2000 character limit reached
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks (1610.00564v1)
Published 3 Oct 2016 in cs.LG and cs.NI
Abstract: 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.