The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
V2X-communication assisted interference minimization for automotive radars
Open accessAbstract
With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2X) communication is a potential way for coordinating automotive radars and reduce the mutual interference. In this paper, we analyze the positional relation of the two radars that interfere with each other, and evaluate the mutual interference for different types of automotive radars based on Poisson point process (PPP). We also propose a centralized framework and the corresponding algorithm, which relies on V2X communication systems to allocate the spectrum resources for automotive radars to minimize the interference. The minimum spectrum resources required for zero-interference are analyzed for different cases. Simulation results validate the analysis and show that the proposed framework can achieve near-zero-interference with the minimum spectrum resources.
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“A mobile cloud and edge computing delay prediction framework [38] is proposed to improve real-time communication, and frameworks such as Ref. [90] and Ref. [91] are proposed to minimize mutual interference among V2X communication.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [90]
Citations
Cites
Survey of RF Communications and Sensing Convergence Research
Paul, B., Chiriyath, A. R., & Bliss, D. W. (2016) · IEEE Access · doi:10.1109/access.2016.2639038 · Cited by 477
Vehicular Communication Networks in the Automated Driving Era
Zhang, S., Chen, J., Lyu, F., Cheng, N., Shi, W., & Shen, X. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1701171 · Cited by 165
Key Technologies, Modeling Approaches, and Challenges for Millimeter-Wave Vehicular Communications
Martín-Vega, F. J., Aguayo‐Torres, M. C., Gómez, G., Entrambasaguas, J. T., & Duong, T. Q. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1800109 · Cited by 61
Big wave of the intelligent connected vehicles
Li, Y., Cao, Y., Qiu, H., Gao, L., Du, Z., & Chen, S. (2016) · China Communications · doi:10.1109/cc.2016.7833458 · Cited by 22
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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-citationAutomotive Radar — From First Efforts to Future Systems
Waldschmidt, C., Hasch, J., & Menzel, W. S. (2021) · IEEE Journal of Microwaves · doi:10.1109/jmw.2020.3033616 · Cited by 584
Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy
Zhang, X., Li, J., Zhou, J., Zhang, S., Wang, J., Yuan, Y., Liu, J., & Li, J. (2025) · Automotive Innovation · doi:10.1007/s42154-024-00310-2 · Cited by 103
Zhang, X., Liu, Z., Kong, Y., & Li, C. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3132924 · Cited by 55
Mutual Interference Mitigation for Multiple Connected Automotive Radar Systems
Bose, A., Tang, B., Soltanalian, M., & Li, J. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3108714 · Cited by 32
Metadata
- Authors
- Huang, J., Fei, Z., Wang, T., Wang, X., Liu, F., Zhou, H., Zhang, J. A., & Wei, G.
- Affiliations
- Beijing Institute of Technology (CN)
- University College London (GB)
- University of Technology Sydney (AU)
- ORCID
- Venue
- China Communications
- Published
- 2019-10
- Volume
- 16
- Issue
- 10
- Pages
- 100-111
- ISSN
- 1673-5447 (ISSN-L)
- OpenAlex
- W3023165390
- Cited by
- 40
- References
- 4
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 8 · 2023: 6 · 2022: 7 · 2021: 7 · 2020: 2
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- green
- discovery.ucl.ac.uk
- Locations
- China Communications (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- European Commission (793345)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Keywords
- vehicle-to-everything communication, automotive radar, interference minimization, spectrum allocation, Poisson point process
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