This paper presents a novel method for classifying radio frequency (RF) devices from their transmission signals. Given a collection of signals from identical devices, we accurately classify both the distance of the transmission and the specific device identity. We develop a multiple classifier system that accurately discriminates between channels and classifies devices using normalized in-phase and quadrature (IQ) samples. Our network uses residual connections for both distance and device classification, reaching 88.33% accuracy classifying 16 unique devices over 11 different distances and two different times, on a task that was previously unlearnable. Furthermore, we demonstrate the efficacy for pre-training neural networks for massive data domains and subtle classification differences.
@article{arxiv.2010.05169,
title = {End-to-End Radio Fingerprinting with Neural Networks},
author = {Ryan M. Dreifuerst and Andrew Graff and Sidharth Kumar and Clive Unger and Dylan Bray},
journal= {arXiv preprint arXiv:2010.05169},
year = {2020}
}