The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Radar-Assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing
Open accessAbstract
In vehicular networks of the future, sensing and communication functionalities will be intertwined. In this article, we investigate a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique. Aiming for realizing 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 each vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. To improve the sensing accuracy while guaranteeing the downlink communication sum-rate, we further propose a power allocation scheme for multiple vehicles. Numerical results have shown that the proposed DFRC based beam tracking approach significantly outperforms the communication-only feedback based technique in the tracking performance. Furthermore, the designed power allocation method is able to achieve a favorable performance trade-off between sensing and communication.
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
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Cited by
Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond
Liu, F., Cui, Y., Masouros, C., Xu, J., Han, T. X., Eldar, Y. C., & Buzzi, S. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3156632 · Cited by 3,857
Self-citationEdge artificial intelligence for 6g: vision, enabling technologies, and applications
Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126076 · Cited by 829
“In particular, radar sensing provides a promising way to predict the vehicular links [214] and holds the potential to provide real-time model aggregation via predictive beamforming in the model aggregation procedure.”
MethodCramér-Rao Bound Optimization for Joint Radar-Communication Beamforming
Liu, F., Liu, Y.-F., Li, A., Masouros, C., & Eldar, Y. C. (2022) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3135692 · Cited by 774
Self-citationA Survey of Beam Management for mmWave and THz Communications Towards 6G
Xue, Q., Ji, C., Ma, S., Guo, J., Xu, Y., Chen, Q., & Zhang, W. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361991 · Cited by 280
Zhong, Y., Bi, T., Wang, J., Zeng, J., Huang, Y., Jiang, T., Wu, Q., & Wu, S. (2022) · IEEE Network · doi:10.1109/mnet.001.2100688 · Cited by 90
Metadata
- Authors
- Liu, F., Yuan, W., Masouros, C., & Yuan, J.
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2020-11
- Volume
- 19
- Issue
- 11
- Pages
- 7704-7719
- ISSN
- 1536-1276 (ISSN-L)
- 1558-2248
- OpenAlex
- W3072033075
- Cited by
- 589
- References
- 1
- FWCI
- 30.18
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- Funders
- Marie Skłodowska-Curie Individual Fellowship (793345)
- Australia Research Council Discovery Project (DP190101363)
- Linkage Projects (LP 160100708, LP170101196)
- Engineering and Physical Sciences Research Council (EPSRC) of the U.K. (EP/S028455/1)
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- Liu, F., Yuan, W., Masouros, C., & Yuan, J. (2020). Radar-Assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/twc.2020.3015735
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