  We present complex-valued Convolutional Neural Networks (CNNs) for RF
fingerprinting that go beyond translation invariance and appropriately account
for the inductive bias with respect to multipath propagation channels, a
phenomenon that is specific to the fields of wireless signal processing and
communications. We focus on the problem of fingerprinting wireless IoT devices
in-the-wild using Deep Learning (DL) techniques. Under these real-world
conditions, the multipath environments represented in the train and test sets
will be different. These differences are due to the physics governing the
propagation of wireless signals, as well as the limitations of practical data
collection campaigns. Our approach follows a group-theoretic framework,
leverages prior work on DL on manifold-valued data, and extends this prior work
to the wireless signal processing domain. We introduce the Lie group of
transformations that a signal experiences under the multipath propagation model
and define operations that are equivariant and invariant to the frequency
response of a Finite Impulse Response (FIR) filter to build a ChaRRNet. We
present results using synthetic and real-world datasets, and we benchmark
against a strong baseline model, that show the efficacy of our approach. Our
results provide evidence of the benefits of incorporating appropriate wireless
domain biases into DL models. We hope to spur new work in the area of robust RF
machine learning, as the 5G revolution increases demand for enhanced security
mechanisms.
