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The upcoming 6G systems will represent a complete paradigm shift for global communications, claiming a three-dimensional (3D) ecosystem with terrestrial and non-terrestrial networks (TNs-NTNs) to improve coverage and capacity and enable advanced applications with strict quality of service (QoS) and quality of experience (QoE) requirements. Addressing the critical research verticals toward the envisioned 2030 will require a seamless unicast/multicast/broadcast convergence and a native softwarized, disaggregated, and intelligent Radio Access Network (RAN) conception. In such a context, the network slicing paradigm is an appealing feature for enhanced differentiated traffic management. This research aims to efficiently manage radio access selection and slice allocation based on Machine Learning (ML) techniques inserted in the O-RAN framework. The project focuses on finding the best combination of access network and network slices to fulfill multiple users' requests and optimize resource usage over a 6G heterogeneous environment. Moreover, it addresses a load-balancing strategy to improve network performance and avoid overloading. The proposed algorithm is adapted to diverse network conditions, numerous service constraints, and several user types with different priorities and mobility behaviors. The proposal is evaluated through network-level simulations, focusing on effectively utilizing network resources and maximizing the QoS/QoE metrics.
González et al. (Mon,) studied this question.