Deep Learning in Physical Layer Communications

DL has shown great potential to revolutionize communication systems. This article provides an overview of the recent advancements in DL-based physical layer communications. DL can improve the performance of each individual block in communication systems or optimize the whole transmitter/receiver. Th...

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Bibliographic Details
Published inIEEE wireless communications Vol. 26; no. 2; pp. 93 - 99
Main Authors Qin, Zhijin, Ye, Hao, Li, Geoffrey Ye, Juang, Biing-Hwang Fred
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Biological neural networks
Communication
Communication systems
Deep learning
Gallium nitride
Mathematical model
Neurons
Performance enhancement
Physical layer
Signal detection
Training
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ISSN1536-1284
1558-0687
DOI10.1109/MWC.2019.1800601

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Summary:DL has shown great potential to revolutionize communication systems. This article provides an overview of the recent advancements in DL-based physical layer communications. DL can improve the performance of each individual block in communication systems or optimize the whole transmitter/receiver. Therefore, we categorize the applications of DL in physical layer communications into systems with and without block structures. For DL-based communication systems with the block structure, we demonstrate the power of DL in signal compression and signal detection. We also discuss the recent endeavors in developing DL-based end-to-end communication systems. Finally, potential research directions are identified to boost intelligent physical layer communications. Introduction
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ISSN:1536-1284
1558-0687
DOI:10.1109/MWC.2019.1800601