Towards Robust RF Fingerprint Identification Using Spectral Regrowth and Carrier Frequency Offset

TL;DR

This paper presents a robust RF fingerprint identification method for IEEE 802.11 devices that leverages spectral regrowth and carrier frequency offset to improve accuracy under channel variations and CFO drift.

Contribution

The paper introduces a novel channel-robust RFFI scheme combining spectral regrowth and CFO-assisted deep learning, with a collaborative multi-antenna approach for enhanced device identification.

Findings

Achieved 92.76% average classification accuracy under diverse conditions.

Demonstrated robustness against channel variations and CFO drift over 5 months.

Outperformed existing RF fingerprinting methods in practical experiments.

Abstract

Radio frequency fingerprint identification (RFFI) is a promising device authentication approach by exploiting the unique hardware impairments as device identifiers. Because the hardware features are extracted from the received waveform, they are twisted with the channel propagation effect. Hence, channel elimination is critical for a robust RFFI system. In this paper, we designed a channel-robust RFFI scheme for IEEE 802.11 devices based on spectral regrowth and proposed a carrier frequency offset (CFO)-assisted collaborative identification mechanism. In particular, the spectral regrowth was utilized as a channel-resilient RFF representation which is rooted in the power amplifier nonlinearity. While CFO is time-varying and cannot be used alone as a reliable feature, we used CFO as an auxiliary feature to adjust the deep learning-based inference. Finally, a collaborative identification was adopted to leverage the diversity in a multi-antenna receiver. Extensive experimental evaluations were performed in practical environments using 10 IEEE 802.11 devices and a universal software radio peripheral (USRP) X310 receiver with 4 antennas. The results demonstrated the effectiveness of the proposed method against diverse channel conditions and CFO drift, where an average classification accuracy of 92.76% was achieved against channel variations and a 5-month time span, significantly outperforming existing methods.