Joint Target Sensing and Energy-Harvesting-Based Remote State Monitoring: A CMDP Approach

The emerging paradigm of perceptive networks has attracted much recent attention to the integration of communication and sensing functionalities. In this letter, we mainly consider an integrated sensing and communication (ISAC) system comprised of a target and multiple energy harvesting (EH) enabled low-power sensors. To support a monitoring task by charging the sensors with radiated radio-frequency power while keeping sensing the target, a transmit beamforming-based scheme is designed for focusing energy in the directions of sensors and the target. Given a quality-of-service (QoS) constraint on the sensing task, a unified stochastic scheduling scheme is presented to assure the freshness of the sensor information at a monitor. Under the stochastic scheduling scheme, we minimize the average Age of Information (AoI) of all sensors by dynamically adjusting transmission power and beamforming weights in use. Given a power constraint, the optimal scheduling policy is obtained by a constrained Markov Decision Process (CMDP) approach. More specifically, an optimal tradeoff between the average power and average AoI is revealed through linear programming (LP) formulation, which is further demonstrated by extensive numerical results.