Abstract

We consider the use of the well-known dual capacity bounding technique for deriving upper bounds on the capacity of indecomposable finite-state channels (FSCs) with finite input and output alphabets. In this technique, capacity upper bounds are obtained by choosing suitable test distributions on the sequence of channel outputs. We propose test distributions that arise from certain graphical structures called Q-graphs. As we show in this paper, the advantage of this choice of test distribution is that, for the important sub-classes of unifilar and input-driven FSCs, the resulting upper bounds can be formulated as a dynamic programming (DP) problem, which makes the bounds tractable. We illustrate this for several examples of FSCs, where we are able to solve the associated DP problems explicitly to obtain capacity upper bounds that either match or beat the best previously reported bounds. For instance, for the classical trapdoor channel, we improve the best known upper bound of 0.661 (due to Lutz (2014)) to 0.584, shrinking the gap to the best known lower bound of 0.572, all bounds being in units of bits per channel use.

Highlights

  • A finite-state channel (FSC) is a mathematical model for a discrete-time channel in which the channel output depends statistically on both the channel input and an underlying channel state, the latter taking values in a finite set

  • Main result — dynamic programming (DP) formulation In the following theorem we informally summarize our main contribution, namely, the computability of the dual capacity upper bound derived from graph-based test distributions, in the case of unifilar and input-driven FSCs

  • We present the DP formulation of the upper bound in Theorem 6, and it will be shown that this formulation satisfies the DP properties

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Summary

Introduction

A finite-state channel (FSC) is a mathematical model for a discrete-time channel in which the channel output depends statistically on both the channel input and an underlying channel state, the latter taking values in a finite set. This model can represent a channel with memory since it allows the channel output to depend on past occurrences via the channel state. This paper was presented in part at the 2019 IEEE International Symposium on Information Theory

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