  In this paper, we consider a scenario with one UAV equipped with a ULA, which
sends combined information and sensing signals to communicate with multiple GBS
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
GBS association to optimize communication performance while ensuring high
sensing accuracy. We propose a predictive beamforming framework based on a dual
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 state-of-the-art beamforming
optimizers. A second DNN is trained to learn the optimal mapping from the input
features, power, and EIRP constraints to the GBS association decision. Finally,
we provide an extensive simulation analysis to corroborate the proposed
approach and show the benefits of EIRP, SINR performance and computational
speed.
