Deep Learning Architectures for Modeling Communication Systems

Recently, deep learning for physical layer has been modeled using autoencoders to model the entire communication system end-to-end. We extend these methods to improve the overall performance by adopting various learning strategies when multiple users try to communicate over a shared channel. We cons...

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Bibliographic Details
Published in2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) pp. 1 - 5
Main Authors Chatterjee, Dibyadip, Jaiswal, Harsh, Honakamble, Adarsh, Shenoy, Konchady Gautam
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
autoencoder
deep learning
machine learning
physical layer
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Summary:Recently, deep learning for physical layer has been modeled using autoencoders to model the entire communication system end-to-end. We extend these methods to improve the overall performance by adopting various learning strategies when multiple users try to communicate over a shared channel. We consider cooperative and non-cooperative schemes in the 2-user Gaussian interference channel. Additionally, a simple neural network architecture is provided for wireless communication systems where channel gain matrix either attenuates or, in some cases, results in fading of the message signal sent by the transmitter.
ISSN:2153-1684
DOI:10.1109/ANTS47819.2019.9118132