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.

翻译：本文研究了面向远程导航智能体状态更新的集成感知与通信（ISAC）架构中的策略设计问题。我们考虑一个具备ISAC能力的基站，该基站能够感知远程信源的状态并将该信息回传至信源。感知与通信过程均以给定概率成功执行，并产生不同的成本开销。研究目标在于优化一个长期成本函数，该函数同时涵盖信源处的信息新鲜度（以信息年龄（AoI）度量）以及感知与通信开销。由此产生的序贯决策问题被建模为一个具有二维AoI状态的折扣无限时域马尔可夫决策过程，其中AoI状态分别表征信源处与基站处的信息新鲜度。我们证明了最优平稳策略具有单调阈值结构，该结构由AoI状态空间中一条非递减的切换曲线所刻画。数值分析展示了值函数与最优决策映射的结构。这些结果表明，基于新鲜度的目标能够自然地融入ISAC设计，同时产生可解释且可实施的策略。