Sensing-Assisted Beam Tracking in V2I Networks: Extended Target Case
Zhen Du, Fan Liu, Zenghui Zhang
Abstract
A sensing-assisted predictive beamforming scheme for vehicle-to-infrastructure (V2I) communication is considered, which is built upon massive multi-input-multi-output (mMIMO) and millimeter wave (mmWave) techniques. In practical V2I networks, vehicles cannot be modeled as point targets in terms of the narrow beamwidth and high range resolution. Accordingly, the communication receiver (CR) may be beyond the beam even the vehicle is accurately tracked, which makes robust beam alignment and tracking challenging. We thus consider the extended target case, in which the beamwidth should be adjusted in real-time to cover the entire vehicle. Then an extended Kalman filtering (EKF) is presented to track the CR according to the resolved high-resolution geometry results. Finally, numerical results are provided to validate the effectiveness of the proposed approach.