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Joint Radar-Communication-Based Bayesian Predictive Beamforming for Vehicular Networks

Yuan, W; Liu, F; Masouros, C; Yuan, J; Ng, DWK; (2020) Joint Radar-Communication-Based Bayesian Predictive Beamforming for Vehicular Networks. In: 2020 IEEE Radar Conference (RadarConf20). IEEE: Florence, Italy. Green open access

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Abstract

In this paper, we develop a predictive beamforming scheme based on the dual-functional radar-communication (DFRC) technique, where the road-side units estimates the motion parameters of vehicles exploiting the echoes of the DFRC signals. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the tracking performance. A novel message passing algorithm is proposed, which yields a near optimal performance achieved by the maximum a posteriori estimation. Simulation results have shown the effectiveness of the proposed DFRC based scheme.

Type: Proceedings paper
Title: Joint Radar-Communication-Based Bayesian Predictive Beamforming for Vehicular Networks
Event: IEEE Radar Conference (RadarConf)
Location: Florence, ITALY
Dates: 21 September 2020 - 25 September 2020
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/RadarConf2043947.2020.9266521
Publisher version: http://dx.doi.org/10.1109/RadarConf2043947.2020.92...
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.
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/10123065
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