Open-Set RF Fingerprinting via Improved Prototype Learning
Weidong Wang Hongshu Liao Lu Gan
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.