Title:
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TOWARDS A MACHINE-LEARNING ARCHITECTURE FOR EFFICIENT RESOURCE MANAGEMENT |
Author(s):
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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:
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978-989-8704-53-5 |
Editors:
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Paula Miranda and Pedro IsaĆas |
Year:
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2023 |
Edition:
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Single |
Keywords:
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Cell-Free, Distributed Cloud, Network Automation, Machine Learning, Secure Multi-Tenancy |
Type:
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Full |
First Page:
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141 |
Last Page:
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148 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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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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