Optimal UAV-Relay Positioning and Transmit Power Allocation in ISAC-Based A2G Networks

Recently, interest in communication services using unmanned aerial vehicles (UAVs) and integrated sensing and communication (ISAC) systems that utilize radar and communication simultaneously has been increasing rapidly. Wireless communications through UAVs have the advantage of easily securing a line-of-sight (LoS) channel with ground users due to their flexibility in deployment. ISAC systems can use bandwidth efficiently by combining radar and communication into a single system. In this study, communication and radar performance were optimized in a UAV-relay-based ISAC system using a single communication waveform through multi-agent Q-learning. The convergence and performance of the proposed Q-learning algorithm were confirmed through simulations.