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Proceedings Paper

Deep convolutional neural network based antenna selection in multiple-input multiple-output system
Author(s): Jiaxin Cai; Yan Li; Ying Hu
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Paper Abstract

Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.

Paper Details

Date Published: 5 March 2018
PDF: 6 pages
Proc. SPIE 10710, Young Scientists Forum 2017, 1071024 (5 March 2018); doi: 10.1117/12.2317603
Show Author Affiliations
Jiaxin Cai, Xiamen Univ. of Technology (China)
Yan Li, Xiamen Univ. of Technology (China)
Ying Hu, Ennew Digital Technology Research Institute (China)
Ennew Super-Brain Technology LLC (China)


Published in SPIE Proceedings Vol. 10710:
Young Scientists Forum 2017
Songlin Zhuang; Junhao Chu; Jian-Wei Pan, Editor(s)

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