Abstract
5G changes the landscape of mobile networks profoundly, with an evolved architecture supporting unprecedented capacity, spectral efficiency, and increased flexibility. The MARSAL project targets the development and evaluation of a complete framework for the management and orchestration of network resources in 5G and beyond by utilizing a converged optical-wireless network infrastructure in the access and fronthaul/midhaul segments. In this paper, we present a conceptual view of the MARSAL architecture, as well as a wide range of experimentation scenarios.
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Application available at RAN Controller for RAN Configuration, AI/ML model policy execution, Radio Resource Management (RRM) model, slice selection, etc., as per O-RAN compliance approved specifications specified by O-RAN WG-1 and WG-3.
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Further information about O-RAN components’ definitions can also be found at: https://docs.o-ran-sc.org/en/latest/architecture/architecture.html.
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Acknowledgments
The paper has been based on the context of the “MARSAL” (“Machine Learning-Based, Networking and Computing Infrastructure Resource Management of 5G and Beyond Intelligent Networks”) Project, funded by the EC under the Grant Agreement (GA) No. 101017171.
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Kostopoulos, A. et al. (2022). Experimentation Scenarios for Machine Learning-Based Resource Management. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-08341-9_11
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DOI: https://doi.org/10.1007/978-3-031-08341-9_11
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