Perm2vec: Attentive Graph Permutation Selection for Decoding of Error Correction Codes

Avi Caciularu, Nir Raviv, Tomer Raviv, Jacob Goldberger, Yair Be'Ery

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number9252949
Pages (from-to)79-88
Number of pages10
JournalIEEE Journal on Selected Areas in Communications
Volume39
Issue number1
DOIs
StatePublished - Jan 2021
Externally publishedYes

Keywords

  • Decoding
  • belief propagation
  • deep learning
  • error correcting codes

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