  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.
