A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes

TL;DR

This paper introduces a reinforcement learning algorithm for constructing rate-profiles of PAC codes, improving their performance in decoding by discovering better profiles and highlighting the importance of convolutional precoding polynomial choice.

Contribution

The paper presents a novel RL-based method with new reward and update strategies for rate-profile construction of PAC codes, outperforming existing designs.

Findings

RL-based rate-profile construction improves FER performance

New reward strategies enable better rate-profile discovery

Choice of convolutional polynomial significantly affects code performance

Abstract

In this paper, we propose a reinforcement learning based algorithm for rate-profile construction of Arikan's Polarization Assisted Convolutional (PAC) codes. This method can be used for any blocklength, rate, list size under successive cancellation list (SCL) decoding and convolutional precoding polynomial. To the best of our knowledge, we present, for the first time, a set of new reward and update strategies which help the reinforcement learning agent discover much better rate-profiles than those present in existing literature. Simulation results show that PAC codes constructed with the proposed algorithm perform better in terms of frame erasure rate (FER) compared to the PAC codes constructed with contemporary rate profiling designs for various list lengths. Further, by using a (64, 32) PAC code as an example, it is shown that the choice of convolutional precoding polynomial can have a significant impact on rate-profile construction of PAC codes.