Error correction codes are an integral part of communication applications, boosting the reliability of transmission. The optimal decoding of transmitted codewords is the maximum likelihood rule, which is NP-hard due to the curse of dimensionality. For practical realizations, sub-optimal decoding algorithms are employed; yet limited theoretical insights prevent one from exploiting the full potential of these algorithms. One such insight is the choice of permutation in permutation decoding. We present a data-driven framework for permutation selection, combining domain knowledge with machine learning concepts such as node embedding and self-attention. Significant and consistent improvements in the bit error rate are introduced for all simulated codes, over the baseline decoders. To the best of the authors’ knowledge, this work is the first to leverage the benefits of the neural Transformer networks in physical layer communication systems.

中文翻译：

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perm2vec：用于纠错码解码的注意力图排列选择

纠错码是通信应用不可或缺的一部分，可提高传输的可靠性。传输码字的最佳解码是最大似然规则，由于维数灾难，这是 NP-hard 问题。对于实际实现，采用次优解码算法；然而，有限的理论见解阻碍了人们充分利用这些算法的潜力。一种这样的见解是置换解码中置换的选择。我们提出了一个数据驱动的置换选择框架，将领域知识与机器学习概念（如节点嵌入和自注意力）相结合。与基线解码器相比，所有模拟代码都引入了显着且一致的误码率改进。据作者所知，