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

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

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

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arxiv 1610.00564 v1 pith:WGGW3RMN submitted 2016-10-03 cs.LG cs.NI

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

Timothy J. O'Shea , Seth Hitefield , Johnathan Corgan This is my paper

classification cs.LG cs.NI

keywords deepnetworksneuralradiorecurrentsequencetrafficalgorithm

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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.

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

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