Open-Set RF Fingerprinting via Improved Prototype Learning

• URL: http://arxiv.org/abs/2306.13895v1
• Date: Sat, 24 Jun 2023 08:04:06 GMT
• Title: Open-Set RF Fingerprinting via Improved Prototype Learning
• Authors: Weidong Wang, Hongshu Liao, and Lu Gan
• Abstract summary: We exploit prototype learning for open-set RF fingerprinting. We propose two improvements, including consistency-based regularization and online label smoothing. Experimental results on a real-world RF dataset demonstrate that our proposed measures can significantly improve prototype learning.
• Score: 7.956132769841986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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