Capacity estimation of two-dimensional channels using Sequential Monte Carlo

TL;DR

This paper introduces a Sequential Monte Carlo algorithm to estimate the capacity of 2D channels, significantly improving accuracy over existing methods, especially for noiseless run-length limited constrained channels.

Contribution

A novel SMC-based algorithm for 2D channel capacity estimation that outperforms current state-of-the-art methods in accuracy and efficiency.

Findings

Over an order of magnitude improvement in estimation accuracy

Effective for noiseless 2D run-length limited channels

Applicable to general 2D channel capacity problems

Abstract

We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D one-infinity run-length limited constrained channel, but the underlying idea is generally applicable. The proposed algorithm is profiled against a state-of-the-art method, yielding more than an order of magnitude improvement in estimation accuracy for a given computation time.