A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV

In this paper, we consider a scenario with one unmanned aerial vehicle (UAV) equipped with a uniform linear array (ULA), which sends combined information and sensing signals to communicate with multiple ground base stations (GBSs) and, at the same time, senses potential targets placed within an interested area on the ground. We aim to jointly design the transmit beamforming with the GBSs association to optimize communication performance while ensuring high sensing accuracy. We propose a predictive beamforming framework based on a dual deep neural network (DNN) solution to solve the formulated nonconvex optimization problem. A first DNN is trained to produce the required beamforming matrix for any point of the UAV flying area in a reduced time compared to stateof- the-art beamforming optimizers. A second DNN is trained to learn the optimal mapping from the input features, power, and effective isotropic radiated power (EIRP) constraints to the GBSs association decision. Finally, we provide an extensive simulation analysis to corroborate the proposed approach and show the benefits of EIRP, Signal-to-Noise-plus-Interference Ratio (SINR) performance and computational speed.