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\unicodex2014defined as the set of estimation covariance pairs for which communication is preferred over sensing\unicodex2014expands with increasing source uncertainty and contracts with increasing base-station uncertainty.
@article{arxiv.2601.22912,
title = {Feedback Control via Integrated Sensing and Communication: Uncertainty Optimisation},
author = {Touraj Soleymani and Mohamad Assaad and John S. Baras},
journal= {arXiv preprint arXiv:2601.22912},
year = {2026}
}