  As a promising non-password authentication technology, radio frequency (RF)
fingerprinting can greatly improve wireless security. Recent work has shown
that RF fingerprinting based on deep learning can significantly outperform
conventional approaches. The superiority, however, is mainly attributed to
supervised learning using a large amount of labeled data, and it significantly
degrades if only limited labeled data is available, making many existing
algorithms lack practicability. Considering that it is often easier to obtain
enough unlabeled data in practice with minimal resources, we leverage deep
semi-supervised learning for RF fingerprinting, which largely relies on a
composite data augmentation scheme designed for radio signals, combined with
two popular techniques: consistency-based regularization and pseudo-labeling.
Experimental results on both simulated and real-world datasets demonstrate that
our proposed method for semi-supervised RF fingerprinting is far superior to
other competing ones, and it can achieve remarkable performance almost close to
that of fully supervised learning with a very limited number of examples.
