Applicability of single- and two-hidden-layer neural networks in decoding linear block codes
Applicability of single- and two-hidden-layer neural networks in decoding linear block codes
复制标题
单隐层和两隐层神经网络在线性分组码解码中的适用性
DOI：
10.1109/telfor52709.2021.9653357
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Vasić, Bane
中科院分区：
文献类型：
--
作者：
Brkic, Srdan;Ivanis, Predrag;Vasić, Bane
本文分析了单隐层和双隐层前馈人工神经网络在线性分组码译码中的适用性。基于SLFN和TLFN逼近离散函数的可证明性，我们讨论了能够执行最大似然译码的网络规模。此外，我们还提出了一种利用人工神经网络(ANN)来降低低密度奇偶校验(LDPC)码的误码平台的译码方案。通过学习LDPC码的典型译码无法纠正的少量错误模式，ANN可以将错误平底降低一个数量级，而只有边际平均复杂度香。
In this paper, we analyze applicability of single-and two-hidden-layer feed-forward artificial neural networks, SLFNs and TLFNs, respectively, in decoding linear block codes. Based on the provable capability of SLFNs and TLFNs to approximate discrete functions, we discuss sizes of the network capable to perform maximum likelihood decoding. Furthermore, we propose a decoding scheme, which use artificial neural networks (ANNs) to lower the error-floors of low-density parity-check (LDPC) codes. By learning a small number of error patterns, uncorrectable with typical decoders of LDPC codes, ANN can lower the error-floor by an order of magnitude, with only marginal average complexity incense.