Sensing-Assisted Beam Tracking in V2I Networks: Extended Target Case

Zhen Du; Fan Liu; Zenghui Zhang

ICASSP 2022accepted0 citations

Sensing-Assisted Beam Tracking in V2I Networks: Extended Target Case

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.

BibTeX

@inproceedings{icassp2022_sensingassistedb,
  title = {Sensing-Assisted Beam Tracking in V2I Networks: Extended Target Case},
  author = {Zhen Du and Fan Liu and Zenghui Zhang},
  booktitle = {ICASSP 2022},
  year = {2022}
}