On Capacity Formulation with Stationary Inputs and Application to a Bit-Patterned Media Recording Channel Model

TL;DR

This paper leverages the stationarity property of capacity-achieving inputs to derive new bounds and capacity characterizations for a bit-patterned media recording channel, improving understanding in low-noise regimes.

Contribution

It introduces a novel approach to realize stationarity in capacity calculations within the Shannon-theoretic framework, leading to tighter bounds and capacity expansion results.

Findings

New lower and upper bounds on channel capacity

Capacity characterization as a low-noise series expansion

Stationarity realization in Shannon-theoretic capacity formulas

Abstract

In this correspondence, we illustrate among other things the use of the stationarity property of the set of capacity-achieving inputs in capacity calculations. In particular, as a case study, we consider a bit-patterned media recording channel model and formulate new lower and upper bounds on its capacity that yield improvements over existing results. Inspired by the observation that the new bounds are tight at low noise levels, we also characterize the capacity of this model as a series expansion in the low-noise regime. The key to these results is the realization of stationarity in the supremizing input set in the capacity formula. While the property is prevalent in capacity formulations in the ergodic-theoretic literature, we show that this realization is possible in the Shannon-theoretic framework where a channel is defined as a sequence of finite-dimensional conditional probabilities, by defining a new class of consistent stationary and ergodic channels.