Status Updating via Integrated Sensing and Communication: Freshness Optimisation

TL;DR

This paper develops an optimal control framework for integrated sensing and communication systems to minimize information age and operational costs, providing structured policies for remote status updating.

Contribution

It introduces a Markov decision process model for ISAC status updates and proves the optimal policy has a monotone threshold structure, enhancing design interpretability.

Findings

Optimal policies have a nondecreasing threshold structure.

The value function and decision map exhibit interpretable properties.

Integrating freshness objectives into ISAC yields practical strategies.

Abstract

This paper studies strategic design in an integrated sensing and communication (ISAC) architecture for status updating of remotely navigating agents. We consider an ISAC-enabled base station that can sense the state of a remote source and communicate this information back to the source. Both sensing and communication succeed with given probabilities and incur distinct costs. The objective is to optimise a long-term cost that captures information freshness, measured by the age of information (AoI), at the source together with sensing and communication overheads. The resulting sequential decision problem is formulated as a discounted infinite-horizon Markov decision process with a two-dimensional AoI state, representing information freshness at the source and at the base station. We prove that the optimal stationary policy admits a monotone threshold structure characterised by a nondecreasing switching curve in the AoI state space. Our numerical analysis illustrates the structures of the value function and the optimal decision map. These results demonstrate that freshness-based objectives can be naturally integrated into ISAC design, while yielding interpretable and implementable strategies.