摘要
5G网络运营商在努力满足数据需求和新应用支持的建设过程中,面临一个关键问题就是基站密度的需求。实际的建设规划中考虑到成本和一些其他因素必然会有区域弱覆盖无法完全解决的情况,这时需要筛选出业务量高的弱覆盖区域优先安排解决。对弱覆盖点进行区域k-means聚类分析,首先根据聚类原则进行数据清洗;然后导入数据通过Matlab拟合与演算,得到弱覆盖区域,计算聚类中心点,推选最优解法,编辑运算最佳数据值,得到所有弱覆盖区域现网址坐标的聚类中心值;最后通过戴维森堡丁指数(DBI)对聚类方法复杂度进行检验。
The feature of 5G network's large bandwidth is that it greatly improves the transmission efficiency.One of the key issues operators face in their efforts to meet data demands and build new application support is the need for base station density.Considering the cost and some other factors in the actual construction planning,there will inevitably be a situation that the weak coverage of the area can not be completely solved,then it is necessary to screen out the weak coverage area with high business volume.The region k-means cluster analysis is carried out for weak coverage points.Firstly,data cleaning is carried out according to the clustering principle.Then importing data through Matlab fitting and calculation was carried out to get the weak coverage area.And then calculation of the clustering center point,selecting the optimal solution method and editing the best data value make it feasible to get the clustering center value of the site coordinates of all the weak coverage area;Finally,it tested the complexity of the clustering method through the Davidson-Boding index(DBI).
作者
陈方芳
CHEN Fangfang(School of General Education,Liming Vocational University,Quanzhou,Fujian 362000,China)
出处
《山东商业职业技术学院学报》
2023年第6期110-114,共5页
Journal of Shandong Institute of Commerce and Technology
基金
黎明职业大学“数字人文研究中心”科研创新平台项目(LMPT202109)。
