A Reinforcement Learning-Based Energy Efficiency Improvement Scheme for a Joint Communication and Radar System with Multiple UAVs

Joint communication and radar (JCR) system integrates communication and radar functions into a single signal waveform. Unmanned aerial vehicles (UAVs) can serve as information relays in the JCR system with the radar detecting the positions of UAVs. However, a challenge of energy constraints remains prominent in UAV-based JCR systems. Through simultaneous wireless information and power transfer (SWIPT), the energy-constrained UAVs can harvest energy and enhance the energy efficiency. In this paper, we propose a reinforcement learning scheme for improving both the JCR system performance and energy efficiency by controlling the positions of multiple UAVs. The simulation results show that the strategic positioning of UAV relays enhances both system performance and energy efficiency.