부산대학교 도서관

This paper focuses on the timely delivery of sensing information in a joint sensing and communication (JSC) system to meet the requirements of emerging applications. Specifically, we investigate the time allocation of a single JSC node equipped with both sensing and communication functions to minimize the long-term average age of estimation information (AoEI) while satisfying the long-term average power constraint. The proposed metric, AoEI, combines radar mutual information (MI) and age of information (AoI) to capture both the passage of time and the accuracy of estimation information, making it more suitable for the JSC system. To solve this problem, we formulate the time allocation problem as a constrained Markov decision process (CMDP) and propose a model-free constrained deep reinforcement learning (CDRL) based algorithm. The simulation results demonstrate that the proposed algorithm can achieve a good trade-off between AoEI and power consumption and converge to a policy that satisfies the constraint in a highly dynamic and uncertain environment.