  Can we distinguish between two wireless transmitters sending exactly the same
message, using the same protocol? The opportunity for doing so arises due to
subtle nonlinear variations across transmitters, even those made by the same
manufacturer. Since these effects are difficult to model explicitly, we
investigate learning device fingerprints using complex-valued deep neural
networks (DNNs) that take as input the complex baseband signal at the receiver.
We ask whether such fingerprints can be made robust to distribution shifts
across time and locations due to clock drift and variations in the wireless
channel. In this paper, we point out that, unless proactively discouraged from
doing so, DNNs learn these strong confounding features rather than the
nonlinear device-specific characteristics that we seek to learn. We propose and
evaluate strategies, based on augmentation and estimation, to promote
generalization across realizations of these confounding factors, using data
from WiFi and ADS-B protocols. We conclude that, while DNN training has the
advantage of not requiring explicit signal models, significant modeling
insights are required to focus the learning on the effects we wish to capture.

Signal Processing;Machine Learning;Machine Learning