Towards Machine-Learning-Based 5G and Beyond Intelligent Networks: The MARSAL Project Vision Article Swipe
John S. Vardakas
,
Kostas Ramantas
,
Eftychia Datsika
,
Miquel Payaró
,
Sofie Pollin
,
Evgenii Vinogradov
,
Emmanouel Varvarigos
,
Panagiotis Kokkinos
,
Roberto Gonzalez-Sanchez
,
Juan José Vegas Olmos
,
Ioannis P. Chochliouros
,
Philippe Chanclou
,
Pierangela Samarati
,
Adam Flizikowski
,
Md Arifur Rahman
,
Christos Verikoukis
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/meditcom49071.2021.9647671
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/meditcom49071.2021.9647671
5G mobile networks will be soon available to handle all types of applications and to provide service to massive numbers of users. In this complex and dynamic network ecosystem, end-to-end performance analysis and optimization will be key features to effectively manage the diverse requirements imposed by multiple vertical industries over the same shared infrastructure. To enable such a vision, 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. At the network design domain, MARSAL targets the development of novel cell-free based solutions by exploiting the application of the distributed cell-free concept and of the serial fronthaul approach, while contributing innovative functionalities to the O-RAN project. In parallel, in the fronthaul/midhaul segments MARSAL aims to radically increase the flexibility of optical access architectures via different levels of fixed-mobile convergence. At the network and service management domain, the design philosophy of MARSAL is to exploit novel ML-based algorithms of both edge and midhaul DCs, by incorporating the Virtual Elastic DataCenters/Infrastructures paradigm. Finally, at the network security domain, MARSAL aims to introduce mechanisms that provide privacy and security to application workload and data, targeting to allow applications and users to maintain control over their data, while AI and and Blockchain technologies will be developed in order to guarantee a secured multi-tenant slicing environment. © 2021 IEEE.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/meditcom49071.2021.9647671
- OA Status
- green
- Cited By
- 16
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200160787Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/meditcom49071.2021.9647671Digital Object Identifier
- Title
-
Towards Machine-Learning-Based 5G and Beyond Intelligent Networks: The MARSAL Project VisionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-07Full publication date if available
- Authors
-
John S. Vardakas, Kostas Ramantas, Eftychia Datsika, Miquel Payaró, Sofie Pollin, Evgenii Vinogradov, Emmanouel Varvarigos, Panagiotis Kokkinos, Roberto Gonzalez-Sanchez, Juan José Vegas Olmos, Ioannis P. Chochliouros, Philippe Chanclou, Pierangela Samarati, Adam Flizikowski, Md Arifur Rahman, Christos VerikoukisList of authors in order
- Landing page
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https://doi.org/10.1109/meditcom49071.2021.9647671Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://zenodo.org/record/7244228Direct OA link when available
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Computer science, Orchestration, Distributed computing, Computer network, Radio access network, Cellular network, Mobile edge computing, Server, Base station, Visual arts, Mobile station, Art, MusicalTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2025: 4, 2024: 4, 2023: 4, 2022: 4Per-year citation counts (last 5 years)
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8Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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