perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
This work addresses a critical bottleneck in communication systems by enhancing decoding reliability, though it appears incremental as it builds on existing sub-optimal algorithms with a novel machine learning integration.
The paper tackles the NP-hard problem of optimal decoding for error correction codes by proposing a data-driven framework for permutation selection, which combines domain knowledge with machine learning techniques like node embedding and self-attention, resulting in significant and consistent improvements in bit error rate for all simulated codes over baseline decoders.
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.