Rethinking Joint UAV Placement and Beamforming: A Correlation-Aware Geometric Approach

Abstract

In multiuser unmanned aerial vehicle (UAV)-assisted downlink communications, UAV placement and transmit beamforming are inherently coupled through the propagation geometry. However, fully joint design based on instantaneous channel state information (CSI) is impractical, because the small-scale fading depends on the UAV location to be optimized and thus is unavailable a priori. Moreover, existing joint placement and beamforming methods do not explicitly optimize the UAV position with respect to the geometry-dependent multiuser interference induced by inter-user steering correlation. To address this issue, we propose a correlation-aware geometric framework for joint UAV placement and beamforming. Specifically, the UAV position is first optimized based on long-term channel statistics, where the steering-vector correlation is incorporated into the placement design through a conservative Gaussian surrogate that avoids interference underestimation. The resulting nonconvex positioning problem is then handled using successive convex approximation, auxiliary-variable decoupling, and quadratic transform techniques. For the obtained UAV location, the transmit beamformer is then optimized using instantaneous CSI. Simulation results show that the proposed framework significantly improves the minimum user spectral efficiency by enhancing angular separability among users and reducing inter-user interference. These results demonstrate that UAV placement should be designed not only for desired-link enhancement but also for interference mitigation through geometry-aware user separation.