ConvAE: A New Channel Autoencoder Based on Convolutional Layers and Residual Connections

In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing computational complexity at run-time compared with conventional channel autoencoders. The simulations using both co...

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Bibliographic Details
Published in:IEEE communications letters Vol. 23; no. 10; pp. 1769 - 1772
Main Authors: Ji, Dong Jin, Park, Jinsol, Cho, Dong-Ho
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autoencoder
Bit error rate
Codes
Computer simulation
Configuration management
Convolutional codes
convolutional neural network
deep learning
MIMO (control systems)
MIMO communication
multiple input multiple output
Peak to average power ratio
Receivers
Training
Transmitters
ISSN:1089-7798, 1558-2558
Online Access:Get full text
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Summary:In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing computational complexity at run-time compared with conventional channel autoencoders. The simulations using both conventional and proposed autoencoders for a 2-by-2 multiple-input multiple-output (MIMO) system under Rayleigh and Nakagami-m fading show that the ConvAE is able to attain a lower bit error rate and higher achievable rate relative to the conventional channel autoencoder schemes.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2019.2930287