Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework

[EN] The unprecedented surge of massive Internet of things (mIoT) traffic in beyond fifth generation (B5G) communication systems calls for transformative approaches for multiple access and data transmission. While classical model-based tools have been proven to be powerful and precise, an imminent t...

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Autores: Kumar, Abhishek, Li, Frank Y., Vidal Catalá, José Ramón|||0000-0002-7137-1349, Martínez Bauset, Jorge|||0000-0003-3342-3037
Formato: artículo
Fecha de publicación:2025
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/231509
Acesso em linha:https://riunet.upv.es/handle/10251/231509
Access Level:acceso abierto
Palavra-chave:Massive Internet of Things
Uplink small data packet
Semi-contention-free and contention-based
Reinforcement learning and policy gradient
Performance evaluation
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spelling Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning FrameworkKumar, AbhishekLi, Frank Y.Vidal Catalá, José Ramón|||0000-0002-7137-1349Martínez Bauset, Jorge|||0000-0003-3342-3037Massive Internet of ThingsUplink small data packetSemi-contention-free and contention-basedReinforcement learning and policy gradientPerformance evaluation[EN] The unprecedented surge of massive Internet of things (mIoT) traffic in beyond fifth generation (B5G) communication systems calls for transformative approaches for multiple access and data transmission. While classical model-based tools have been proven to be powerful and precise, an imminent trend for resource management in B5G networks is promoting solutions towards data-driven design. Considering an IoT network with devices spread in clusters covered by a base station, we present in this paper a novel model-free multiple access and data transmission framework empowered by reinforcement learning, designed for power-domain non-orthogonal multiple access networks to facilitate uplink traffic of small data packets. The framework supports two access modes referred to as contention-based and semi-contention-free, with its core component being a policy gradient algorithm executed at the base station. The base station performs access control and optimal radio resource allocation by periodically broadcasting two control parameters to each cluster of devices that considerably reduce data detection failures with a minimum computation requirement on devices. Numerical results, in terms of system and cluster throughput, throughput fairness, access delay, and energy consumption, demonstrate the efficiency and scalability of the framework as network size and traffic load vary.The research leading to these results has received funding from the European Economic Area (EEA) Norway (NO) Grants 2014-2021, under Project contract no. 42/2021, RO-NO-2019-0499 A Massive MIMO Enabled IoT Platform with Networking Slicing for Beyond 5G IoV/V2X and Maritime Services (SOLID-B5G). The work of Jose-Ramon Vidal and Jorge Martinez- ¿ Bauset was also supported by Grant PID2021-123168NB-I00, funded by MCIN/AEI, Spain/10.13039/501100011033 and the European Union A way of making Europe/ERDF.Institute of Electrical and Electronics EngineersEscuela Técnica Superior de Ingeniería de TelecomunicaciónDepartamento de ComunicacionesEEA GrantsGovernment of NorwayAGENCIA ESTATAL DE INVESTIGACIONEuropean Regional Development FundRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/231509reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-123168NB-I00 EVOLUCION DE LA RED DE ACCESO RADIO HACIA 6G PARA SERVICIOS MASIVOS Y DE BAJA LATENCIAGovernment of Norway Government of Norway 42%2F2021 NO Grants 2014 2021EEA Grants EEA Grants RO-NO2019-0499open accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2315092026-06-13T07:49:27Z
dc.title.none.fl_str_mv Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework
title Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework
spellingShingle Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework
Kumar, Abhishek
Massive Internet of Things
Uplink small data packet
Semi-contention-free and contention-based
Reinforcement learning and policy gradient
Performance evaluation
title_short Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework
title_full Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework
title_fullStr Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework
title_full_unstemmed Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework
title_sort Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework
dc.creator.none.fl_str_mv Kumar, Abhishek
Li, Frank Y.
Vidal Catalá, José Ramón|||0000-0002-7137-1349
Martínez Bauset, Jorge|||0000-0003-3342-3037
author Kumar, Abhishek
author_facet Kumar, Abhishek
Li, Frank Y.
Vidal Catalá, José Ramón|||0000-0002-7137-1349
Martínez Bauset, Jorge|||0000-0003-3342-3037
author_role author
author2 Li, Frank Y.
Vidal Catalá, José Ramón|||0000-0002-7137-1349
Martínez Bauset, Jorge|||0000-0003-3342-3037
author2_role author
author
author
dc.contributor.none.fl_str_mv Escuela Técnica Superior de Ingeniería de Telecomunicación
Departamento de Comunicaciones
EEA Grants
Government of Norway
AGENCIA ESTATAL DE INVESTIGACION
European Regional Development Fund
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Massive Internet of Things
Uplink small data packet
Semi-contention-free and contention-based
Reinforcement learning and policy gradient
Performance evaluation
topic Massive Internet of Things
Uplink small data packet
Semi-contention-free and contention-based
Reinforcement learning and policy gradient
Performance evaluation
description [EN] The unprecedented surge of massive Internet of things (mIoT) traffic in beyond fifth generation (B5G) communication systems calls for transformative approaches for multiple access and data transmission. While classical model-based tools have been proven to be powerful and precise, an imminent trend for resource management in B5G networks is promoting solutions towards data-driven design. Considering an IoT network with devices spread in clusters covered by a base station, we present in this paper a novel model-free multiple access and data transmission framework empowered by reinforcement learning, designed for power-domain non-orthogonal multiple access networks to facilitate uplink traffic of small data packets. The framework supports two access modes referred to as contention-based and semi-contention-free, with its core component being a policy gradient algorithm executed at the base station. The base station performs access control and optimal radio resource allocation by periodically broadcasting two control parameters to each cluster of devices that considerably reduce data detection failures with a minimum computation requirement on devices. Numerical results, in terms of system and cluster throughput, throughput fairness, access delay, and energy consumption, demonstrate the efficiency and scalability of the framework as network size and traffic load vary.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-12-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/231509
url https://riunet.upv.es/handle/10251/231509
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-123168NB-I00 EVOLUCION DE LA RED DE ACCESO RADIO HACIA 6G PARA SERVICIOS MASIVOS Y DE BAJA LATENCIA
Government of Norway Government of Norway 42%2F2021 NO Grants 2014 2021
EEA Grants EEA Grants RO-NO2019-0499
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
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
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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