Variational Autoencoders for Studying the Manifold of Precoding Matrices with High Spectral Efficiency

• URL: http://arxiv.org/abs/2111.15626v2
• Date: Wed, 1 Dec 2021 06:41:39 GMT
• Title: Variational Autoencoders for Studying the Manifold of Precoding Matrices with High Spectral Efficiency
• Authors: Evgeny Bobrov (1 and 2), Alexander Markov (3), Dmitry Vetrov (3) ((1) Moscow Research Center, Huawei Technologies, Russia, (2) M. V. Lomonosov Moscow State University, Russia, (3) National Research University Higher School of Economics, Russia)
• Abstract summary: We look at how to use a variational autoencoder to find a precoding matrix with a high Spectral Efficiency (SE) Our objective is to create a less time-consuming algorithm with minimum quality degradation.
• Score: 47.187609203210705
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In multiple-input multiple-output (MIMO) wireless communications systems, neural networks have been employed for channel decoding, detection, channel estimation, and resource management. In this paper, we look at how to use a variational autoencoder to find a precoding matrix with a high Spectral Efficiency (SE). To collect optimal precoding matrices, an optimization approach is used. Our objective is to create a less time-consuming algorithm with minimum quality degradation. To build precoding matrices, we employed two forms of variational autoencoders: conventional variational autoencoders (VAE) and conditional variational autoencoders (CVAE). Both methods may be used to study a wide range of optimal precoding matrices. To the best of our knowledge, the development of precoding matrices for the spectral efficiency objective function (SE) utilising VAE and CVAE methods is being published for the first time.

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