Thermodynamics of the Binary Symmetric Channel

TL;DR

This paper analyzes the thermodynamics of a hidden Markov process derived from a binary symmetric channel, linking it to the 1D RFIM, and provides new bounds on memory decay relevant for denoising algorithms.

Contribution

It identifies the Gibbs potential of the process and establishes a stronger bound on the memory decay rate, connecting information theory and statistical physics.

Findings

Identified the Gibbs potential of the hidden Markov process.

Established a stronger bound on memory decay rate.

Discussed implications for denoising algorithm development.

Abstract

We study a hidden Markov process which is the result of a transmission of the binary symmetric Markov source over the memoryless binary symmetric channel. This process has been studied extensively in Information Theory and is often used as a benchmark case for the so-called denoising algorithms. Exploiting the link between this process and the 1D Random Field Ising Model (RFIM), we are able to identify the Gibbs potential of the resulting Hidden Markov process. Moreover, we obtain a stronger bound on the memory decay rate. We conclude with a discussion on implications of our results for the development of denoising algorithms.