The Feedback Capacity of Noisy Output is the STate (NOST) Channels

TL;DR

This paper derives the feedback capacity formulas for a new class of finite-state channels called NOST channels, with and without causal state information, generalizing previous models and providing computable solutions.

Contribution

It introduces the NOST channel model, derives explicit feedback capacity formulas for both scenarios, and shows these can be computed via convex optimization, expanding understanding of finite-state channels.

Findings

Feedback capacity formulas are explicitly derived.

Capacity is computable via convex optimization.

CSI at encoder may not always increase capacity.

Abstract

We consider finite-state channels (FSCs) where the channel state is stochastically dependent on the previous channel output. We refer to these as Noisy Output is the STate (NOST) channels. We derive the feedback capacity of NOST channels in two scenarios: with and without causal state information (CSI) available at the encoder. If CSI is unavailable, the feedback capacity is $C_{\text{FB}}= \max_{P(x|y')} I(X;Y|Y')$, while if it is available at the encoder, the feedback capacity is $C_{\text{FB-CSI}}= \max_{P(u|y'),x(u,s')} I(U;Y|Y')$, where $U$ is an auxiliary RV with finite cardinality. In both formulas, the output process is a Markov process with stationary distribution. The derived formulas generalize special known instances from the literature, such as where the state is i.i.d. and where it is a deterministic function of the output. $C_{\text{FB}}$ and $C_{\text{FB-CSI}}$ are also shown to be computable via convex optimization problem formulations. Finally, we present an example of an interesting NOST channel for which CSI available at the encoder does not increase the feedback capacity.