Yin, Guolin, Zhang, Junqing 
ORCID: 0000-0002-3502-2926, Ding, Yuan and Cotton, Simon
(2025)
Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model.
In: 2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025-3-24 - 2025-3-27.
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WCNC2025_RFFI_WiFi.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (868kB) | Preview |
Abstract
Securing Internet of Things (IoT) devices presents increasing challenges due to their limited computational and energy resources. Radio Frequency Fingerprint Identification (RFFI) emerges as a promising authentication technique to identify wireless devices through hardware impairments. RFFI performance under low signal-to-noise ratio (SNR) scenarios is significantly degraded because the minute hardware features can be easily swamped in noise. In this paper, we leveraged the diffusion model to effectively restore the RFF under low SNR scenarios. Specifically, we trained a powerful noise predictor and tailored a noise removal algorithm to effectively reduce the noise level in the received signal and restore the device fingerprints. We used Wi-Fi as a case study and created a testbed involving 6 commercial off-the-shelf Wi-Fi dongles and a USRP N210 software-defined radio (SDR) platform. We conducted experimental evaluations on various SNR scenarios. The experimental results show that the proposed algorithm can improve the classification accuracy by up to 34.9%.
| Item Type: | Conference Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | 40 Engineering, 46 Information and Computing Sciences, 4006 Communications Engineering, 4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software |
| 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: | 11 Aug 2025 13:42 |
| DOI: | 10.1109/wcnc61545.2025.10978824 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3190640 |
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