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Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology
Open accessAbstract
The vehicle-to-everything (V2X) technology has recently drawn attention from both academic and industrial areas. However, the openness of the wireless communication system makes it more vulnerable to identity impersonation and information tampering. How to employ the powerful radio frequency fingerprint (RFF) identification technology in V2X systems turns out to be a vital and challenging task. In this paper, we propose a novel RFF extraction method for Long Term Evolution-V2X (LTE-V2X) systems. In order to conquer the difficulty of extracting transmitter RFF in the presence of wireless channel and receiver noise, we first estimate the wireless channel which excludes the RFF. Then, we remove the impact of the wireless channel based on the channel estimate and obtain initial RFF features. Finally, we conduct RFF denoising to enhance the quality of the initial RFF. Simulation and experiment results both demonstrate that our proposed RFF extraction scheme achieves a high identification accuracy. Furthermore, the performance is also robust to the vehicle speed.
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“Besides, Chen et al. [77] suggested a solution that involves estimating the carrier frequency offset (CFO) by performing autocorrelation between two adjacent identical primary synchronization signal (PSS) symbols and two adjacent identical secondary synchronization signal (SSS) symbols.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [77]
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References 18 · Cited by 22
Research works printed but not linked: 20
- 10.1109/COMST.2020.3029723 · DOI
- 10.1109/VTCFall.2019.8891313 · DOI
- 10.1109/TVT.2020.3037899 · DOI
- 10.1109/MNET.011.1900628 · DOI
- 10.1109/MSP.2018.3761723 · DOI · Paper page
- 10.1109/MWC.2010.5601959 · DOI
- 10.1109/SIU.2017.7960417 · DOI
- 10.1109/TIFS.2011.2162949 · DOI
- 10.1109/GLOCOM.2008.ECP.421 · DOI
- 10.1109/JIOT.2018.2838071 · DOI
- 10.1109/TIFS.2022.3152404 · DOI
- 10.1109/LWC.2021.3106396 · DOI
- 10.1109/jiot.2022.3154595 · DOI
- 10.1109/TIFS.2016.2552146 · DOI
- LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation (Release 14)
- 10.1109/26.650240 · DOI
- 10.1109/78.599949 · DOI
- 10.1109/VETEC.1995.504981 · DOI
- 2010, Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception (Release 9)
- Breiman, 2001, Random forests, Machine learning, 45, 5, 10.1023/A:1010933404324 · DOI · Paper page
Metadata
- Authors
- Chen, T., Shen, H., Hu, A., He, W., Xu, J., & Hu, H.
- Affiliations
- Southeast University (CN)
- Purple Mountain Laboratories (CN)
- China Automotive Innovation Corporation,Nanjing,China
- Venue
- 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)
- Publisher
- IEEE
- Published
- 2022-09
- Pages
- 1-6
- ISSN
- 1090-3038 (ISSN-L)
- OpenAlex
- W4317418952
- Cited by
- 22
- References
- 18
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 4 · 2023: 4
- FWCI
- 2.63
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Vehicular Technology Conference (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Keywords
- radio frequency fingerprinting, LTE-V2X, vehicle-to-everything communication, channel estimation, transmitter identification, identity impersonation
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- CoChat. (2026). Citation check: Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology. https://app.cochat.ai/papers/10.1109/vtc2022-fall57202.2022.10012865
- The paper
- Chen, T., Shen, H., Hu, A., He, W., Xu, J., & Hu, H. (2022). Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology. 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). https://doi.org/10.1109/vtc2022-fall57202.2022.10012865
