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

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

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arXiv · 1610.00564

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

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

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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.

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Timothy J. O'Shea, Seth Hitefield, Johnathan Corgan. 2016-10-03. End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks. https://arxiv.org/abs/1610.00564

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