5G Network Management System With Machine Learning Based Analytics

Application of intelligent data analytics using machine learning in management of 5G networks can enable autonomous networking capabilities in 5G networks. This paper describes the design and implementation of CygNet MaSoN, a management system supporting advanced aggregation and analytics features combined with machine learning. The system supports detection of anomalous network behaviour, detection of degradation in network performance and service quality and also supports resource optimization. The main objective is to achieve self-organizing and closed loop automation functionalities expected as part of autonomous functioning of 5G networks. Details of the system architecture and components are presented. Three real-life use cases implemented on this system are then described. Machine learning models built and synthetic data generation methods adopted are presented with the features considered. The results obtained using the MaSoN system are also presented to demonstrate the effectiveness of the system in 5G network operations.

Automated summary

This paper describes the application of intelligent data analytics using machine learning in the management of 5G networks to achieve autonomous networking capabilities. It presents the design and implementation of CygNet MaSoN, a management system that supports advanced aggregation and analytics combined with machine learning. The system detects anomalous network behavior, degradation in network performance and service quality, and optimizes resources. The main objective is to achieve self-organizing and closed loop automation functionalities. The paper also presents three real-life use cases and the machine learning models and synthetic data generation methods used in the system, as well as the results obtained to demonstrate its effectiveness in 5G network operations.