Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems

Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The ef...

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Autores principales:	Xiaoping Zhou, Haichao Liu, Bin Wang, Qian Zhang, Yang Wang
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/44bf338295724699b5e669187fedc533
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spelling	oai:doaj.org-article:44bf338295724699b5e669187fedc5332021-11-15T00:33:22ZNovel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems1550-147710.1177/15501477211055376https://doaj.org/article/44bf338295724699b5e669187fedc5332021-11-01T00:00:00Zhttps://doi.org/10.1177/15501477211055376https://doaj.org/toc/1550-1477Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The effectiveness of this method in simple systems has been well verified, but its performance is still unknown due to many problems in real communication such as interference from other users and base stations, and users are constantly on the move. In this article, we propose a dynamic user clustering hybrid precoding method in the high-dimensional millimeter-wave multiple-input multiple-output system, which uses low-dimensional manifolds to avoid complicated calculations when there are many antennas. We model each user set as a novel Convolutional Restricted Boltzmann Machine manifold, and the problem is transformed into cluster-oriented multi-manifold learning. The novel Convolutional Restricted Boltzmann Machine manifold learning seeks to learn embedded low-dimensional manifolds through manifold learning in the face of user mobility in clusters. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.Xiaoping ZhouHaichao LiuBin WangQian ZhangYang WangSAGE PublishingarticleElectronic computers. Computer scienceQA75.5-76.95ENInternational Journal of Distributed Sensor Networks, Vol 17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Xiaoping Zhou Haichao Liu Bin Wang Qian Zhang Yang Wang Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems
description	Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The effectiveness of this method in simple systems has been well verified, but its performance is still unknown due to many problems in real communication such as interference from other users and base stations, and users are constantly on the move. In this article, we propose a dynamic user clustering hybrid precoding method in the high-dimensional millimeter-wave multiple-input multiple-output system, which uses low-dimensional manifolds to avoid complicated calculations when there are many antennas. We model each user set as a novel Convolutional Restricted Boltzmann Machine manifold, and the problem is transformed into cluster-oriented multi-manifold learning. The novel Convolutional Restricted Boltzmann Machine manifold learning seeks to learn embedded low-dimensional manifolds through manifold learning in the face of user mobility in clusters. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.
format	article
author	Xiaoping Zhou Haichao Liu Bin Wang Qian Zhang Yang Wang
author_facet	Xiaoping Zhou Haichao Liu Bin Wang Qian Zhang Yang Wang
author_sort	Xiaoping Zhou
title	Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems
title_short	Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems
title_full	Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems
title_fullStr	Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems
title_full_unstemmed	Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems
title_sort	novel convolutional restricted boltzmann machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/44bf338295724699b5e669187fedc533
work_keys_str_mv	AT xiaopingzhou novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems AT haichaoliu novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems AT binwang novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems AT qianzhang novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems AT yangwang novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems
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Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems

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