Deep learning (DL)-based RF fingerprinting (RFFP) technology has emerged as a powerful physical-layer security mechanism, enabling device identification and authentication based on unique device-specific signatures that can be extracted from the received RF signals. However, DL-based RFFP methods face major challenges concerning their ability to adapt to domain (e.g., day/time, location, channel, etc.) changes and variability. This work proposes a novel IQ data representation and feature design, termed Double-Sided Envelope Power Spectrum or EPS, that is proven to overcome the domain adaptation problems significantly. By accurately capturing device hardware impairments while suppressing irrelevant domain information, EPS offers improved feature selection for DL models in RFFP. Experimental evaluations demonstrate its effectiveness, achieving over 99% testing accuracy in same-day/channel/location evaluations and 93% accuracy in cross-day evaluations, outperforming the traditional IQ representation. Additionally, EPS excels in cross-location evaluations, achieving a 95% accuracy. The proposed representation significantly enhances the robustness and generalizability of DL-based RFFP methods, thereby presenting a transformative solution to IQ data-based device fingerprinting.

翻译：基于深度学习的射频指纹（RFFP）技术已成为一种强大的物理层安全机制，它通过从接收到的射频信号中提取设备特有的签名来实现设备识别与认证。然而，基于深度学习的RFFP方法在适应域（如日/时间、位置、信道等）变化和多样性方面面临重大挑战。本文提出一种新型IQ数据表征及特征设计方案——双面包络功率谱（EPS），该方案被证明能显著克服域适应问题。通过精确捕获设备硬件缺陷并抑制无关域信息，EPS为RFFP中的深度学习模型提供了更优的特征选择。实验评估验证了其有效性：在同日/信道/位置评估中实现了超过99%的测试准确率，跨日评估中达到93%的准确率，均优于传统IQ表征。此外，EPS在跨位置评估中表现优异，准确率达95%。所提表征显著提升了基于深度学习的RFFP方法的鲁棒性和泛化能力，为基于IQ数据的设备指纹识别提供了变革性解决方案。