The accurate identification of wireless devices is
critical for enabling automated network access monitoring and
authenticated data communication in large-scale networks; e.g.,
IoT networks. RF fingerprinting has emerged as a potential solution
for device identification by leveraging the transmitter unique
manufacturing impairments of the RF components. Although
deep learning is proven efficient in classifying devices based
on the hardware impairments, trained models perform poorly
due to channel variations. That is, although training and testing
neural networks using data generated during the same period
achieve reliable classification, testing them on data generated at
different times degrades the accuracy substantially. To the best
of our knowledge, we are the first to propose to leverage MIMO
capabilities to mitigate the channel effect and provide a channelresilient
device classification. For the proposed technique we
show that, for Rayleigh channels, blind partial channel estimation
enabled by MIMO increases the testing accuracy by up to 40%
when the models are trained and tested over the same channel,
and by up to 60% when the models are tested on a channel that
is different from that used for training.
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This content will become publicly available on June 11, 2025
Unsupervised Contrastive Learning for Robust RF Device Fingerprint Under Time-Domain Shift
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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- Award ID(s):
- 2003273
- NSF-PAR ID:
- 10512357
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE International Conference on Communications
- Subject(s) / Keyword(s):
- Domain adaptation, device classification, deep neural networks, contrastive learning, RF fingerprinting.
- Format(s):
- Medium: X
- Location:
- Denver, CO
- Sponsoring Org:
- National Science Foundation
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