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

TL;DR

This paper introduces a channel estimation-based method for extracting radio frequency fingerprints in LTE-V2X systems, improving vehicle identification accuracy and robustness against noise and mobility.

Contribution

A novel RFF extraction approach that effectively isolates and denoises RF features in LTE-V2X by estimating and removing wireless channel effects.

Findings

High identification accuracy achieved

Robust performance under varying vehicle speeds

Effective channel estimation and noise reduction

Abstract

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.