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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-11-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211055376 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547910361808896 |
|---|---|
| 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 |
| collection | DOAJ |
| 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 |
| id | doaj-art-44bf338295724699b5e669187fedc533 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2021-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-44bf338295724699b5e669187fedc5332025-02-03T06:43:00ZengWileyInternational Journal of Distributed Sensor Networks1550-14772021-11-011710.1177/15501477211055376Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systemsXiaoping ZhouHaichao LiuBin WangQian ZhangYang WangMillimeter-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.https://doi.org/10.1177/15501477211055376 |
| spellingShingle | 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 International Journal of Distributed Sensor Networks |
| title | 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_short | 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 |
| url | https://doi.org/10.1177/15501477211055376 |
| work_keys_str_mv | AT xiaopingzhou novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems AT haichaoliu novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems AT binwang novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems AT qianzhang novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems AT yangwang novelconvolutionalrestrictedboltzmannmachinemanifoldlearninginspireddynamicuserclusteringhybridprecodingformillimeterwavemassivemultipleinputmultipleoutputsystems |