End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks

Timothy J. O'Shea, Seth Hitefield, Johnathan Corgan

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.

Knowledge Graph

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