Deep Learning Based Communication Over the Air

End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiab...

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Bibliographische Detailangaben
Veröffentlicht in:	IEEE journal of selected topics in signal processing Jg. 12; H. 1; S. 132 - 143
Hauptverfasser:	Dorner, Sebastian, Cammerer, Sebastian, Hoydis, Jakob, Brink, Stephan ten
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Autoencoder communication Communication systems Communications systems Computer simulation Data transmission Deep learning end-to-end learning Hardware modulation neural network Neural networks over-the-air Receivers software-defined radio Source code Synchronism Synchronization Training Transmitters
ISSN:	1932-4553, 1941-0484
Online-Zugang:	Volltext
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