Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications. However, the movement of UAVs impose challenge for accurate beam alignment between the UAV and the ground user equipment (UE). In this letter, we propose a deep learning-based location-aware predictive beamforming scheme to track the beam for UAV communications in a dynamic scenario. Specifically, a long short-term memory (LSTM)-based recurrent neural network (LRNet) is designed for UAV location prediction. Based on the predicted location, a predicted angle between the UAV and the UE can be determined for effective and fast beam alignment in the next time slot, which enables reliable communications between the UAV and the UE. Simulation results demonstrate that the proposed scheme can achieve a satisfactory UAV-to-UE communication rate, which is close to the upper bound of communication rate obtained by the perfect genie-aided alignment scheme.

中文翻译：

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用于无人机通信的位置感知预测波束成形：一种深度学习方法

由于无人机的高机动性和机动性，可以适应不同应用的异构需求，无人机（UAV）辅助通信成为实现超第五代（5G）无线网络的有前途的技术。然而，无人机的移动对无人机和地面用户设备 (UE) 之间的精确波束对准提出了挑战。在这封信中，我们提出了一种基于深度学习的位置感知预测波束成形方案，以在动态场景中跟踪无人机通信的波束。具体而言，基于长短期记忆 (LSTM) 的循环神经网络 (LRNet) 设计用于无人机位置预测。基于预测的位置，可以确定无人机和 UE 之间的预测角度，以便在下一个时隙中进行有效和快速的波束对准，这使得 UAV 和 UE 之间能够进行可靠的通信。仿真结果表明，所提出的方案能够获得令人满意的UAV-to-UE通信速率，接近完美精灵辅助对准方案获得的通信速率上限。