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dc.contributor.authorChang, Zheng
dc.contributor.authorChen, Tao
dc.date.accessioned2023-02-20T10:35:08Z
dc.date.available2023-02-20T10:35:08Z
dc.date.issued2022
dc.identifier.citationChang, Z., & Chen, T. (2022). Virtual Resource Allocation for Wireless Virtualized Heterogeneous Network with Hybrid Energy Supply. <i>IEEE Transactions on Wireless Communications</i>, <i>21</i>(3), 1886-1896. <a href="https://doi.org/10.1109/twc.2021.3107867" target="_blank">https://doi.org/10.1109/twc.2021.3107867</a>
dc.identifier.otherCONVID_100373488
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/85532
dc.description.abstractIn this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseriesIEEE Transactions on Wireless Communications
dc.rightsIn Copyright
dc.subject.otherenergy harvesting
dc.subject.otherADMM
dc.subject.otherreinforcement learning
dc.subject.otherdeep learning
dc.subject.otherwireless network virtualization
dc.subject.otherresource allocation
dc.titleVirtual Resource Allocation for Wireless Virtualized Heterogeneous Network with Hybrid Energy Supply
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202302201791
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1886-1896
dc.relation.issn1536-1276
dc.relation.numberinseries3
dc.relation.volume21
dc.type.versionacceptedVersion
dc.rights.copyright© 2022, IEEE
dc.rights.accesslevelopenAccessfi
dc.subject.ysoresursointi
dc.subject.ysovirtualisointi
dc.subject.ysoenergian kerääminen
dc.subject.ysolangattomat verkot
dc.subject.ysokoneoppiminen
dc.subject.ysosyväoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p24562
jyx.subject.urihttp://www.yso.fi/onto/yso/p22009
jyx.subject.urihttp://www.yso.fi/onto/yso/p38234
jyx.subject.urihttp://www.yso.fi/onto/yso/p24221
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p39324
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/twc.2021.3107867
jyx.fundinginformationThis work was supported in part by the NSFC under Grant 62071105.
dc.type.okmA1


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