  This work introduces DeepCRF, a deep learning framework designed for channel
state information-based radio frequency fingerprinting (CSI-RFF). The
considered CSI-RFF is built on micro-CSI, a recently discovered radio-frequency
(RF) fingerprint that manifests as micro-signals appearing on the channel state
information (CSI) curves of commercial WiFi devices. Micro-CSI facilitates
CSI-RFF which is more streamlined and easily implementable compared to existing
schemes that rely on raw I/Q samples. The primary challenge resides in the
precise extraction of micro-CSI from the inherently fluctuating CSI
measurements, a process critical for reliable RFF. The construction of a
framework that is resilient to channel variability is essential for the
practical deployment of CSI-RFF techniques. DeepCRF addresses this challenge
with a thoughtfully trained convolutional neural network (CNN). This network's
performance is significantly enhanced by employing effective and strategic data
augmentation techniques, which bolster its ability to generalize to novel,
unseen channel conditions. Furthermore, DeepCRF incorporates supervised
contrastive learning to enhance its robustness against noises. Our evaluations
demonstrate that DeepCRF significantly enhances the accuracy of device
identification across previously unencountered channels. It outperforms both
the conventional model-based methods and standard CNN that lack our specialized
training and enhancement strategies.
