TOWARDS A MACHINE-LEARNING ARCHITECTURE FOR EFFICIENT RESOURCE MANAGEMENT.

Autor: Kostopoulos, Alexandros, Chochliouros, Ioannis P., Vardakas, John, Verikoukis, Christos, Rahman, Arifur, Guevara, Andrea P., Beerten, Robbert, Chanclou, Philippe, Pryor, Simon, Varvarigos, Emmanouel, Soumplis, Polyzois
Předmět:
Zdroj: Proceedings of the IADIS International Conference on WWW/Internet; 2023, p141-148, 8p
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Databáze: Supplemental Index