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Open-Set RF Fingerprinting via Improved Prototype Learning

Published 24 Jun 2023 in eess.SP and cs.CV | (2306.13895v1)

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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Authors (3)

  1. Weidong Wang 
  2. Hongshu Liao 
  3. Lu Gan 

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