CygNet MaSoN: Analytics and Machine Learning Enabled Management System for 5G Networks

Autonomous Networking is expected to be the mode of functioning by future networks, including 5G wireless networks. This requires intelligent data analytics and cognitive capabilities to be inherently supported as part of the networking functions, in addition to the current capabilities such as automation and correlation. This demonstration presents CygNet MaSoN, a management system that integrates multiple instances of radio access and core network functions of 5G networks supporting advanced aggregation and analytics features. This system continuously collects critical data related to network and system events, performance measurements and key performance indicators (KPIs) in real-time. It then uses the associated machine learning system to provide insights on network behaviour, estimation of service quality/experience and prediction of probable future network problems. Some of the analytics and machine learning use cases related to 5G networks and implemented on the MaSoN system are also described.

Automated summary

Autonomous Networking is expected to be the future mode of network operation, requiring intelligent data analytics and cognitive capabilities. CygNet MaSoN is a management system that integrates multiple 5G network functions, supporting advanced aggregation and analytics features. By continuously collecting critical data and utilizing machine learning, it provides insights into network behavior, service quality estimation, and prediction of future network issues.