Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift

• URL: http://arxiv.org/abs/2403.04036v1
• Date: Wed, 6 Mar 2024 20:33:55 GMT
• Title: Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift
• Authors: Jun Chen, Weng-Keen Wong, Bechir Hamdaoui
• Abstract summary: Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for automated wireless device identification and classification. It faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings. This paper introduces a novel solution that leverages contrastive learning to mitigate this domain shift problem.
• Score: 12.443489826220183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for enabling automated wireless device identification and classification. However, it faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings, potentially degrading the accuracy of RF-based device classification when testing and training data is collected in different domains. This paper introduces a novel solution that leverages contrastive learning to mitigate this domain shift problem. Contrastive learning, a state-of-the-art self-supervised learning approach from deep learning, learns a distance metric such that positive pairs are closer (i.e. more similar) in the learned metric space than negative pairs. When applied to RF fingerprinting, our model treats RF signals from the same transmission as positive pairs and those from different transmissions as negative pairs. Through experiments on wireless and wired RF datasets collected over several days, we demonstrate that our contrastive learning approach captures domain-invariant features, diminishing the effects of domain-specific variations. Our results show large and consistent improvements in accuracy (10.8\% to 27.8\%) over baseline models, thus underscoring the effectiveness of contrastive learning in improving device classification under domain shift.

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