Título:
A path forward : 6G resource allocation from a deep Q-learning perspective.
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Compilador:
Autor:
Inglés, Lucas
Rattaro, Claudina
Belzarena, Pablo
Rattaro, Claudina
Belzarena, Pablo
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
6G mobile communication
Q-learning
Codes
Network slicing
Resource management
Faces
Service level agreements
6G
Resource Allocation
Deep Q-Learning
Network Slicing
Q-learning
Codes
Network slicing
Resource management
Faces
Service level agreements
6G
Resource Allocation
Deep Q-Learning
Network Slicing
Descriptores:
Año de publicación:
2024
Contenido:
Resumen:
The 6G paradigm presents a myriad of challenges, as it promises complex features such as managing diverse traffic profiles under a unified infrastructure. While many studies propose deep-Q learning (DQN) approaches for resource management in Network Slicing (NS) schemes, these algorithms often face a core issue: they are not easily reproducible in real-world environments due to their high dimensionality. In this study, we analyze a distributed DQN-based radio resource allocation methodology, designed to efficiently meet specific Service Level Agreements (SLAs). Our contribution includes making the code publicly available for further research and evaluation. We then assess its performance through a comparison with a Baseline DQN approach, highlighting the strengths and limitations of both models.
Descripción:
metadata.articulos.dc.description.uri:
Editorial:
IEEE
EN:
2024 IEEE URUCON, Montevideo, Uruguay, 18-20 nov. 2024, pp. 1-5.
Financiadores:
CSIC R&D project : 5/6G Optical Network Convergence: an holistic view
Citación:
Inglés, L., Rattaro, C. y Belzarena, P. "A path forward : 6G resource allocation from a deep Q-learning perspective" [en línea]. EN: 2024 IEEE URUCON, Montevideo, Uruguay, 18-20 nov. 2024, pp. 1-5.
Citación:
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Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| IRB24.pdf | Camera-Ready | 1.15 MB | Adobe PDF |