ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting

TL;DR

This paper introduces ChaRRNets, a novel complex-valued CNN architecture that incorporates domain-specific invariances for RF fingerprinting, improving robustness in multipath wireless environments for IoT device identification.

Contribution

The paper develops a group-theoretic, invariant CNN framework tailored for wireless RF fingerprinting, extending prior manifold-based DL methods to account for multipath propagation effects.

Findings

ChaRRNets outperform baseline models on synthetic and real-world datasets.

Incorporating wireless domain biases improves model robustness against multipath variations.

Benchmark results demonstrate the effectiveness of the proposed approach.

Abstract

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.