摘要翻译：
好的差错控制码的近似最优译码通常是一项困难的任务。然而，对于某一类（足够）好的码，存在一种性能接近最佳的高效译码算法。这些码是通过具有低复杂度网格表示的组成码的组合来定义的。它们的译码算法是（循环）信念传播的一个实例，并且基于组成信念的迭代传递。由此，信念由在组成网格中计算的符号概率给出。即使采用弱成分码，也能获得接近最优的性能，即编/译码器对几乎达到信息论容量。然而，（loopy）信念传播只对特定的代码集执行良好，这限制了它的适用性。本文给出了迭代译码的一个推广。它建议传递更多的价值，而不仅仅是组成信念。这是通过独立调查代码空间的部分所获得的信念的转移来实现的。这就引出了鉴别器的概念，鉴别器用于在一定区域内提高解码器分辨率，并定义鉴别的符号信念。结果表明，这些信念近似于整个符号概率。这导致迭代规则（低于信道容量）通常只允许整体解码问题的解决方案。通过高斯近似，导出了该算法的低复杂度版本。此外，该方法随后可应用于大范围的信道映射，而不会显著增加复杂度。
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英文标题：
《Discriminated Belief Propagation》
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作者：
Uli Sorger
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  Near optimal decoding of good error control codes is generally a difficult task. However, for a certain type of (sufficiently) good codes an efficient decoding algorithm with near optimal performance exists. These codes are defined via a combination of constituent codes with low complexity trellis representations. Their decoding algorithm is an instance of (loopy) belief propagation and is based on an iterative transfer of constituent beliefs. The beliefs are thereby given by the symbol probabilities computed in the constituent trellises. Even though weak constituent codes are employed close to optimal performance is obtained, i.e., the encoder/decoder pair (almost) achieves the information theoretic capacity. However, (loopy) belief propagation only performs well for a rather specific set of codes, which limits its applicability.   In this paper a generalisation of iterative decoding is presented. It is proposed to transfer more values than just the constituent beliefs. This is achieved by the transfer of beliefs obtained by independently investigating parts of the code space. This leads to the concept of discriminators, which are used to improve the decoder resolution within certain areas and defines discriminated symbol beliefs. It is shown that these beliefs approximate the overall symbol probabilities. This leads to an iteration rule that (below channel capacity) typically only admits the solution of the overall decoding problem. Via a Gauss approximation a low complexity version of this algorithm is derived. Moreover, the approach may then be applied to a wide range of channel maps without significant complexity increase.
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PDF链接：
https://arxiv.org/pdf/0710.5501