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.
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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 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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