An Introduction to Deep Learning for the Physical Layer

We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize trans...

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Bibliographic Details
Published inIEEE transactions on cognitive communications and networking Vol. 3; no. 4; pp. 563 - 575
Main Authors O'Shea, Timothy, Hoydis, Jakob
Format Journal Article
LanguageEnglish
Published IEEE 01.12.2017
Subjects
Artificial neural networks
cognitive radio
Communication systems
deep learning
digital communications
Machine learning
Modulation
Physical layer
radio communication
Radio transmitters
Receivers
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Summary:We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded with a discussion of open challenges and areas for future investigation.
ISSN:2332-7731
2332-7731
DOI:10.1109/TCCN.2017.2758370