End-to-end radio traffic sequence recognition with recurrent neural networks
Timothy J. O’Shea, Seth D. Hitefield, Johnathan Corgan · 2016
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.