Particle Filter Enabled Fast And Robust MmWave Beam Tracking Algorithm For Autonomous Driving Vehicles

Driven by the mature implementation and demands for autonomous driving in recent years,the sixth-generation(6G)communication enabled mobile communication system is undergoing an evolution by increasing the system bandwidth,the data transmission rate and the network capacity with different types of new technologies.Considering the low-latency and high data rate requirements in the autonomous driving,the millimeter wave(mmWave)technology can support over tens Gbps information sharing among Internet of Vehicles(IoV).However,the challenging problem is how to achieve the fast and robust mm Wave beam tracking in the IoV scenario.To solve this problem,we propose a vehicle behavior prediction based beam tracking algorithm using the modified particle filter(MPF),and the MPF algorithm are evaluated by the developed hardware testbed.The contributions of this thesis are as follows.We propose to explore the implicit correlation between the characteristics of the beam angle varying process and the real-time driving behaviors with the fine-grained state space model.The beam angle varying process is modeled by the correlated random smooth rate and the steering unexpected change rate,which is iteratively updated according to the real-time driving behaviors.By updating the correlated random smooth rate and the steering unexpected change rate,the fine-grained state space model can distinguish the angular changes under different conditions and maintain a continuous beam tracking in the non-stationary environments.The simulation results show that the proposed beam tracking algorithm can reduce the root mean square error by 10 dB in contrast to conventional algorithms.Besides,we propose a vehicle behavior prediction based beam tracking algorithm using the modified particle filter(MPF),which includes both the geometric based coarse beam estimation and the MPF based fine-grained beam tracking algorithms.To reduce the beam search overhead,the beam space subset is predicted based on the beam change rate and the position-yaw information from autonomous driving vehicles.Furthermore,the MPF algorithm is designed to update the particle weights based on the optimal observation to avoid the particle divergence and the error accumulation.The simulation results show that the proposed beam tracking algorithm can reduce the root mean square error by 10 dB in contrast to conventional PF algorithms.Field test results prove that our proposed algorithms can achieve a stable data rate of 2.8 Gbps within 200 ms latency in a mobile vehicle communication scenario.Finally,the research content of this thesis is summarized,and the future of mm Wave beam tracking algorithm and platform optimization are prospected.