This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system, where a UAV acts as the energy transmitter (ET) to deliver radio frequency (RF) energy to a set of energy receivers (ERs). Our aim is to maximize the total harvested energy at all ERs by jointly optimizing the three-dimensional (3D) position of the UAV and beam pattern. However, the optimization problem which takes into account the coverage radius of the UAV and beam scanning range, is formulated as a non-convex problem and hence is difficult to solve. To address this problem, we propose a low-complexity iterative algorithm that decomposes the original problem into three sub-problems and solves the 2D position of the UAV, flight altitude and beam pattern in an iterative manner. In particular, we first apply the exhaustive search algorithm to find the global optimal 2D position of the UAV. Subsequently, we can obtain the optimal UAV’s flight altitude via monotonicity theory. Finally, by applying the Butler Matrix feed network, we propose a multi-beam generation scheme to optimize the beam patterns. Numerical results validate that the theoretical findings and demonstrate that significant performance gain in terms of energy harvesting of all ERs can be achieved by the proposed algorithm in UAV-assisted WPT networks.

本文研究了启用无人机（UAV）的无线电力传输（WPT）系统，其中无人机用作能量发送器（ET），以将射频（RF）能量传递给一组能量接收器（ER）。我们的目标是通过共同优化无人机和波束方向图的三维（3D）位置来最大化所有ER处的总采集能量。然而，考虑到无人机的覆盖半径和光束扫描范围的最优化问题被表述为非凸问题，因此难以解决。为了解决这个问题，我们提出了一种低复杂度的迭代算法，该算法将原始问题分解为三个子问题，并以迭代方式解决了无人机的二维位置，飞行高度和波束方向图。尤其是，我们首先应用穷举搜索算法来找到无人机的全局最优2D位置。随后，我们可以通过单调性理论获得最佳的无人机飞行高度。最后，通过应用巴特勒矩阵馈送网络，我们提出了一种多光束生成方案来优化光束方向图。数值结果验证了理论结果，并证明了通过在无人机辅助WPT网络中提出的算法，可以实现所有ER能量收集方面的显着性能提升。