Towards Channel-Robust Radio Frequency Fingerprint Identification Using Contrastive Learning


Ma, Jie, Zhang, Junqing ORCID: 0000-0002-3502-2926, Shen, Guanxiong, Peng, Linning and Marshall, Alan 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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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
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 08:31
Last Modified: 05 Jun 2025 03:38
DOI: 10.1109/wcnc61545.2025.10978330
Related Websites:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3190639
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