APP-SON: Application characteristics-driven SON to optimize 4G/5G network performance and quality of experience

摘要

Self-Organizing Networks (SON) is an automation technology making the planning, deployment, operation, optimization, and healing of networks simpler and faster. Legacy SON is targeted at network automation and network optimization through certain optimization rules and policies which are globally applied in networks. However, scalable and targeted optimization is not considered yet in 3GPP. Furthermore, SON is driven by performance optimization rather than ultimately improving user Quality of Experience (QoE). The impact of application characteristics on network performance and further on QoE are also not considered in 3GPP SON. This paper presents an application characteristics-driven SON system (APP-SON) to optimize 4G/5G network performance and user Quality of Experience. APP-SON leverages a scalable big data platform for targeted optimization through profiling cell application characteristics in an incremental manner in temporal space. A Hungarian Algorithm Assisted Clustering (HAAC) algorithm and a deep learning-assisted regression algorithm are developed to profile the cell application characteristics and find the targeted KPIs to be optimized for each cell. A similarity-based, parametertuning algorithm is developed to tune the corresponding engineering parameters to optimize the targeted KPIs which further improve QoE. Experimental results demonstrate that the APP-SON system can precisely profile cell traffic and application characteristics to find the targeted KPIs for optimization for each cell. APP-SON can also automatically tune the corresponding engineering parameters to improve the corresponding KPIs, ultimately improving QoE. APP-SON has been successfully implemented in production and applied in a tier-1 operator's 4G network and as a universal SON solution it will be smoothly transitioned and applied in 5G networks for this operator.