  Deep learning has been widely used in radio frequency (RF) fingerprinting.
Despite its excellent performance, most existing methods only consider a
closed-set assumption, which cannot effectively tackle signals emitted from
those unknown devices that have never been seen during training. In this
letter, we exploit prototype learning for open-set RF fingerprinting and
propose two improvements, including consistency-based regularization and online
label smoothing, which aim to learn a more robust feature space. Experimental
results on a real-world RF dataset demonstrate that our proposed measures can
significantly improve prototype learning to achieve promising open-set
recognition performance for RF fingerprinting.
