5G RAN and Core Orchestration with ML-Driven QoS Profiling

5G has revolutionised mobile communication networks; however, it poses significant challenges due to the increased number of connected devices and the escalating data demands from applications. The Open Radio Access Network (O-RAN) architecture has emerged as a solution, characterised by open and standardised interfaces that foster interoperability among diverse vendors and enable the implementation of innovative solutions. In this context, the RAN Intelligent Controller (RIC) emerges as an intelligent control entity that empowers the efficient management and optimisation of the 5G RAN. Central to this orchestration are xApps, which can be developed and executed within the RIC. These applications possess the potential to drive innovation and substantially enhance the operation of 5G networks. As primary objective, this paper demonstrates the feasibility of employing a monitoring xApp within the Near-RT RIC to support the 5G core. This contributes to a better selection of user profiles, resulting in a better management of allocated resources to each user, and improved Quality of Service (QoS). By collecting and analysing real-time data, an Orchestrator enables proactive management and informed decision-making to optimise the Core performance, QoS, and resource utilisation. Specifically, Machine Learning (ML)-processed data is leveraged to select QoS profiles and assign them to individual users with the assistance of the Core Network (CN) agent. The results demonstrate the system's capability to efficiently collect and process real-time RAN data, to make user profile category predictions, and to allocate resources accordingly.