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Title:      TOWARDS A MACHINE-LEARNING ARCHITECTURE FOR EFFICIENT RESOURCE MANAGEMENT
Author(s):      Alexandros Kostopoulos, Ioannis P. Chochliouros, John Vardakas, Christos Verikoukis, Arifur Rahman, Andrea P. Guevara, Robbert Beerten, Philippe Chanclou, Simon Pryor, Emmanouel Varvarigos and Polyzois Soumplis
ISBN:      978-989-8704-53-5
Editors:      Paula Miranda and Pedro IsaĆ­as
Year:      2023
Edition:      Single
Keywords:      Cell-Free, Distributed Cloud, Network Automation, Machine Learning, Secure Multi-Tenancy
Type:      Full
First Page:      141
Last Page:      148
Language:      English
Cover:      cover          
Full Contents:      click to dowload Download
Paper Abstract:     

5G mobile networks will soon be available to handle all types of applications and to provide services to massive numbers of users. In this complex and dynamic network ecosystem, an end-to-end performance analysis and optimisation will be "key" features to effectively manage the diverse requirements imposed by multiple vertical industries over the same shared infrastructure. To enable such a challenging vision, the MARSAL EU-funded project (MARSAL, 2021) 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 the network architecture of the MARSAL, as well as how the cell-free experimentation scenarios are mapped to the considered architecture.

   

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