ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting
This work addresses the need for robust device identification in wireless communications, particularly for security in 5G networks, by introducing a novel method that is incremental in adapting existing group-theoretic frameworks to the wireless domain.
The authors tackled the problem of RF fingerprinting for wireless IoT devices under real-world multipath channel variations by developing complex-valued CNNs that incorporate domain-specific inductive biases, achieving improved performance over a strong baseline on synthetic and real-world datasets.
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.