  In recent years, Deep Learning (DL) has been successfully applied to detect
and classify Radio Frequency (RF) Signals. A DL approach is especially useful
since it identifies the presence of a signal without needing full protocol
information, and can also detect and/or classify non-communication waveforms,
such as radar signals. In this work, we focus on the different pre-processing
steps that can be used on the input training data, and test the results on a
fixed DL architecture. While previous works have mostly focused exclusively on
either time-domain or frequency domain approaches, we propose a hybrid image
that takes advantage of both time and frequency domain information, and tackles
the classification as a Computer Vision problem. Our initial results point out
limitations to classical pre-processing approaches while also showing that it's
possible to build a classifier that can leverage the strengths of multiple
signal representations.
