Evaluation of a multi-cell and multi-tenant capacity sharing solution under heterogeneous traffic distributions
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10.1109/VTC2021-Spring51267.2021.9448889
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/366676
Tipus de documentComunicació de congrés
Data publicació2021
Condicions d'accésAccés obert
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Abstract
One of the key features of the 5G architecture is network slicing, which allows the simultaneous support of diverse service types with heterogeneous requirements over a common network infrastructure. In order to support this feature in the Radio Access Network (RAN), it is required to have capacity sharing mechanisms that distribute the available capacity in each cell among the existing RAN slices while satisfying their requirements and efficiently using the available resources. Deep Reinforcement Learning (DRL) techniques are good candidates to deal with the complexity of capacity sharing in multi-cell scenarios where the traffic in the different cells can be heterogeneously distributed in the time and space domains. In this paper, a multi-agent reinforcement learning-based solution for capacity sharing in multi-cell scenarios is discussed and assessed under heterogeneous traffic conditions. Results show the capability of the solution to satisfy the requirements of the RAN slices while using the resources in the different cells efficiently.
CitacióVila, I. [et al.]. Evaluation of a multi-cell and multi-tenant capacity sharing solution under heterogeneous traffic distributions. A: IEEE Vehicular Technology Conference. "Proceedings of the IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)". 2021, p. 1-5. DOI 10.1109/VTC2021-Spring51267.2021.9448889.
Versió de l'editorhttps://ieeexplore.ieee.org/document/9448889
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