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The vehicular-to-everything (V2X) technology has recently drawn a number ofattentions from both academic and industrial areas. However, the openness ofthe wireless communication system makes it more vulnerable to identityimpersonation and information tampering. How to employ the powerful radiofrequency fingerprint (RFF) identification technology in V2X systems turns outto be a vital and also challenging task. In this paper, we propose a novel RFFextraction method for Long Term Evolution-V2X (LTE-V2X) systems. In order toconquer the difficulty of extracting transmitter RFF in the presence ofwireless channel and receiver noise, we first estimate the wireless channelwhich excludes the RFF. Then, we remove the impact of the wireless channelbased on the channel estimate and obtain initial RFF features. Finally, weconduct RFF denoising to enhance the quality of the initial RFF. Simulation andexperiment results both demonstrate that our proposed RFF extraction schemeachieves a high identification accuracy. Furthermore, the performance is alsorobust to the vehicle speed.

Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology