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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31002
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dc.contributor.authorCosmas, J-
dc.contributor.authorAli, K-
dc.contributor.authorMahbas, A-
dc.contributor.authorBoakye, PK-
dc.contributor.authorMiguel, J-
dc.contributor.authorGabillon, V-
dc.contributor.authorKazmierowski, A-
dc.contributor.authorSear, L-
dc.date.accessioned2025-03-31T17:12:39Z-
dc.date.available2025-03-31T17:12:39Z-
dc.date.issued2024-04-21-
dc.identifierORCiD: John Cosmas https://orcid.org/0000-0003-4378-5576-
dc.identifierORCiD: Ali Mahbas https://orcid.org/0000-0002-1134-9414-
dc.identifier.citationCosmas J. et al. (2024) 'Design of Scalable Population of Reinforcement Learning Agents for Autonomous 5G Radio Link Control', IEEE Wireless Communications and Networking Conference, WCNC, 2024, Dubai, United Arab Emirates, 21-24 April, pp. 1 - 6. doi: 10.1109/WCNC57260.2024.10570617.en_US
dc.identifier.isbn979-8-3503-0358-2 (ebk)-
dc.identifier.isbn979-8-3503-0359-9 (PoD)-
dc.identifier.issn1525-3511-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/31002-
dc.description.abstractThis research demonstrates how MATLAB's Reinforcement Learning Markov Decision Process (MDP) Example Model can be used to design Radio Link Control MDP Reinforcement Learning (RL) agent. Since the number of agents in MATLAB's RL toolbox is not scalable beyond one agent, then an agent scalability scheme is required to design RL agents in MATLAB's RL toolbox and then realize multiple lightweight simultaneously operable Python instances of it for each of the multiple user equipment UE in a network.en_US
dc.description.sponsorshipThe authors gratefully acknowledge support of EU Horizon 2020 Research Project 6G BRAINS (Bringing Reinforcement learning Into Radio Light Network for Massive Connections).en_US
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2024 Institute of Electrical and Electronics Engineers (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 ( https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ ).-
dc.rights.urihttps://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/-
dc.subjectMATLAB reinforcement learning tool boxen_US
dc.subject5G autonomous radio link control scalable population of reinforcement learning agentsen_US
dc.titleDesign of Scalable Population of Reinforcement Learning Agents for Autonomous 5G Radio Link Controlen_US
dc.typeConference Paperen_US
dc.identifier.doihttps://doi.org/10.1109/WCNC57260.2024.10570617-
dc.relation.isPartOfIEEE Wireless Communications and Networking Conference, WCNC-
pubs.publication-statusPublished-
dc.identifier.eissn1558-2612-
dcterms.dateAccepted2023-12-22-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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