🤖 AI Summary
This study addresses the optimization of status updates for remote navigation agents in integrated sensing and communication (ISAC) systems, aiming to balance information freshness against system overhead. By formulating a long-term cost model based on the Age of Information (AoI), the work incorporates the stochastic success probabilities and associated costs of both sensing and communication into a sequential decision-making framework defined over a two-dimensional AoI state space. The problem is modeled as a discounted infinite-horizon Markov decision process, and theoretical analysis reveals that the optimal stationary policy exhibits a monotone threshold structure, characterized by a non-decreasing switching curve—offering both interpretability and implementability. Numerical experiments validate the structural properties of the value function and optimal policy, demonstrating that an AoI-driven optimization objective can effectively guide ISAC system design.
📝 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.