摘要
目前运营商主要采用NSA组网的方式进行5G网络建设,用户需要通过4G锚点接入5G网络,使得4G/5G之间的协同优化难度增大,同时受限于5G终端信号显示策略等问题,假5G、哑5G及无5G问题较为普遍,严重影响用户的5G体验。针对该问题,基于无线侧CHR/MR定位、用户IMSI关联及5G终端自学习机制实现5G假哑无问题的自动识别和基于MR的5G驻留比统计分析,并具备栅格、小区和用户级4G/5G网络问题分析,支撑5G驻留比提升。
Currently,operators mainly use NSA networking for 5G network constructions.Users need to access 5G networks through 4G anchors,which increases the difficulty in 4G/5G coordination and optimization.In addition,due to the problems such as 5G terminal signal display policies,fake 5G,dumb 5G,and no 5G problems are common,5G user experience is severely affected.The wireless side CHR/MR positioning,user IMSI association,and 5G terminal auto-learning mechanisms are used to achieve the automatic identification of fake 5G,dumb 5G,and no 5G problems and the MR-based 5G resident-ratio statistics analysis.The grid,cell and user-level 4G/5G network problem analysis is provided to improve the 5G resident-ratio.
作者
韩斌杰
赵晓晖
HAN Binjie;ZHAO Xiaohui(China Mobile Communication Group Hebei Co.,Ltd.,Shijiazhuang 050000,China)
出处
《无线电工程》
北大核心
2021年第4期331-335,共5页
Radio Engineering
关键词
5G驻留比
假哑无
4G/5G
MR定位
5G终端自学习
问题识别
5G resident-ratio
fake,dumb and no
4G/5G MR positioning
5G device auto-learning
problem identification
