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Convolutional Radio Modulation Recognition Networks

  • Timothy J. O’Shea
  • Johnathan Corgan
  • T. Charles Clancy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 629)

Abstract

We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e 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.

Keywords

Machine learning Radio Software radio Convolutional networks Deep learning Modulation recognition Cognitive radio Dynamic spectrum access 

Notes

Acknowledgments

The authors would like to thank the Bradley Department of Electrical and Computer Engineering at the Virginia Polytechnic Institute and State University, the Hume Center, and DARPA all for their generous support in this work.

This research was developed with funding from the Defense Advanced Research Projects Agency’s (DARPA) MTO Office under grant HR0011-16-1-0002. The views, opinions, and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Timothy J. O’Shea
    • 1
  • Johnathan Corgan
    • 2
  • T. Charles Clancy
    • 1
  1. 1.Bradley Department of Electrical and Computer EngineeringVirginia TechArlingtonUSA
  2. 2.Corgan LabsSan JoseUSA

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