Feedback Control via Integrated Sensing and Communication: Uncertainty Optimisation

TL;DR

This paper develops an optimal control framework for integrated sensing and communication systems in cyber-physical feedback control, revealing threshold-based policies and how uncertainty influences system behavior.

Contribution

It provides a rigorous characterization of optimal policies in ISAC-enabled feedback control, including threshold-based switching and linear control strategies under uncertainty.

Findings

Optimal switching policy is threshold-based on estimation covariances.

Control policy is linear in the source state estimate.

Threshold region expands with source uncertainty, contracts with base-station uncertainty.

Abstract

This paper studies strategic design in an integrated sensing and communication (ISAC) architecture for feedback control of cyber-physical systems. We focus on a setting in which the regulation of a physical process (i.e., remote source) is performed via an ISAC-enabled base station. The base station can alternate between tracking the state of the source and delivering control-relevant information back to the source. For a Gauss-Markov source subject to i.i.d. Bernoulli sensing and communication links, under a finite-horizon linear-quadratic-Gaussian cost, we rigorously characterise the optimal policies through an uncertainty-aware synthesis. We establish that the optimal switching policy, for the ISAC system at the base station, is threshold-based in terms of the source and base-station estimation covariances, while the optimal control policy, for the actuator at the source, is linear in the source state estimate. We show that the threshold region$\unicode{x2014}$defined as the set of estimation covariance pairs for which communication is preferred over sensing$\unicode{x2014}$expands with increasing source uncertainty and contracts with increasing base-station uncertainty.