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Deep PC-MAC: a deep reinforcement learning pointer-critic media access protocol

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.abstractDeveloping artificial intelligence (AI) solutions for communication problems is one of the hottest topics nowadays. This article presents Deep PC-MAC, a novel deep reinforcement learning (DRL) solution to solve the fair coexistence problem (FCP) between heterogeneous nodes in the unlicensed bands. It is based on a hybrid architecture between pointer networks (Ptr-nets) and advantage actor-critic (A2C), i.e., pointer-critic architecture. The proposed model allows base stations to fairly share unlicensed bands with incumbent nodes. It jointly protects the incumbent nodes from spectrum starvation and improves key-performance indicators (KPIs). Deep PC-MAC is trained from scratch with zero-knowledge about FCP and experimental results demonstrate its efficiency when compared to a baseline method.
dc.description.sponsorshipPOCI-01-0145FEDER-030500
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.isbn978-1-7281-6339-0
dc.identifier.issn2378-4865
dc.identifier.urihttp://hdl.handle.net/10400.1/16611
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.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject5G
dc.subjectDeep reinforcement learning
dc.subjectMAC
dc.subjectUnlicensed bands
dc.subjectCSMA/CA
dc.subjectLTE-LAA
dc.subjectLTE-U
dc.subjectWiFi
dc.subjectCoexistence
dc.subject.otherComputer Science; Telecommunications
dc.titleDeep PC-MAC: a deep reinforcement learning pointer-critic media access protocol
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
person.identifierR-00G-A33
person.identifierR-00G-XFD
person.identifierR-000-DJV
person.identifier.ciencia-id2515-AFE3-525F
person.identifier.ciencia-idD91F-F08D-5380
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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