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

Jia-Xin Cai1, Ranxu Zhong2, Yan Li3

  • 1School of Applied Mathematics, Xiamen University of Technology, Xiamen, P.R. China.

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|May 3, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning antenna selection method for Multiple-Input Multiple-Output (MIMO) systems. The novel approach optimizes antenna subsets, improving communication performance and reducing complexity compared to existing techniques.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Antenna selection in Multiple-Input Multiple-Output (MIMO) systems is crucial for balancing communication performance and computational complexity.
  • Deep learning (DL) methods show significant promise in various application fields, including wireless communications.

Purpose of the Study:

  • To propose a novel deep learning (DL) based antenna selection technique for MIMO systems.
  • To enhance communication performance while managing computational complexity in wireless systems.

Main Methods:

  • Generated training data labels by maximizing channel capacity.
  • Utilized a deep convolutional neural network (CNN) to analyze channel matrices and exploit latent cues.
  • Employed the CNN for optimal antenna subset selection based on assigned class labels.

Main Results:

  • The proposed DL-based method achieved superior performance compared to state-of-the-art baselines.
  • Demonstrated effectiveness in data-driven antenna selection for MIMO systems.

Conclusions:

  • The DL-based antenna selection technique offers a promising solution for optimizing MIMO systems.
  • The method effectively balances performance and complexity, outperforming existing approaches.