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 language | English |
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Article number | 9252949 |
Pages (from-to) | 79-88 |
Number of pages | 10 |
Journal | IEEE Journal on Selected Areas in Communications |
Volume | 39 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2021 |
Externally published | Yes |
Keywords
- Decoding
- belief propagation
- deep learning
- error correcting codes
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering