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International Conference on Mathematical Optimization Theory and Operations Research

MOTOR 2022: Mathematical Optimization Theory and Operations Research: Recent Trends pp 315–326Cite as

Variational Autoencoders for Precoding Matrices with High Spectral Efficiency

Part of the Communications in Computer and Information Science book series (CCIS,volume 1661)

Abstract

Neural networks are used for channel decoding, channel detection, channel evaluation, and resource management in multi-input and multi-output (MIMO) wireless communication systems. In this paper, we consider the problem of finding precoding matrices with high spectral efficiency (SE) using variational autoencoder (VAE). We propose a computationally efficient algorithm for sampling precoding matrices with minimal loss of quality compared to the optimal precoding. In addition to VAE, we use the conditional variational autoencoder (CVAE) to build a unified generative model. Both of these methods are able to reconstruct the distribution of precoding matrices of high SE by sampling latent variables. This distribution obtained using VAE and CVAE methods is described in the literature for the first time.

Keywords

  • MIMO
  • Precoding
  • Optimization
  • DL
  • VAE
  • SE
  • SINR

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Acknowledgements

The authors are grateful to Mr. D. Kropotov and Prof. O. Senko for discussions.

Author information

Authors and Affiliations

  1. M. V. Lomonosov Moscow State University, Moscow, Russia

    Evgeny Bobrov

  2. Moscow Research Center, Huawei Technologies, Moscow, Russia

    Evgeny Bobrov

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry Vetrov

  4. Artificial Intelligence Research Institute, Moscow, Russia

    Dmitry Vetrov

  5. Moscow Institute of Physics and Technology, Moscow, Russia

    Alexander Markov & Sviatoslav Panchenko

Authors
  1. Evgeny Bobrov
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  2. Alexander Markov
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  3. Sviatoslav Panchenko
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  4. Dmitry Vetrov
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Corresponding author

Correspondence to Evgeny Bobrov .

Editor information

Editors and Affiliations

  1. Sobolev Institute of Mathematics, Novosibirsk, Russia

    Yury Kochetov

  2. Sobolev Institute of Mathematics, Novosibirsk, Russia

    Anton Eremeev

  3. Melentiev Energy Systems Institute, Irkutsk, Russia

    Oleg Khamisov

  4. Institute of Applied Mathematical Research, Petrozavodsk, Russia

    Anna Rettieva

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Bobrov, E., Markov, A., Panchenko, S., Vetrov, D. (2022). Variational Autoencoders for Precoding Matrices with High Spectral Efficiency. In: Kochetov, Y., Eremeev, A., Khamisov, O., Rettieva, A. (eds) Mathematical Optimization Theory and Operations Research: Recent Trends. MOTOR 2022. Communications in Computer and Information Science, vol 1661. Springer, Cham. https://doi.org/10.1007/978-3-031-16224-4_22

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  • DOI: https://doi.org/10.1007/978-3-031-16224-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16223-7

  • Online ISBN: 978-3-031-16224-4

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