Learning-Enabled Radar-Assisted Predictive Beamforming for UAV-Aided Networks

Unmanned Aerial Vehicle (UAV) technologies have garnered significant attention, particularly in the context of UAV-assisted wireless networks, which are seen as a pivotal component in the development of Sixth-Generation (6G) mobile communication systems. In this research, we delve into the realm of UAV-assisted wireless communication networks, where a single UAV efficiently caters to numerous random mobile users on the ground. Our focus lies in optimizing user movement tracking, beamforming, and UAV trajectory to maximize the data transmission rates for users within a specified time frame, all while adhering to stringent power constraints and the UAV's limited flight range. We harness the power of deep reinforcement learning (DRL) to monitor mobile users and predict the ever-changing channel state information. As beamforming and UAV trajectory adjustments operate on different timescales, we introduce a dual-layer deep unfolding network to fine-tune the transmit beamformer and UAV trajectory simultaneously. The outcomes of our simulations demonstrate the effectiveness and commendable performance of the proposed scheme.