Context: The growing complexity of mobile networks presents challenges in optimising network performance. While 6G promises potential performance improvement, managing 6G to ensure optimal performance in all operating environments is challenging.

Challenges: The 6G-SMART project deals with the challenges of self-organisation in 6G networks based on the Open Radio Access Network (O-RAN) architecture, Machine Learning (ML), and Artificial Intelligence (AI) techniques. However, multiple aspects of the network performance are often required to be jointly optimised, and applying various ML models without proper management may result in conflicting decision-making by each ML model, causing the network to operate in an undesirable state. 

Objectives: Develop several individual ML algorithms to fulfil self-configuration, self-optimisation, and self-healing. The ML models will be integrated before developing an ML orchestrator to manage and resolve conflicting decisions made by various ML models. Testing will be conducted in two countries, focusing on telecommunications and smart factory environments