SVM-Based Optimization on the Number of Data Streams for Massive MIMO Systems

被引:5
|
作者
Wang, Shiguo [1 ,2 ]
He, Mingyue [2 ]
Ruby, Rukhsana [3 ]
Zhang, Yongjian [4 ]
机构
[1] Changsha Univ Sci & Technol, Comp & Commun Engn Inst, Changsha 410114, Peoples R China
[2] Xiangtan Univ, Sch Automat & Elect Informat, Xiangtan 411105, Peoples R China
[3] Shenzhen Univ, Coll Comp Sci, Shenzhen 518060, Peoples R China
[4] Univ Int Relat, Sch Informat Sci & Technol, Beijing 100091, Peoples R China
来源
IEEE SYSTEMS JOURNAL | 2023年 / 17卷 / 01期
关键词
Radio frequency; Precoding; Signal to noise ratio; Data models; Massive MIMO; Computational modeling; Adaptation models; Large-scale systems; millimeter-wave (mmWave) communication; multiple-input and multiple-output (MIMO); wireless communication; optimization;
D O I
10.1109/JSYST.2021.3139991
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
For millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems, hardware cost and power consumption can be reduced via hybrid precoding, while keeping almost the same spectral efficiency (SE) as the fully digital precoding scheme. However, for different hardware configuration and channel environments, system SE varies with the number of transmitted data streams at the transmitter, and hence this should be determined primarily before hybrid precoding design. In this article, for single-user massive MIMO systems, a regression model based on the support vector machine technique is proposed, through which the optimal number of data streams can be obtained automatically based on the propagation characteristics of wireless channels, the number of antennas, and RF chains. To validate the correctness of the proposed model, simulation results are presented under various parameter settings.
引用
收藏
页码:83 / 86
页数:4
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