov random fields, Tanner graphs, and Bayesian networks), we derive a general distributed marginalization algorithm far functions described by factor graphs. From this general algorithm, Pearl's belief propagation algorithm is easily derived as a special case, We point out that recently developed iterative decoding algorithms for various codes, including "turbo decoding" of parallel-concatenated convolutional codes, may be viewed as probability propagation in a graphical model of the code, We focus on Bayesian network descriptions of codes, which give a natural input/state/output/channel description of a code and channel, and we indicate how iterative decoders can be developed for parallel- and serially concatenated coding systems, product codes, and low-density parity-check codes.