Machine Learning and Operations Research for Intelligence Engines in Future Networks
Abstract
The evolution of fifth-generation (5G) and Beyond mobile technologies is spurred by the rapid demands and high-end requirements of next-generation mobile networks. It is imperative that advanced intelligence and machine learning techniques address these dynamic requirements by supporting network operations in terms of maintenance, servicing, and performance. The Third Generation Partnership Project (3GPP) has outlined a Network Data Analytics Function (NWDAF) for 5G Core (5GC) networks that should provide predictive network maintenance and improve network performance in these dynamic networks, and which must leverage the capabilities of artificial intelligence, machine learning, and advanced data analytics methods to satisfy its specification requirements. The work presented in this thesis surveys the current trends and future outlooks for 5G Core networks, in addition to presenting the capabilities of an implemented NWDAF, in emulated 5G environments, towards addressing a scaling optimization problem for Network Functions (NFs) in the 5G Control Plane. The insights from the NWDAF and its support in analytical and optimization problems justify its use as more than a network monitoring and data aggregation tool, but as an intelligence engine that will drive 5G and Beyond networks to satisfy user demand and improve consumer experience altogether.