  We investigate sequence machine learning techniques on raw radio signal
time-series data. By applying deep recurrent neural networks we learn to
discriminate between several application layer traffic types on top of a
constant envelope modulation without using an expert demodulation algorithm. We
show that complex protocol sequences can be learned and used for both
classification and generation tasks using this approach.
