On the deployment of AI-driven solutions in edge-enabled beyond 5G scenarios
The evolution beyond the Fifth Generation (5G) of mobile communications systems is expected to exploit different advanced radio access techniques, such as distributed multiple input multiple output (D-MIMO) or network slicing, together with an extensive support of Artificial Intelligence (AI) and Ma...
| Autores: | , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/450569 |
| Acceso en línea: | https://hdl.handle.net/2117/450569 https://dx.doi.org/10.1109/MCOMSTD.2025.3640682 |
| Access Level: | acceso abierto |
| Palabra clave: | Beyond 5G 6G Edge computing AI/ML-based optimization Distributed MIMO Edge-to-cloud compute continuum Network slicing Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | The evolution beyond the Fifth Generation (5G) of mobile communications systems is expected to exploit different advanced radio access techniques, such as distributed multiple input multiple output (D-MIMO) or network slicing, together with an extensive support of Artificial Intelligence (AI) and Machine Learning (ML) solutions for different aspects of network optimization. These systems will also benefit from the evolution of edge computing technologies through the availability of computing and storage capabilities that will span from the radio access network nodes to the cloud, forming an edge-to-cloud compute continuum. The availability of these features will provide the beyond 5G (B5G) and 6G networks with an increased flexibility for deploying the different AI/ML-based optimization functionalities, trading-off aspects such as delay requirements, computational complexity or availability of data for feeding the AI/ ML models. In this context, this paper takes as a reference the evolved edge computing architecture of the VERGE project and presents a pipeline with the required functionalities for the lifecycle management of AI/ML models in edge-enabled B5G systems. This pipeline is particularized for the deployment of two specific AI-driven solutions used for capacity sharing in network slicing and for D-MIMO power control. |
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