Beam Prediction and Tracking for UAV: Identify and Exploit Future Information

Because of the flexible scheduling, improved reliability, enhanced capacity over much wider range, the unmanned aerial vehicle (UAV) communications have become an important part of the space-air-ground integrated network. However, the high mobility and perturbation of UAV impose a challenge on aligning narrow beams between the UAV and another node, such as the base station (BS). Although the position and attitude of UAV have been exploited to develop beam tracking algorithms, they belong to current or past information, which often provide limited performance improvement in the high-mobility scenario. To tackle this challenging issue, we, for the first time, identify a kind of important but ready-made information - the command or control sequence (CCS) provided by the flight control system (FCS). We explain in detail that CCS provides real and direct (rather than estimated) future information for beam prediction. Then, we propose an efficient learning-based algorithm to exploit the information. In particular, we prove theoretically that the convolutional neural network (CNN) is an appropriate choice of the network structure within the nonlinear prediction model. Experiment results from practical real UAVs confirm the effectiveness and superiority of our proposal.