Decoding Polar Codes with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method to optimize factor-graph permutations in polar codes, significantly enhancing decoding performance by learning during the decoding process.

Contribution

It formalizes factor-graph permutation selection as a multi-armed bandit problem and develops an online-learning decoder for improved error correction in polar codes.

Findings

Achieves 0.125 dB gain at FER of 10^{-4} for 5G polar codes

Uses reinforcement learning to adapt permutation selection during decoding

Outperforms random permutation selection methods

Abstract

In this paper we address the problem of selecting factor-graph permutations of polar codes under belief propagation (BP) decoding to significantly improve the error-correction performance of the code. In particular, we formalize the factor-graph permutation selection as the multi-armed bandit problem in reinforcement learning and propose a decoder that acts like an online-learning agent that learns to select the good factor-graph permutations during the course of decoding. We use state-of-the-art algorithms for the multi-armed bandit problem and show that for a 5G polar codes of length 128 with 64 information bits, the proposed decoder has an error-correction performance gain of around 0.125 dB at the target frame error rate of 10^{-4}, when compared to the approach that randomly selects the factor-graph permutations.