Ma, J, Zhang, J 
ORCID: 0000-0002-3502-2926, Shen, G, Peng, L and Marshall, A ORCID: 0000-0002-8058-5242
(2025)
Towards Channel-Robust Radio Frequency Fingerprint Identification Using Contrastive Learning
In: 2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025-3-24 - 2025-3-27.
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WCNC2025_RFFI_LoRa.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that is based on intrinsic hardware impairments. Internet of things (IoT) devices can be identified and classified based on their wireless signals using RFFI. Developing a robust RFFI system that can maintain high classification accuracy across diverse communication scenarios is a critical challenge. In this paper, we proposed a contrastive learning-based RFFI approach to establish a channel-robust system using the spectrogram. Specifically, we leverage contrastive learning in the training stage, which has been implemented with data augmentation techniques to mitigate the influence of channels on RFFI. We carried out extensive experimental evaluations involving a public dataset and a self-collected dataset, both with ten LoRa devices. Utilizing these datasets, the performance of the system has been tested in various channel environments, including stationary, mobile, line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The results demonstrated that our approach is effective and robust to channel variation, achieving 93% and 82% on static and dynamic channels. respectively.
| Item Type: | Conference Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | 4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, Machine Learning and Artificial Intelligence |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 04 Mar 2025 08:31 |
| Last Modified: | 24 Jan 2026 05:10 |
| DOI: | 10.1109/WCNC61545.2025.10978330 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3190639 |
| 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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