  We conduct an in depth study on the performance of deep learning based radio
signal classification for radio communications signals. We consider a rigorous
baseline method using higher order moments and strong boosted gradient tree
classification and compare performance between the two approaches across a
range of configurations and channel impairments. We consider the effects of
carrier frequency offset, symbol rate, and multi-path fading in simulation and
conduct over-the-air measurement of radio classification performance in the lab
using software radios and compare performance and training strategies for both.
Finally we conclude with a discussion of remaining problems, and design
considerations for using such techniques.
