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.

翻译：作为一项有前景的非密码认证技术，射频指纹识别可极大提升无线安全性。最新研究表明，基于深度学习的射频指纹识别方法显著优于传统方案。然而，这种优越性主要归功于利用大量标注数据的监督学习，当仅有少量标注数据可用时性能会显著下降，导致现有算法缺乏实用性。考虑到实际应用中通常能以极低成本获取充足的无标注数据，我们提出采用深度半监督学习方案进行射频指纹识别。该方法核心在于为无线电信号设计的复合数据增强方案，并结合一致性正则化与伪标签两种主流技术。在仿真数据集和真实数据集上的实验结果表明，我们提出的半监督射频指纹识别方法远优于其他对比方法，且能在极少量样本条件下实现近乎完全监督学习的优异性能。