Construction of Polar Codes with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method for constructing polar codes, framing the problem as a maze game, which results in better code performance for the SCL decoder compared to traditional methods.

Contribution

It proposes a novel reinforcement learning approach to polar-code construction that does not rely on reliability sorting, improving performance for the SCL decoder.

Findings

Reinforcement learning can effectively optimize polar-code construction.

The method outperforms standard constructions in frame-error rate.

The approach is adaptable to various code lengths and decoders.

Abstract

This paper formulates the polar-code construction problem for the successive-cancellation list (SCL) decoder as a maze-traversing game, which can be solved by reinforcement learning techniques. The proposed method provides a novel technique for polar-code construction that no longer depends on sorting and selecting bit-channels by reliability. Instead, this technique decides whether the input bits should be frozen in a purely sequential manner. The equivalence of optimizing the polar-code construction for the SCL decoder under this technique and maximizing the expected reward of traversing a maze is drawn. Simulation results show that the standard polar-code constructions that are designed for the successive-cancellation decoder are no longer optimal for the SCL decoder with respect to the frame error rate. In contrast, the simulations show that, with a reasonable amount of training, the game-based construction method finds code constructions that have lower frame-error rate for various code lengths and decoders compared to standard constructions.