Improved Maximum-Likelihood Decoding Using Sparse Parity-Check Matrices
Improved Maximum-Likelihood Decoding Using Sparse Parity-Check Matrices
复制标题
使用稀疏奇偶校验矩阵改进最大似然解码
DOI：
10.1109/ict.2018.8464884
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
N. Wehn
中科院分区：
文献类型：
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作者：
Florian Gensheimer;Tobias Dietz;Stefan Ruzika;Kira Kraft;N. Wehn
最大似然（ML）译码是通信中获得信道码最佳性能的一种重要而有力的方法。然而，ML解码是一个具有挑战性的问题，其复杂性随着代码的块长度呈指数增长，使得对于大型代码进行最佳解码是不可行的。在本文中，我们提出了一种新的方法来加速ML解码，通过最小化代码的底层矩阵表示中的1的数量。我们将这个最小化问题表示为整数规划，并使用启发式算法来解决它。使用这些最小化的矩阵，我们显着减少了各种代码的不同ML解码器的运行时间，与原始矩阵的运行时间相比，加速高达81%。
Maximum-likelihood (ML) decoding is an important and powerful method in communications to obtain the optimal performance of a channel code. However, ML decoding is a challenging problem whose complexity grows exponentially with the blocklength of the code, making it unfeasible to decode optimally for large codes. In this paper, we present a new approach to accelerate ML decoding by minimizing the number of ones in the code's underlying matrix representation. We formulate this minimization problem as an integer program and use a heuristic algorithm to solve it. Using these minimized matrices, we significantly reduce the runtime of different ML decoders for various codes, resulting in speedups of up to 81% compared to the runtime on the original matrices.