基于聚类分析和AI深度学习的超忙小区智能优化

Intelligent optimization of super busy cell based on cluster analysis and AI deep learning

利用Python语言,结合大数据分析、AI深度学习算法、实现高负荷小区智能优化输出。通过导入LTE扇区工参和负荷指标,用户数、流量TA数据、MR数据等指标,挖掘高负荷小区主要影响因素、利用AI深度学习算法智能生成优化参数调整建议。该方法能够开展全网一键核查和优化,具备大数据运算和数据可视化能力,能快速降低高负荷小区负载,提高网络资源利用率,实现超忙小区压降智能优化,提升智慧运营能力。在某本地网第一期超忙小区优化试点工作中,指导网络优化解决59.4%的超忙问题,改善用户感知。

Using Python language, combined with big data analysis, AI deep learning algorithm, output high load cell intelligent optimization solution. By importing LTE network configuration via Configuration Management(CM) and operation supervision via Performance Management(PM), it can dig into the main influencing factors of high-load cells and generate optimization parameter adjustment suggestions. The method can carry out one-click checking and optimization, with data visualization capabilities, which can quickly reduce the load of high-load cells, improve network resource utilization, realize intelligent optimization of pressure drop in over-busy cells, and enhance intelligent operation capability. In a local network over-busy cell optimization work, it guides network optimization to solve 59.4% of over-busy problems and improve user perception.