Journal
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 39, Issue 1, Pages 79-88Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2020.3036951
Keywords
Maximum likelihood decoding; Bit error rate; Task analysis; Iterative decoding; Electrical engineering; Convergence; Inference algorithms; Decoding; error correcting codes; belief propagation; deep learning
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This paper discusses the importance of error correction codes in communication applications, proposes a data-driven framework for permutation selection, and achieves significant improvements.
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.
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