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Deep Learning Algorithm for Maximizing the Spectral Efficiency of Wireless Systems

verfasst von: Evgeny Bobrov

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 4/2022

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Abstract

In multiple input multiple output (MIMO) wireless communication systems, neural networks are utilized for resource management, channel decoding, and the finding and assessment of channels. This paper addresses the problem of finding a precoding matrix with a high spectral efficiency (SE) using a variational autoencoder. An optimization procedure for finding optimal precoding matrices is known. The goal of this study is to construct a less time-consuming algorithm than the optimization procedure with minimum loss of quality. As a solution to achieve this goal, two types of variational autoencoders are used to construct precoding matrices: a classical variational autoencoder (VAE) and conditional variational autoencoder (CVAE). Both methods can be used to explore a wide range of optimal precoding matrices. The VAE and CVAE methods make it possible to restore the distribution of the predicted value by sampling random variables from the normal distribution at an intermediate stage of calculations. The construction of precoding matrices and their distribution for the SE objective function using the VAE and CVAE methods is described in a publication for the first time.

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Titel: Deep Learning Algorithm for Maximizing the Spectral Efficiency of Wireless Systems
verfasst von: Evgeny Bobrov
Publikationsdatum: 01.12.2022
Verlag: Pleiades Publishing
Erschienen in: Pattern Recognition and Image Analysis / Ausgabe 4/2022
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661822040034

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