The Radio frequency (RF) fingerprinting technique makes highly secure device authentication possible for future networks by exploiting hardware imperfections introduced during manufacturing. Although this technique has received considerable attention over the past few years, RF fingerprinting still faces great challenges of channel-variation-induced data distribution drifts between the training phase and the test phase. To address this fundamental challenge and support model training and testing at the edge, we propose a federated RF fingerprinting algorithm with a novel strategy called model transfer and adaptation (MTA). The proposed algorithm introduces dense connectivity among convolutional layers into RF fingerprinting to enhance learning accuracy and reduce model complexity. Besides, we implement the proposed algorithm in the context of federated learning, making our algorithm communication efficient and privacy-preserved. To further conquer the data mismatch challenge, we transfer the learned model from one channel condition and adapt it to other channel conditions with only a limited amount of information, leading to highly accurate predictions under environmental drifts. Experimental results on real-world datasets demonstrate that the proposed algorithm is model-agnostic and also signal-irrelevant. Compared with state-of-the-art RF fingerprinting algorithms, our algorithm can improve prediction performance considerably with a performance gain of up to 15\%.
翻译:通过利用制造过程中引进的硬件缺陷,无线电频率(RF)指纹技术为未来网络提供了高度安全的装置认证。虽然这一技术在过去几年中受到相当的注意,但RF指纹在培训阶段和测试阶段之间仍面临频道变换引起的数据分布漂移的巨大挑战。为了应对这一基本挑战,支持边缘地区的示范培训和测试,我们提议采用一个称为模式转移和适应(MTA)的新战略,采用联合式的RF指纹算法,为未来网络提供高度安全的装置认证。提议的算法使同级层之间大量连接到RF指纹,以提高学习的准确性和降低模型复杂性。此外,我们在联邦化学习的背景下实施拟议的算法,使我们的算法通信效率更高,并维护隐私。为了进一步克服数据错配的挑战,我们把学到的模型从一个频道条件转移到另一个条件,并调整到信息数量有限的其他频道条件,导致在环境漂移下作出高度准确的预测。现实世界数据集的实验结果表明,拟议的算法是模型-不相干,也可以与信号相关。此外,我们还采用了一种州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-