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

© 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 s...

Descripción completa

Detalles Bibliográficos
Autores: Vilà Muñoz, Irene|||0000-0002-7086-9591, Pérez Romero, Jordi|||0000-0001-9131-5013, Sallent Roig, Oriol|||0000-0002-2114-1406, Umbert Juliana, Anna|||0000-0001-7825-5212
Tipo de recurso: artículo
Fecha de publicación:2021
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:2117/355785
Acceso en línea:https://hdl.handle.net/2117/355785
https://dx.doi.org/10.1109/TVT.2021.3099557
Access Level:acceso abierto
Palabra clave:Mobile communication systems
RAN Slicing
Capacity Sharing
Multi-Agent Reinforcement Learning
Deep Q-Network.
Comunicacions mòbils, Sistemes de
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
id ES_61cec17c67e1be32bc2a7ef0d03c101a
oai_identifier_str oai:upcommons.upc.edu:2117/355785
network_acronym_str ES
network_name_str España
repository_id_str
spelling A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenariosVilà Muñoz, Irene|||0000-0002-7086-9591Pérez Romero, Jordi|||0000-0001-9131-5013Sallent Roig, Oriol|||0000-0002-2114-1406Umbert Juliana, Anna|||0000-0001-7825-5212Mobile communication systemsRAN SlicingCapacity SharingMulti-Agent Reinforcement LearningDeep Q-Network.Comunicacions mòbils, Sistemes deÀrees temàtiques de la UPC::Enginyeria de la telecomunicació© 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.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.This work was supported in part by the Spanish Research Council and FEDER funds under SONAR 5G Grant ref. TEC2017-82651-R, in part by the European Commission’s Horizon 2020 5G-CLARITY project under Grant 871428, and in part by the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia under Grant 2020FI_B2 00075.Peer ReviewedInstitute of Electrical and Electronics Engineers (IEEE)20212021-07-2720212021-11-08journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/355785https://dx.doi.org/10.1109/TVT.2021.3099557reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 871428 Beyond 5G multi-tenant private networks integrating Cellular, WiFi, and LiFi, Powered by ARtificial Intelligence and Intent Based PolicYAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TEC2017-82651-R SOFTWARIZACION Y OPTIMIZACION AUTOMATICA DE REDES DE ACCESO RADIO 5G MULTI-TENANTopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3557852026-05-27T15:37:01Z
dc.title.none.fl_str_mv A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios
title A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios
spellingShingle A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios
Vilà Muñoz, Irene|||0000-0002-7086-9591
Mobile communication systems
RAN Slicing
Capacity Sharing
Multi-Agent Reinforcement Learning
Deep Q-Network.
Comunicacions mòbils, Sistemes de
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
title_short A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios
title_full A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios
title_fullStr A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios
title_full_unstemmed A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios
title_sort A multi-agent reinforcement learning approach for capacity sharing in multi-tenant scenarios
dc.creator.none.fl_str_mv Vilà Muñoz, Irene|||0000-0002-7086-9591
Pérez Romero, Jordi|||0000-0001-9131-5013
Sallent Roig, Oriol|||0000-0002-2114-1406
Umbert Juliana, Anna|||0000-0001-7825-5212
author Vilà Muñoz, Irene|||0000-0002-7086-9591
author_facet Vilà Muñoz, Irene|||0000-0002-7086-9591
Pérez Romero, Jordi|||0000-0001-9131-5013
Sallent Roig, Oriol|||0000-0002-2114-1406
Umbert Juliana, Anna|||0000-0001-7825-5212
author_role author
author2 Pérez Romero, Jordi|||0000-0001-9131-5013
Sallent Roig, Oriol|||0000-0002-2114-1406
Umbert Juliana, Anna|||0000-0001-7825-5212
author2_role author
author
author
dc.subject.none.fl_str_mv Mobile communication systems
RAN Slicing
Capacity Sharing
Multi-Agent Reinforcement Learning
Deep Q-Network.
Comunicacions mòbils, Sistemes de
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
topic Mobile communication systems
RAN Slicing
Capacity Sharing
Multi-Agent Reinforcement Learning
Deep Q-Network.
Comunicacions mòbils, Sistemes de
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-07-27
2021
2021-11-08
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/355785
https://dx.doi.org/10.1109/TVT.2021.3099557
url https://hdl.handle.net/2117/355785
https://dx.doi.org/10.1109/TVT.2021.3099557
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 871428 Beyond 5G multi-tenant private networks integrating Cellular, WiFi, and LiFi, Powered by ARtificial Intelligence and Intent Based PolicY
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TEC2017-82651-R SOFTWARIZACION Y OPTIMIZACION AUTOMATICA DE REDES DE ACCESO RADIO 5G MULTI-TENANT
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869409446124322816
score 15.300719

Similares en LA Referencia