Liu, F;
Yuan, W;
Masouros, C;
Yuan, J;
(2020)
Radar-assisted predictive beamforming for vehicle-to-infrastructure links.
In:
Proceedings of the 2020 IEEE International Conference on Communications Workshops (ICC Workshops).
The Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
© 2020 IEEE. In this paper, we propose a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by relying on the joint sensing and communication functionalities at road side units (RSUs). We present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of the vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. Numerical results have demonstrated that the proposed radar-assisted approach significantly outperforms the communication-only feedback based technique in both the angle tracking and the downlink communication.
Type: | Proceedings paper |
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Title: | Radar-assisted predictive beamforming for vehicle-to-infrastructure links |
Event: | 2020 IEEE International Conference on Communications Workshops (ICC Workshops) |
ISBN-13: | 978-1-7281-7440-2 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/ICCWorkshops49005.2020.9145241 |
Publisher version: | https://doi.org/10.1109/ICCWorkshops49005.2020.914... |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
Keywords: | Radar tracking, Antenna arrays, Array signal processing, Signal to noise ratio, Sensors, Doppler effect |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10111764 |




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