  The vehicular-to-everything (V2X) technology has recently drawn a number of
attentions 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 also 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.
