Location-Aware Predictive Beamforming for UAV Communications: A Deep Learning Approach
Location-Aware Predictive Beamforming for UAV Communications: A Deep Learning Approach
复制标题
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
DOI：
10.1109/lwc.2020.3045150
复制
发表时间：
2021-03-01
影响因子：
6.3
通讯作者：
Ng, Derrick Wing Kwan
中科院分区：
文献类型：
--
作者：
Liu, Chang;Yuan, Weijie;Ng, Derrick Wing Kwan
无人机（UAV）具有高度的移动性和机动性，能够适应不同应用的异构需求，因此，无人机蜂窝连接通信成为实现超第五代（5G）无线网络的一种很有前途的技术。然而，UAV的移动对UAV和地面基站（BS）之间的精确波束对准提出了独特的挑战。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形成方案，用于在动态场景中跟踪无人机通信的波束。具体而言，设计了一种基于长短期记忆（LSTM）的递归神经网络（LRNet）用于无人机位置预测。基于预测的位置，可以确定UAV与BS之间的预测角度，以用于下一时隙中的有效且快速的波束对准，这使得UAV与BS之间能够进行可靠的通信。仿真结果表明，该方法可以获得满意的通信速率，接近于理想的精灵辅助对准方案的通信速率上限。
The cellular-connected unmanned aerial vehicle (UAV) 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 a unique challenge for accurate beam alignment between the UAV and the ground base station (BS). 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 BS can be determined for effective and fast beam alignment in the next time slot, which enables reliable communications between the UAV and the BS. Simulation results demonstrate that the proposed scheme can achieve a satisfactory UAV-to-BS communication rate, which is close to the upper bound of communication rate obtained by the perfect genie-aided alignment scheme.