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Radio Resource Scheduling with Deep Pointer Networks and Reinforcement Learning

2020Conference object Restricted access
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dc.contributor.authorAl-Tam, Faroq
dc.contributor.authorMazayev, Andriy
dc.contributor.authorCorreia, Noélia
dc.contributor.authorRodriguez, J.
dc.date.accessioned2021-06-24T11:36:03Z
dc.date.available2021-06-24T11:36:03Z
dc.date.issued2020
dc.description.abstractThis article presents an artificial intelligence (AI) adaptable solution to handle the radio resource scheduling (RRS) task in 5G networks. RRS is one of the core tasks in radio resource management (RRM) and aims to efficiently allocate frequency domain resources to users. The proposed solution is an advantage pointer critic (APC) deep reinforcement learning (DRL) agent. It is built with a deep pointer network architecture and trained by the policy gradient algorithm. The proposed agent is deployed in a system level simulator and the experimental results demonstrate its adaptability to network dynamics and efficiency when compared to baseline algorithms.
dc.description.sponsorshipPOCI-01-0145-FEDER-030500
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1109/CAMAD50429.2020.9209313
dc.identifier.isbn978-1-7281-6339-0
dc.identifier.issn2378-4865
dc.identifier.urihttp://hdl.handle.net/10400.1/16612
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relation.ispartofseriesIEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks
dc.subject5G
dc.subjectDeep reinforcement learning
dc.subjectRadio resource management
dc.subjectRadio resource scheduling
dc.subjectPointer network
dc.subjectActor-critic
dc.subject.otherComputer Science; Telecommunications
dc.titleRadio Resource Scheduling with Deep Pointer Networks and Reinforcement Learning
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00631%2F2020/PT
oaire.citation.conferencePlacePisa, Italy
oaire.citation.title2020 Ieee 25Th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (Camad)
oaire.citation.title25Th Ieee International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (Ieee Camad)
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAl-Tam
person.familyNameMazayev
person.familyNameCorreia
person.givenNameFaroq
person.givenNameAndriy
person.givenNameNoélia
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person.identifierR-00G-XFD
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person.identifier.ciencia-id2515-AFE3-525F
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person.identifier.ciencia-idDD19-1F35-B804
person.identifier.orcid0000-0001-9718-2039
person.identifier.orcid0000-0003-0495-9801
person.identifier.orcid0000-0001-7051-7193
person.identifier.ridK-7031-2016
person.identifier.ridM-3554-2013
person.identifier.scopus-author-id55246034700
person.identifier.scopus-author-id56565451000
person.identifier.scopus-author-id8411596100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccess
rcaap.typeconferenceObject
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