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dc.contributor.authorIssaid, Chaouki Ben
dc.contributor.authorAntón-Haro, Carles
dc.contributor.authorMestre, Xavier
dc.contributor.authorAlouini, Mohamed-Slim
dc.date.accessioned2020-11-18T05:31:17Z
dc.date.available2020-11-18T05:31:17Z
dc.date.issued2020
dc.identifier.citationIssaid, C. B., Anton-Haro, C., Mestre, X., & Alouini, M.-S. (2020). User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees. IEEE Access, 1–1. doi:10.1109/access.2020.3038490
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/ACCESS.2020.3038490
dc.identifier.urihttp://hdl.handle.net/10754/666011
dc.description.abstractIn this paper, we propose a data-driven approach to group users in a Non-Orthogonal Multiple Access (NOMA) MIMO setting. Specifically, we formulate user clustering as a multi-label classification problem and solve it by coupling a Classifier Chain (CC) with a Gradient Boosting Decision Tree (GBDT), namely, the LightGBM algorithm. The performance of the proposed CC-LightGBM scheme is assessed via numerical simulations. For benchmarking, we consider two classical adaptation learning schemes: Multi-Label k-Nearest Neighbours (ML-KNN) and Multi-Label Twin Support Vector Machines (ML-TSVM); as well as other naive approaches. Besides, we also compare the computational complexity of the proposed scheme with those of the aforementioned benchmarks.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9261336/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9261336
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectNOMA
dc.subjectmulti-label classification
dc.subjectclassifier chains
dc.subjectgradient-boosting decision trees
dc.subjectuser clustering
dc.titleUser Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees
dc.typeArticle
dc.contributor.departmentCommunication Theory Lab
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Access
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCentre for Wireless Communications (CWC), University of Oulu, 90570 Oulu, Finland.
dc.contributor.institutionCentre Tecnològic de Telecomunicacions de Catalunya (CTTC/iCERCA), Parc Mediterrani Tecnologia (PMT), Av Carl Friedrich Gauss 7, Bldg. B6, 08860 Castelldefels, Spain.
kaust.personAlouini, Mohamed-Slim
refterms.dateFOA2020-11-18T05:32:58Z


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