A Gated Hypernet Decoder for Polar Codes
This work improves neural polar decoders for error correction in communications, but it is incremental as it builds on existing hypernetwork and belief propagation methods.
The authors tackled the problem of decoding polar codes by applying hypernetworks to a formalized polar belief propagation scheme, achieving bit-error-rate performance comparable to the successive list cancellation method at high SNRs.
Hypernetworks were recently shown to improve the performance of message passing algorithms for decoding error correcting codes. In this work, we demonstrate how hypernetworks can be applied to decode polar codes by employing a new formalization of the polar belief propagation decoding scheme. We demonstrate that our method improves the previous results of neural polar decoders and achieves, for large SNRs, the same bit-error-rate performances as the successive list cancellation method, which is known to be better than any belief propagation decoders and very close to the maximum likelihood decoder.