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arXiv:1602.04105cs.LG

Convolutional Radio Modulation Recognition Networks

Timothy J O'Shea, Johnathan Corgan, T. Charles Clancy

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Abstract

We study the adaptation of convolutional neural networks to the complex temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert features which are widely used in the field today and we show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.

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DOI
Subjects
cs.LGMachine Learning cs.CVComputer Vision and Pattern Recognition

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  1. A Beginner's Guide to Understanding Convolutional Neural Networks 1 comment

    • http://arxiv.org/abs/1602.04105# -- This paper is awesome for a use of CNNs, for automatic modulation recognition of RF signals. I'm attempting to use their approach with GNU Radio currently - https://radioml.com/blog/2016/07/18/towards-a-gnu-radio-cnn-...

      chrisrukView on HNParent context

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