Deep convolutional neural network based antenna selection in multiple-input multiple-output system

Jiaxin Cai; Yan Li; Ying Hu

doi:10.1117/12.2317603

5 March 2018 Deep convolutional neural network based antenna selection in multiple-input multiple-output system

Jiaxin Cai, Yan Li, Ying Hu

Proceedings Volume 10710, Young Scientists Forum 2017; 1071024 (2018) https://doi.org/10.1117/12.2317603
Event: Young Scientists Forum 2017, 2017, Shanghai, China

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.

Citation Download Citation

Jiaxin Cai, Yan Li, and Ying Hu "Deep convolutional neural network based antenna selection in multiple-input multiple-output system", Proc. SPIE 10710, Young Scientists Forum 2017, 1071024 (5 March 2018); https://doi.org/10.1117/12.2317603

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