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Neural Networks in Hybrid Precoding for Millimeter Wave Massive MIMO Systems

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Artificial Intelligence for Communications and Networks (AICON 2019)

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Abstract

Neural networks have been applied to the physical layer of wireless communication systems to solve complex problems. In millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid precoding has been considered as an energy-efficient technology to replace fully-digital precoding. The way of designing hybrid precoding in mmWave massive MIMO systems by multi-layer neural networks has not been investigated. Based on further decomposing the baseband precoding matrix, an idea is proposed in this paper to map hybrid precoding structure to a multi-layer neural network. Considering the deterioration in the throughput and energy efficiency of mmWave massive MIMO systems, the feasibility of the proposed idea is analyzed. Moreover, a singular value decomposition (SVD) based decomposing (SVDDE) algorithm is proposed to evaluate the feasibility of the proposed idea. Simulation results indicate that there is an optimal number of users which can minimize the performance deterioration. Moreover, the simulation results also show that slight deterioration in the throughput and energy efficiency of mmWave massive MIMO systems is caused by further decomposing the baseband precoding matrix. In other words, further decomposing the baseband precoding matrix is a feasible way to map the hybrid precoding structure to a multi-layer neural network.

This work was supported by the National Key Research and Development Program of China under Grant 2017YFE0121600.

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Author information

Authors and Affiliations

  1. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Jing Yang, Kai Chen & Xiaohu Ge

  2. School of Electrical and Information Engineering, University of Sydney, Sydney, Australia

    Yonghui Li

  3. Beijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Lin Tian

Authors
  1. Jing Yang
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  2. Kai Chen
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  3. Xiaohu Ge
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  4. Yonghui Li
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  5. Lin Tian
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Corresponding author

Correspondence to Xiaohu Ge .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Shuai Han

  2. Harbin Institute of Technology, Harbin, China

    Liang Ye

  3. Harbin Institute of Technology, Harbin, China

    Weixiao Meng

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Yang, J., Chen, K., Ge, X., Li, Y., Tian, L. (2019). Neural Networks in Hybrid Precoding for Millimeter Wave Massive MIMO Systems. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 286. Springer, Cham. https://doi.org/10.1007/978-3-030-22968-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-22968-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22967-2

  • Online ISBN: 978-3-030-22968-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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