Position Prediction Based Fast Beam Tracking Scheme for Multi-User UAV-mmWave Communications

Unmanned aerial vehicle (UAV) millimeter-wave (mmWave) communication is emerging as a promising technique for future networks with flexible network topology and ultra-high data transmission rate. Within such full-dimensionally dynamic mmWave network, beam-tracking is challenging and critical, especially when all the UAVs are in motion for some collaborative tasks that require high-quality communications. In this paper, we propose a fast beam tracking scheme, which is built on an efficient position prediction of multiple moving UAVs. In particular, a Gaussian process based machine learning scheme is proposed to achieve fast and accurate UAV position prediction with quantifiable positional uncertainty. Based on the prediction results, the beam-tracking can be confined within some specific spatial regions centered on the predicted UAV positions. In contrast to the full-space searching based scheme, our proposed position prediction based beam tracking requires little system overhead and thus achieves high net spectrum efficiency. Moreover, we also propose a practical communication protocol embedding our beam-tracking scheme, which monitors the channel evolution and triggers the UAV position prediction for beam-tracking, transmit-receive beam pair selection and data transmission. Simulation results validate the advantages of our scheme over the existing works.