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Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks.

Authors :
Cai, Jia-xin
Zhong, Ranxu
Li, Yan
Source :
PLoS ONE; 5/1/2019, Vol. 14 Issue 5, p1-16, 16p
Publication Year :
2019

Abstract

Antenna selection in Multiple-Input Multiple-Output (MIMO) systems has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity. Recently, deep learning based methods have achieved promising performance in many application fields. This paper proposed a deep learning (DL) based antenna selection technique. First, we generated the label of training antenna systems by maximizing the channel capacity. Then, we adopted the deep convolutional neural network (CNN) on the channel matrices to explicitly exploit the massive latent cues of attenuation coefficients. Finally, we used the adopted CNN to assign the class label and then select the optimal antenna subset. Experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based antenna selection. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19326203
Volume :
14
Issue :
5
Database :
Complementary Index
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
136168092
Full Text :
https://doi.org/10.1371/journal.pone.0215672