Unmanned aerial vehicles (UAVs) with flexible deployment contribute to enlarging the distance of information transmission to mobile users (MUs) in constrained environment. However, due to the high mobility of both UAVs and MUs, it is challenging to establish an accurate beam towards the target MU with high beam gain in real-time. In this study, UAV base stations (UAV-BSs) consisting of position-known assisted UAVs (A-UAVs) and position-unknown assisted UAVs (U-UAVs) are employed to transmit data to MUs. Specifically, a bi-directional angle-aware beam tracking with adaptive beam reconstruction (BAB-AR) algorithm is proposed to construct an optimal beam that can quickly adapt the movement of the target MU. First, the angle-aware beam tracking is realized within the UAVBSs using a proposed global dynamic crow search algorithm without historical trajectory. Furthermore, the Gaussian process regression model is trained by A-UAVs to predict the azimuth and elevation angles of MUs. Meanwhile, we focus on the beam width and design a time interval adjustment mechanism for adaptive beam reconstruction to track high-speed MUs. Finally, the performance of the BAB-AR algorithm is compared with that of benchmark algorithms, and simulate results verifies that the BAB-AR algorithm can construct an accurate beam capable of covering high-speed MUs with the half power beam width in a timely manner.

翻译：灵活部署的无人机可扩大受限环境下向移动用户的信息传输距离。然而，由于无人机和移动用户均具有高机动性，实时建立具有高波束增益的精确目标波束极具挑战性。本研究采用由位置已知辅助无人机和位置未知辅助无人机组成的无人机基站向移动用户传输数据。具体而言，提出了一种双向角度感知波束跟踪与自适应波束重构算法，用于构建能快速适应目标移动用户运动的最优波束。首先，通过提出的无需历史轨迹的全局动态乌鸦搜索算法，在无人机基站内实现角度感知波束跟踪。其次，利用位置已知辅助无人机训练高斯过程回归模型，预测移动用户的方位角和俯仰角。同时，针对波束宽度问题，设计了一种时间间隔调整机制用于自适应波束重构，以跟踪高速移动用户。最后，将所提算法与基准算法进行性能对比，仿真结果表明该算法能及时构建覆盖高速移动用户且具有半功率波束宽度的精确波束。