A Gated Hypernet Decoder for Polar Codes

TL;DR

This paper introduces a hypernetwork-based decoder for polar codes that enhances neural decoding performance, matching the success of advanced decoding methods at high SNRs, and approaches maximum likelihood decoding accuracy.

Contribution

It presents a novel gated hypernet decoder formalization for polar codes, improving neural decoding results and achieving near-optimal performance at high SNRs.

Findings

Outperforms previous neural polar decoders

Achieves performance comparable to successive list cancellation at high SNRs

Close to maximum likelihood decoding accuracy

Abstract

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.