Research on Intelligent Optimization of Massive MIMO Coverage Based on 5G MR

被引：2
作者：
Xu, Guoping [1 ]
Xiao, Tian [2 ]
Tao, Tao [1 ]
Zhang, Duo [3 ]
Li, Wei [1 ]
Ma, Hongbing [1 ]
机构：
[1] China United Network Commun Grp Co Ltd, Beijing, Peoples R China
[2] China Unicom Res Inst, Beijing, Peoples R China
[3] Beijing Jiaotong Univ, Beijing, Peoples R China
来源：
2020 IEEE INTL SYMP ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, INTL CONF ON BIG DATA & CLOUD COMPUTING, INTL SYMP SOCIAL COMPUTING & NETWORKING, INTL CONF ON SUSTAINABLE COMPUTING & COMMUNICATIONS (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2020) | 2020年
关键词：
5G; Measurement Report; Massive MIMO; Intelligent Optimization;
D O I：
10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00218
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Massive MIMO technology is widely used in 5G networks. It is necessary to do research on new intelligent optimization algorithms to improve network performance. In this paper, an intelligent weight selection and optimization algorithm is proposed to improve network coverage and performance. Firstly, based on the technical characteristics of 5G NSA, the technical scheme for obtaining high-precision latitude and longitude information of NSA MR is proposed. Furthermore, a scheme of Massive MIMO weight intelligent optimization based on MR longitude and latitude information is proposed, and the algorithm of intelligent weight selection is used to search for the optimal solution in the Massive MIMO weight space. Considering the limitation of the existing system, a simplified algorithm is proposed to find the local optimal solution, which improves the practicability and popularization value of the algorithm.
引用
收藏
页码：1455 / 1459
页数：5
相关论文
共 22 条
[21]   A new efficient cross-layer relay node selection model for Wireless Community Mesh Networks [J].
Zhao, Liang ;
Al-Dubai, Ahmed ;
Li, Xianwei ;
Chen, Guolong ;
Min, Geyong .
COMPUTERS & ELECTRICAL ENGINEERING, 2017, 61 :361-372
[22]   Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture [J].
Zhao, Xudong ;
Tao, Ran ;
Li, Wei ;
Li, Heng-Chao ;
Du, Qian ;
Liao, Wenzhi ;
Philips, Wilfried .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (10) :7355-7370
123