Experimentation Scenarios for Machine Learning-Based Resource Management
Published in Unknown Venue, 2022
Recommended citation: Alexandros Kostopoulos and Ioannis P. Chochliouros and John Vardakas and Miquel Payaró and Sergio Barrachina and Md Arifur Rahman and Evgenii Vinogradov and Philippe Chanclou and Roberto Gonzalez and Charalambos Klitis and Sabrina De Capitani di Vimercati and Polyzois Soumplis and Emmanuel Varvarigos and Dimitrios Kritharidis and Kostas Chartsias (2022). Experimentation Scenarios for Machine Learning-Based Resource Management. Unknown Venue. https://link.springer.com/chapter/10.1007/978-3-031-08341-9_11
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.