Adaptive Beam Design for V2I Communications using Vehicle Tracking with Extended Kalman Filter

Abstract

A vehicle-to-everything communication system is a strong candidate for improving the driving experience and automotive safety by linking vehicles to wireless networks. To guarantee the full benefits of vehicle connectivity, it is essential to ensure a stable network connection between the roadside unit (RSU) and fast-moving vehicles. Based on an extended Kalman filter (EKF), we develop a beam-tracking algorithm to enable reliable radio connections. For the vehicle tracking algorithm, we focus on estimating the rapid changes in the beam direction of a high-mobility vehicle while reducing the feedback overhead. Furthermore, we design a beamforming codebook that considers the road layout and RSU. By leveraging the proposed beamforming codebook, vehicles on the road can expect a service quality similar to that of conventional cellular services. Finally, a beamformer selection algorithm is developed to secure sufficient gain for a link budget. Numerical results verify that the EKFbased vehicle tracking algorithm and the proposed beamforming structure are more suitable for vehicle-to-infrastructure networks compared to existing schemes.