Reinforcement Learning Evaluation and Solution for the Feedback Capacity of the Ising Channel with Large Alphabet

TL;DR

This paper introduces a reinforcement learning approach to evaluate and approximate the feedback capacity of the Ising channel with large alphabets, overcoming computational challenges of traditional methods.

Contribution

It develops a neural network-based RL method to estimate feedback capacity for channels with large alphabets and derives analytic bounds and coding schemes from the structure of solutions.

Findings

RL provides a numerical lower bound on feedback capacity.

Analytic solutions are derived for alphabet sizes up to 8.

Asymptotic coding schemes are proposed for large alphabets.

Abstract

We propose a new method to compute the feedback capacity of unifilar finite state channels (FSCs) with memory using reinforcement learning (RL). The feedback capacity was previously estimated using its formulation as a Markov decision process (MDP) with dynamic programming (DP) algorithms. However, their computational complexity grows exponentially with the channel alphabet size. Therefore, we use RL, and specifically its ability to parameterize value functions and policies with neural networks, to evaluate numerically the feedback capacity of channels with a large alphabet size. The outcome of the RL algorithm is a numerical lower bound on the feedback capacity, which is used to reveal the structure of the optimal solution. The structure is modeled by a graph-based auxiliary random variable that is utilized to derive an analytic upper bound on the feedback capacity with the duality bound. The capacity computation is concluded by verifying the tightness of the upper bound by testing whether it is BCJR invariant. We demonstrate this method on the Ising channel with an arbitrary alphabet size. For an alphabet size smaller than or equal to 8, we derive the analytic solution of the capacity. Next, the structure of the numerical solution is used to deduce a simple coding scheme that achieves the feedback capacity and serves as a lower bound for larger alphabets. For an alphabet size greater than 8, we present an upper bound on the feedback capacity. For an asymptotically large alphabet size, we present an asymptotic optimal coding scheme.