Reinforcement Learning for Nested Polar Code Construction

TL;DR

This paper introduces a reinforcement learning approach to construct nested polar codes by modeling the process as an MDP and using neural networks, genetic algorithms, and integrated learning to optimize code performance without prior expert knowledge.

Contribution

It presents a novel RL-based framework for polar code construction, combining neural networks and genetic algorithms to improve efficiency and performance over existing methods.

Findings

Achieves comparable or better performance than state-of-the-art methods.

Converges faster using integrated learning with genetic algorithms.

Does not rely on traditional polar coding theory knowledge.

Abstract

In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques. First, an MDP environment with state, action, and reward is defined in the context of polar coding. Specifically, a state represents the construction of an $(N,K)$ polar code, an action specifies its reduction to an $(N,K-1)$ subcode, and reward is the decoding performance. A neural network architecture consisting of both policy and value networks is proposed to generate actions based on the observed states, aiming at maximizing the overall rewards. A loss function is defined to trade off between exploitation and exploration. To further improve learning efficiency and quality, an `integrated learning' paradigm is proposed. It first employs a genetic algorithm to generate a population of (sub-)optimal polar codes for each $(N,K)$, and then uses them as prior knowledge to refine the policy in RL. Such a paradigm is shown to accelerate the training process, and converge at better performances. Simulation results show that the proposed learning-based polar constructions achieve comparable, or even better, performances than the state of the art under successive cancellation list (SCL) decoders. Last but not least, this is achieved without exploiting any expert knowledge from polar coding theory in the learning algorithms.