Xie, Lingnan 
ORCID: 0009-0008-3753-1231, Peng, Linning ORCID: 0000-0001-5859-7119, Zhang, Junqing ORCID: 0000-0002-3502-2926, Gao, Ang, Fu, Hua ORCID: 0000-0001-7863-2989 and Shi, Junxian ORCID: 0000-0003-4745-2496
(2025)
Channel2Channel: Toward Robust Radio Frequency Fingerprint Extraction and Identification
IEEE Journal on Selected Areas in Communications, 43 (11).
pp. 3737-3751.
ISSN 0733-8716, 1558-0008
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JSAC2025RFFI.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (4MB) | Preview |
Abstract
In radio frequency fingerprint identification (RFFI) systems, mitigating channel interference remains a critical challenge. This paper introduces a robust RFFI system to tackle this issue effectively. Specifically, taking the IEEE 802.11 signal as the case study, a signal representation is designed based on the logarithmic spectrum, while an RFF extractor based on the U-Net neural network is employed which is guided by a proposed Channel2Channel (C2C) algorithm and powered by a designed data augmentation method. Furthermore, a collaborative identification mechanism is proposed based on a support vector machine (SVM) classifier, where a multi-frame RFF fusion method is designed to exploit the diversity across different frames of received signal. Extensive experimental evaluations are performed in various real-world scenarios using 7 mobile phones and a universal software radio peripheral (USRP) X310 receiver, where an average classification accuracy of 95.72% is obtained with a single frame of received signal, outperforming the neural network-based benchmarks, and an average accuracy of 99.46% is acquired with 10 signal frames based on the proposed collaborative identification method. In addition, the deployability of the system on a resource-constrained computing platform is also validated.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 4605 Data Management and Data Science, 46 Information and Computing Sciences, Machine Learning and Artificial Intelligence, Bioengineering, Networking and Information Technology R&D (NITRD) |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 02 Jun 2025 10:34 |
| Last Modified: | 17 Jan 2026 02:52 |
| DOI: | 10.1109/jsac.2025.3584434 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3192953 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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