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

Weijie YUAN; Fan LIU; Christos MASOUROS; Jinhong YUAN; Derrick Wing Kwan NG

doi:10.1109/RadarConf2043947.2020.9266521

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

Weijie YUAN, Fan LIU, Christos MASOUROS, Jinhong YUAN, Derrick Wing Kwan NG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

7 Citations (Scopus)

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.

Original language	English
Title of host publication	2020 IEEE Radar Conference, RadarConf 2020
Publisher	IEEE
ISBN (Electronic)	9781728189420
DOIs	https://doi.org/10.1109/RadarConf2043947.2020.9266521
Publication status	Published - 21 Sept 2020
Externally published	Yes
Event	2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy Duration: 21 Sept 2020 → 25 Sept 2020

Publication series

Name	IEEE National Radar Conference - Proceedings
Volume	2020-September
ISSN (Print)	1097-5659

Conference

Conference	2020 IEEE Radar Conference, RadarConf 2020
Country/Territory	Italy
City	Florence
Period	21/09/20 → 25/09/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Access to Document

10.1109/RadarConf2043947.2020.9266521

Cite this

@inproceedings{b0358a0e83a64c5da22746236ef7f1ac,

title = "Joint Radar-Communication-Based Bayesian Predictive Beamforming for Vehicular Networks",

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.",

author = "Weijie YUAN and Fan LIU and Christos MASOUROS and Jinhong YUAN and NG, {Derrick Wing Kwan}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Radar Conference, RadarConf 2020 ; Conference date: 21-09-2020 Through 25-09-2020",

year = "2020",

month = sep,

day = "21",

doi = "10.1109/RadarConf2043947.2020.9266521",

language = "English",

series = "IEEE National Radar Conference - Proceedings",

publisher = "IEEE",

booktitle = "2020 IEEE Radar Conference, RadarConf 2020",

}

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, RadarConf 2020., 9266521, IEEE National Radar Conference - Proceedings, vol. 2020-September, IEEE, 2020 IEEE Radar Conference, RadarConf 2020, Florence, Italy, 21/09/20. https://doi.org/10.1109/RadarConf2043947.2020.9266521

Joint Radar-Communication-Based Bayesian Predictive Beamforming for Vehicular Networks. / YUAN, Weijie; LIU, Fan; MASOUROS, Christos et al.
2020 IEEE Radar Conference, RadarConf 2020. IEEE, 2020. 9266521 (IEEE National Radar Conference - Proceedings; Vol. 2020-September).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Joint Radar-Communication-Based Bayesian Predictive Beamforming for Vehicular Networks

AU - YUAN, Weijie

AU - LIU, Fan

AU - MASOUROS, Christos

AU - YUAN, Jinhong

AU - NG, Derrick Wing Kwan

PY - 2020/9/21

Y1 - 2020/9/21

N2 - 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.

AB - 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.

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U2 - 10.1109/RadarConf2043947.2020.9266521

DO - 10.1109/RadarConf2043947.2020.9266521

M3 - Conference paper (refereed)

AN - SCOPUS:85098564386

T3 - IEEE National Radar Conference - Proceedings

BT - 2020 IEEE Radar Conference, RadarConf 2020

PB - IEEE

T2 - 2020 IEEE Radar Conference, RadarConf 2020

Y2 - 21 September 2020 through 25 September 2020

ER -

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

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