ADMM decoding on trapping sets
ADMM decoding on trapping sets
复制标题
陷阱集上的 ADMM 解码
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Draper
中科院分区：
文献类型：
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作者：
Xishuo Liu;S. Draper
乘法器交替方向译码（ADMM）是一种新的低密度奇偶校验（LDPC）码译码框架。它可以用来实现线性规划（LP）解码或惩罚LP解码。与置信传播（BP）解码类似，ADMM解码由局部“检查”和“变量更新”组成。然而，ADMM解码在高信噪比（SNR）下的性能优于BP。为了理解为什么这两种局部操作算法会导致不同的错误平层行为，本文研究了ADMM解码的动态特性。特别是，我们专注于陷阱集，这是观察到ADMM解码中的错误地板。我们的研究结果表明，ADMM解码的动力学陷阱集可以被描述为一个跳跃线性系统。此外，这些结果表明，参与ADMM的拉格朗日乘子起着重要的作用，在纠正陷阱集错误。最后，我们提出的模拟结果，支持这种理解。
Alternating direction method of multipliers (ADMM) decoding is a new decoding framework for low-density parity-check (LDPC) codes. It can be used to implement linear programming (LP) decoding or penalized LP decoding. Similar to belief propagation (BP) decoding, ADMM decoding consists of local “check” and “variable updates”. However, ADMM decoding performs better than BP at high signal-to-noise ratios (SNRs). To understand why these two locally operating algorithms result in different error floor behaviors, we study the dynamics of ADMM decoding in this paper. In particular, we focus on trapping sets, which are observed to cause error floors in ADMM decoding. Our results show that the dynamics of ADMM decoding on trapping sets can be characterized as a jump linear system. Furthermore, these results indicate that the Lagrange multipliers involved in ADMM play an important role in correcting trapping set errors. Finally, we present simulation results that support this understanding.