Mobility-Aware Adaptive Beam Tracking for Vehicles in MmWave Communication Networks

The millimeter wave(mmWave)is a po-tential solution for high data rate communication due to its availability of large bandwidth.However,it is challenging to perform beam tracking in vehicular mm Wave communication systems due to high mobil-ity and narrow beams.In this paper,an adaptive beam tracking algorithm is proposed to improve the network throughput performance while reducing the training signal overhead.In particular,based on the mobility prediction at base station(BS),a novel frame structure with dynamic bundled timeslot is designed.More-over,an actor-critic reinforcement learning based al-gorithm is proposed to obtain the joint optimization of both beam width and the number of bundled timeslots,which makes the beam tracking adapt to the chang-ing environment.Simulation results demonstrate that,compared with the traditional full scan and Kalman fil-ter based beam tracking algorithms,our proposed al-gorithm can improve the time-averaged throughput by 11.34％and 24.86％respectively.With the newly de-signed frame structure,it also outperforms beam track-ing with conventional frame structure,especially in scenarios with large range of vehicle speeds.