Xie, Lingnan, Peng, Linning and Zhang, Junqing 
ORCID: 0000-0002-3502-2926
(2025)
Towards Robust RF Fingerprint Identification Using Spectral Regrowth and Carrier Frequency Offset.
In: IEEE INFOCOM 2025 - IEEE Conference on Computer Communications, 2025-5-19 - 2025-5-22.
|
Text
INFOCOM2025_RFFI_WiFi.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (452kB) | Preview |
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.
| Item Type: | Conference Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | 46 Information and Computing Sciences, 4006 Communications Engineering, 40 Engineering |
| Divisions: | Faculty of Science and Engineering Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 04 Mar 2025 08:30 |
| Last Modified: | 18 Sep 2025 14:34 |
| DOI: | 10.1109/infocom55648.2025.11044651 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3190641 |
Altmetric
Altmetric