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arXiv 0812.2559cs.ITmath.IT
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一种改进的线性分组码LP译码分离算法

A Separation Algorithm for Improved LP-Decoding of Linear Block Codes

Akin Tanatmis, Stefan Ruzika, Horst W. Hamacher, Mayur Punekar, Frank Kienle, Norbert Wehn

摘要:

最大似然(ML)译码是任意线性分组码的最优译码算法,可以写成一个可编程(IP)问题。Feldman等人放宽了这个IP问题,提出了基于线性规划的线性分组码译码算法。在本文中,我们提出了一个新的IP制定的ML解码问题,并解决了IP与一般的方法。该公式使用指示器变量来检测违反的奇偶检验。我们从我们的配方中推导出Gomory切割,并在分离算法中使用它们来找到ML码字。我们进一步提出了一个有效的方法,发现削减引起的冗余奇偶检验(RPC)。在某些情况下,我们可以保证这些RPC切割是有效的,并切断LP解码的分数最优解。我们证明了两个LDPC码和BCH码,我们的分离算法的性能显着优于LP解码。

英文摘要:

Maximum Likelihood (ML) decoding is the optimal decoding algorithm for arbitrary linear block codes and can be written as an Integer Programming (IP) problem. Feldman et al. relaxed this IP problem and presented Linear Programming (LP) based decoding algorithm for linear block codes. In this paper, we propose a new IP formulation of the ML decoding problem and solve the IP with generic methods. The formulation uses indicator variables to detect violated parity checks. We derive Gomory cuts from our formulation and use them in a separation algorithm to find ML codewords. We further propose an efficient method of finding cuts induced by redundant parity checks (RPC). Under certain circumstances we can guarantee that these RPC cuts are valid and cut off the fractional optimal solutions of LP decoding. We demonstrate on two LDPC codes and one BCH code that our separation algorithm performs significantly better than LP decoding.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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