Machine Learning and 5G Charging Function with Network Analytics Function for Network Slice as a Service

Abstract

5g system is more customer centric which give very high throughput, low latency, highly scalable, customer industry driven technology. 5G system has charging function which generate session and event based information from Session Management Function (SMF) and Access and mobility management function (AMF). The standard for advance use cases for 5G by incorporating network element/services like Network Data Analytics Function (NWDAF), Network Slice Management Function (NSMF) are defined in Third Generation Partnership Project (3GPP) release 15 and 16. These efforts are to make 5G use cases more customers driven, industry driven and make more analytical Key target of this project to incorporate machine learning techniques on the data available through NWDAF, NSMF and NAMF (Service-based Interface for Access and Mobility Management Function). This makes operator to launch potentially consumed charging use cases specifically to use Network Slice as a Service (N-SaaS). With the use of Machine Learning (ML) and Artificial Intelligence (AI) algorithms, data can be modelled to predict the customer charging model, predictive analysis to avoid future outages, optimal use of resources and traffic forecasting which gives operator potentially high revenue and of course customer satisfaction for better service.