摘要
随着5G网络的大量建设和快速发展,市场5G终端的大力推广及普及使用,5G网络负荷压力问题日益突出,特别是高校、商业区、居民区等热点场景,存在高负荷问题处理滞后、分析效率低等问题。文章提出了一种5G高负荷问题智能定位预测方法,通过利用KPI的全局性、周期性和实时性的特点,构建负荷训练模型,对关键指标进行监控分析,快速识别业务变化情况,预测定位未来某个时刻或某个区域的负荷情况。在负荷问题发生前,精准定位问题小区,提前预测分析处理,将问题处理前置化,提前化解业务增长带来的网络风险,提升用户感知。
With the rapid development and extensive deployment of 5G networks,the widespread adoption of 5G terminals in the market has led to an increasing burden on 5G networks.This burden is particularly prominent in high-density areas such as universities,commercial zones,and residential areas,where issues like delayed handling of high load problems and low analysis efficiency exist.This paper proposes an intelligent positioning and prediction method for addressing 5G high load issues.By leveraging the global,periodic,and real-time characteristics of Key Performance Indicators(KPIs),a load training model is constructed to monitor and analyze critical indicators.This allows for the rapid identification of changes in business scenarios and the prediction of load conditions for specific time frames or areas in the future.By precisely locating problematic cells before load issues occur,performing early prediction analysis,and intervening proactively,the approach preempts the network risks caused by business growth,thereby enhancing user perception.
作者
华滢
HUA Ying(China Mobile Communications Group Hubei Co.LTD,Hubei Wuhan,430023)
出处
《长江信息通信》
2024年第1期206-208,212,共4页
Changjiang Information & Communications
关键词
5G高负荷
智能定位预测
业务场景识别
感知压抑拐点
5G high load
intelligent positioning prediction
business scenario recognition
Perception depressive inflection point.
