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

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.

arXiv comment14 pages, 11 figures, accepted to IEEE Transactions on Vehicular Technology

References in corpus (7)

• Spatially Sparse Precoding in Millimeter Wave MIMO Systems
• Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
• Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks
• Hierarchical Codebook Design for Beamforming Training in Millimeter-Wave Communication
• Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training with Memory
• Adaptive Beam Tracking with the Unscented Kalman Filter for Millimeter Wave Communication
• Beam Designs for Millimeter-Wave Backhaul with Dual-Polarized Uniform Planar Arrays