  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.
