Location-Aware Predictive Beamforming for UAV Communications: A Deep Learning Approach

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.