  Deep learning-based RF fingerprinting offers great potential for improving
the security robustness of various emerging wireless networks. Although much
progress has been done in enhancing fingerprinting methods, the impact of
device hardware stabilization and warm-up time on the achievable fingerprinting
performances has not received adequate attention. As such, this paper focuses
on addressing this gap by investigating and shedding light on what could go
wrong if the hardware stabilization aspects are overlooked. Specifically, our
experimental results show that when the deep learning models are trained with
data samples captured after the hardware stabilizes but tested with data
captured right after powering on the devices, the device classification
accuracy drops below 37%. However, when both the training and testing data are
captured after the stabilization period, the achievable average accuracy
exceeds 99%, when the model is trained and tested on the same day, and achieves
88% and 96% when the model is trained on one day but tested on another day, for
the wireless and wired scenarios, respectively. Additionally, in this work, we
leverage simulation and testbed experimentation to explain the cause behind the
I/Q signal behavior observed during the device hardware warm-up time that led
to the RF fingerprinting performance degradation. Furthermore, we release a
large WiFi dataset, containing both unstable (collected during the warm-up
period) and stable (collected after the warm-up period) captures across
multiple days. Our work contributes datasets, explanations, and guidelines to
enhance the robustness of RF fingerprinting in securing emerging wireless
networks.
