Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL

The escalating demands of next-generation, beyond5G services necessitate innovative approaches to dynamic resource management, critical for satisfying end-user expectations and maintaining quality of service (QoS). This study leverages the Open Radio Access Network (O-RAN) architecture’s flexibility...

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Detalles Bibliográficos
Autores: Mhatre, Suvidha Sudhakar, Adelantado Freixer, Ferran, Ramantas, Kostas, Verikoukis, Christos
Tipo de recurso: artículo
Fecha de publicación:2024
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:2117/417773
Acceso en línea:https://hdl.handle.net/2117/417773
https://dx.doi.org/10.1109/TVT.2024.3483288
Access Level:acceso abierto
Palabra clave:Slicing
DRL
QoS
Resource allocation and management
URLLC
eMBB
KPI
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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repository_id_str
spelling Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRLMhatre, Suvidha SudhakarAdelantado Freixer, FerranRamantas, KostasVerikoukis, ChristosSlicingDRLQoSResource allocation and managementURLLCeMBBKPIÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadorsÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialThe escalating demands of next-generation, beyond5G services necessitate innovative approaches to dynamic resource management, critical for satisfying end-user expectations and maintaining quality of service (QoS). This study leverages the Open Radio Access Network (O-RAN) architecture’s flexibility and programmability to introduce a novel deep reinforcement learning (DRL) strategy for QoS-aware intraslice resource allocation. By employing intelligent agents and a Deep Q-Network (DQN)-based framework, our approach precisely tailors resource distribution within O-RAN, optimizing for enhanced mobile broadband (eMBB) and ultra-reliable lowlatency communications (URLLC) slices. The proposed method, featuring intelligent QoS-aware resource allocation (IQRA) and its low-complexity variant (LIQRA), demonstrates significant throughput improvements for eMBB by 11.5% compared to state-of-the-art (SOTA) methods and reduces URLLC latency by 19.94% and 16.54%, achieving up to 45.5% lower latency than baseline. A streamlined algorithm effectively reduces computational complexity, ensuring robust performance under resource constraints. Simulation results underscore the algorithm’s ability to substantially enhance 5G network slice performance, offering a parameterized solution for user association in O-RAN networks using DRL. This research not only meets high key performance indicators (KPIs) but also advances edge intelligence, fostering a more responsive network ecosystem.The above work is carried out as part of the MSCA SEMANTIC project with ITN under grant 861165, ADROIT6G (101095363), 6G-BRICKS (101096954) SNS JU projects, RFVOLUTION (PID2021-122247OB-I00) under Spanish Ministry of Science, Innovation and Universities, Generalitat de Catalunya under Grant 2021 SGR 174.Peer ReviewedInstitute of Electrical and Electronics Engineers (IEEE)20252025-02-0120242024-11-14journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/417773https://dx.doi.org/10.1109/TVT.2024.3483288reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4177732026-05-27T15:37:01Z
dc.title.none.fl_str_mv Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL
title Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL
spellingShingle Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL
Mhatre, Suvidha Sudhakar
Slicing
DRL
QoS
Resource allocation and management
URLLC
eMBB
KPI
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL
title_full Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL
title_fullStr Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL
title_full_unstemmed Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL
title_sort Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL
dc.creator.none.fl_str_mv Mhatre, Suvidha Sudhakar
Adelantado Freixer, Ferran
Ramantas, Kostas
Verikoukis, Christos
author Mhatre, Suvidha Sudhakar
author_facet Mhatre, Suvidha Sudhakar
Adelantado Freixer, Ferran
Ramantas, Kostas
Verikoukis, Christos
author_role author
author2 Adelantado Freixer, Ferran
Ramantas, Kostas
Verikoukis, Christos
author2_role author
author
author
dc.subject.none.fl_str_mv Slicing
DRL
QoS
Resource allocation and management
URLLC
eMBB
KPI
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Slicing
DRL
QoS
Resource allocation and management
URLLC
eMBB
KPI
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description The escalating demands of next-generation, beyond5G services necessitate innovative approaches to dynamic resource management, critical for satisfying end-user expectations and maintaining quality of service (QoS). This study leverages the Open Radio Access Network (O-RAN) architecture’s flexibility and programmability to introduce a novel deep reinforcement learning (DRL) strategy for QoS-aware intraslice resource allocation. By employing intelligent agents and a Deep Q-Network (DQN)-based framework, our approach precisely tailors resource distribution within O-RAN, optimizing for enhanced mobile broadband (eMBB) and ultra-reliable lowlatency communications (URLLC) slices. The proposed method, featuring intelligent QoS-aware resource allocation (IQRA) and its low-complexity variant (LIQRA), demonstrates significant throughput improvements for eMBB by 11.5% compared to state-of-the-art (SOTA) methods and reduces URLLC latency by 19.94% and 16.54%, achieving up to 45.5% lower latency than baseline. A streamlined algorithm effectively reduces computational complexity, ensuring robust performance under resource constraints. Simulation results underscore the algorithm’s ability to substantially enhance 5G network slice performance, offering a parameterized solution for user association in O-RAN networks using DRL. This research not only meets high key performance indicators (KPIs) but also advances edge intelligence, fostering a more responsive network ecosystem.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-11-14
2025
2025-02-01
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://hdl.handle.net/2117/417773
https://dx.doi.org/10.1109/TVT.2024.3483288
url https://hdl.handle.net/2117/417773
https://dx.doi.org/10.1109/TVT.2024.3483288
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
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
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
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
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score 15.81155

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