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

TL;DR

This paper presents an adaptive beamforming approach using vehicle tracking with an extended Kalman filter to enhance V2I communication stability and reduce feedback overhead in high-mobility scenarios.

Contribution

It introduces a novel vehicle tracking algorithm based on EKF and a specialized beamforming codebook considering road layout, improving link reliability and service quality.

Findings

EKF-based tracking outperforms existing schemes in high-mobility conditions

Proposed beamforming codebook enhances link stability and service quality

Numerical results confirm improved system performance

Abstract

Vehicle-to-everything communication system is a strong candidate for improving the driving experience and automotive safety by linking vehicles to wireless networks. To take advantage of the full benefits of vehicle connectivity, it is essential to ensure a stable network connection between roadside unit (RSU) and fast-moving vehicles. Based on the extended Kalman filter (EKF), we develop a vehicle 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 the system's link budget. Numerical results verify that the EKF-based vehicle tracking algorithm and the proposed beamforming structure are more suitable for vehicle-to-infrastructure networks compared to existing schemes.