Radio Frequency Fingerprinting (RFF) techniques promise to authenticate wireless devices at the physical layer based on inherent hardware imperfections introduced during manufacturing. Such RF transmitter imperfections are reflected into over-the-air signals, allowing receivers to accurately identify the RF transmitting source. Recent advances in Machine Learning, particularly in Deep Learning (DL), have improved the ability of RFF systems to extract and learn complex features that make up the device-specific fingerprint. However, integrating DL techniques with RFF and operating the system in real-world scenarios presents numerous challenges. This article identifies and analyzes these challenges while considering the three reference phases of any DL-based RFF system: (i) data collection and preprocessing, (ii) training, and finally, (iii) deployment. Our investigation points out the current open problems that prevent real deployment of RFF while discussing promising future directions, thus paving the way for further research in the area.

翻译：射频指纹识别（RFF）技术旨在基于制造过程中引入的硬件固有缺陷，在物理层对无线设备进行认证。这些射频发射机的不完善之处会反映在空间信号中，使接收器能够准确识别射频发射源。机器学习的最新进展，尤其是深度学习，提升了射频指纹识别系统提取和学习构成设备特有指纹的复杂特征的能力。然而，将深度学习技术与射频指纹识别相结合并在实际场景中运营系统带来了诸多挑战。本文识别并分析了这些挑战，同时考虑了基于深度学习的射频指纹识别系统的三个参考阶段：（i）数据采集与预处理，（ii）训练，以及最终（iii）部署。我们的研究指出了当前阻碍射频指纹识别实际部署的开放性问题，同时探讨了有前景的未来方向，从而为该领域的进一步研究铺平了道路。