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arxiv 1602.04105 v3 pith:VQXMYKJF submitted 2016-02-12 cs.LG cs.CV

Convolutional Radio Modulation Recognition Networks

Timothy J O'Shea , Johnathan Corgan , T. Charles Clancy This is my paper
classification cs.LG cs.CV
keywords convolutionalnetworksradiofeaturesmodulationneuralsignaltemporal
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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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Cited by 2 Pith papers

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  2. SurReal: Fr\'echet Mean and Distance Transform for Complex-Valued Deep Learning

    cs.CV 2019-06 unverdicted novelty 7.0

    SurReal architecture applies weighted Fréchet mean convolution and distance-based FC layers to complex data, improving accuracy on MSTAR (94% to 98%) and RadioML with 8-10% of baseline model size.