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A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios

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10.1109/TVT.2021.3099557
 
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hdl:2117/355785

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Vilà Muñoz, IreneMés informacióMés informació
Pérez Romero, JordiMés informacióMés informacióMés informació
Sallent Roig, OriolMés informacióMés informacióMés informació
Umbert Juliana, AnnaMés informacióMés informacióMés informació
Document typeArticle
Defense date2021-07-27
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Project5G-CLARITY - Beyond 5G multi-tenant private networks integrating Cellular, WiFi, and LiFi, Powered by ARtificial Intelligence and Intent Based PolicY (EC-H2020-871428)
SOFTWARIZACION Y OPTIMIZACION AUTOMATICA DE REDES DE ACCESO RADIO 5G MULTI-TENANT (AEI-TEC2017-82651-R)
Abstract
5G is envisioned to simultaneously provide diverse service types with heterogeneous needs under very different application scenarios and business models. Therefore, network slicing is included as a key feature of the 5G architecture to allow sharing a common infrastructure among different tenants, such as mobile communication providers, vertical market players, etc. In order to provide the Radio Access Network (RAN) with network slicing capabilities, mechanisms that efficiently distribute the available capacity among the different tenants while satisfying their needs are required. For this purpose, this paper proposes a multi-agent reinforcement learning approach for RAN capacity sharing. It makes use of the Deep Q-Network algorithm in a way that each agent is associated to a different tenant and learns the capacity to be provided to this tenant in each cell while ensuring that the service level agreements are satisfied and that the available radio resources are efficiently used. The consideration of multiple agents contributes to a better scalability and higher learning speed in comparison to single-agent approaches. In this respect, results show that the policy learnt by the agent of one tenant can be generalised and directly applied by other agents, thus reducing the complexity of the training and making the proposed solution easily scalable, e.g., to add new tenants in the system. The proposed approach is well aligned with the on-going 3GPP standardization work and guidelines for the parametrization of the solution are provided, thus enforcing its practical applicability.
Description
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
CitationVila, I. [et al.]. A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios. "IEEE transactions on vehicular technology", 27 Juliol 2021, vol. 70, núm. 9, p. 9450-9465. 
URIhttp://hdl.handle.net/2117/355785
DOI10.1109/TVT.2021.3099557
ISSN0018-9545
Publisher versionhttps://ieeexplore.ieee.org/document/9497684
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  • Departament de Teoria del Senyal i Comunicacions - Articles de revista [2.468]
  • GRCM - Radio Communication Research Group - Articles de revista [144]
  • Doctorat en Teoria del Senyal i Comunicacions - Articles de revista [188]
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