  Most prior works on deep learning-based wireless device classification using
radio frequency (RF) data apply off-the-shelf deep neural network (DNN) models,
which were matured mainly for domains like vision and language. However,
wireless RF data possesses unique characteristics that differentiate it from
these other domains. For instance, RF data encompasses intermingled time and
frequency features that are dictated by the underlying hardware and protocol
configurations. In addition, wireless RF communication signals exhibit
cyclostationarity due to repeated patterns (PHY pilots, frame prefixes, etc.)
that these signals inherently contain. In this paper, we begin by explaining
and showing the unsuitability as well as limitations of existing DNN feature
design approaches currently proposed to be used for wireless device
classification. We then present novel feature design approaches that exploit
the distinct structures of the RF communication signals and the spectrum
emissions caused by transmitter hardware impairments to custom-make DNN models
suitable for classifying wireless devices using RF signal data. Our proposed
DNN feature designs substantially improve classification robustness in terms of
scalability, accuracy, signature anti-cloning, and insensitivity to environment
perturbations. We end the paper by presenting other feature design strategies
that have great potentials for providing further performance improvements of
the DNN-based wireless device classification, and discuss the open research
challenges related to these proposed strategies.
