  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, originating from the embedded systems
and the DL research domains. This paper systematically identifies and analyzes
the essential considerations and challenges encountered in the creation of
DL-based RFF systems across their typical development life-cycle, which include
(i) data collection and preprocessing, (ii) training, and finally, (iii)
deployment. Our investigation provides a comprehensive overview of the current
open problems that prevent real deployment of DL-based RFF systems while also
discussing promising research opportunities to enhance the overall accuracy,
robustness, and privacy of these systems.
