End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

This paper presents end-to-end learning from spectrum data-an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to: 1) automatically learn features directly from simple wireless...

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Bibliographic Details
Main Authors: Merima Kulin, Tarik Kazaz, Ingrid Moerman, Eli De Poorter
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Big spectrum data
spectrum monitoring
end-to-end learning
deep learning
convolutional neural networks
wireless signal identification
Online Access:https://ieeexplore.ieee.org/document/8325299/

Internet

https://ieeexplore.ieee.org/document/8325299/

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