Sensing-Assisted Beamforming and Trajectory Design for UAV-Enabled Networks

Unmanned aerial vehicle (UAV)-enabled network has been regarded as a promising component of the sixth generation (6G) wireless network to provide flexible and on-demand services. In the UAV-enabled networks, due to the user mobility and the dynamic channel characteristics, it is difficult to obtain the accurate location information and channel state information (CSI), therefore causing the uplink transmissions to be easily constrained. In this paper, we propose a sensing-assisted beamforming and trajectory design for the uplink transmissions in the UAV-enabled networks, where the accurate location information and CSI are not available. In the proposed design, the UAV-mounted base station (BS) first obtains the angle-of-arrivals (AoAs) information of the uplink signals from mobile users, thus enabling the estimation of the location information and CSI. Then, based on the estimated location information and CSI, a hybrid receiving beamforming and trajectory optimization problem is formulated to maximize the weighted average throughput (WAT) of our considered network. To solve this NP-hard problem, we decompose it into two subproblems, namely, a hybrid receive beamforming optimization subproblem and a UAV trajectory optimization subproblem. Then, the semi-definite relaxation (SDR), manifold optimization, and successive convex approximation (SCA)-based approaches are developed to solve these two subproblems. Finally, an alternating optimization algorithm is designed to obtain a suboptimal solution. Numerical results verify the effectiveness and robustness of our proposed design, showing how it can achieve the comparable performance as several benchmarks, especially when the estimated location information and CSI are less accurate.