FREE6G_RegEdge - Machine learning-based cell-free networks for 6G – Regional Edge

FREE6G-RegEdge aims at providing an integrated orchestration and management of communication, storage and computational resources. To this end, FREE6G-RegEdge aims to optimize the functionality of the MEC system and the Network Slicing Management via a hierarchy of analytic and decision engines, proposing novel ML-based approaches for distributed representation based on multi-objective optimization. Moreover, FREE6G-RegEdge envisages a new approach based on the Elastic Edge Computing notion of an evolved MEC system which allows application functions to by disaggregated and dynamically migrated across the edge and also from the RAN to the core cloud and vice-versa. This implies the need to support advanced intra-DC and inter-DC load balancing scenarios addressing optimized distribution of latency budgets. The ultimate goal is to achieve a zero perceived latency primarily in MEC applications requiring smart connectivity. Finally, dynamic slicing approaches will also be explored, to trigger predictive slice reconfiguration across domains.