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Unsupervised clustering for 5G network planning assisted by real data

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Comunicacioneses
dc.contributor.authorKhan, M. Umar
dc.contributor.authorAzizi, Mostafa
dc.contributor.authorGarcía-Armada, Ana
dc.contributor.authorEscudero-Garzás, J. J.
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2023-05-23T10:32:34Z
dc.date.available2023-05-23T10:32:34Z
dc.date.issued2022
dc.description.abstractThe fifth-generation (5G) of networks is being deployed to provide a wide range of new services and to manage the accelerated traffic load of the existing networks. In the present-day networks, data has become more noteworthy than ever to infer about the traffic load and existing network infrastructure to minimize the cost of new 5G deployments. Identifying the region of highest traffic density in megabyte (MB) per km2 has an important implication in minimizing the cost per bit for the mobile network operators (MNOs). In this study, we propose a base station (BS) clustering framework based on unsupervised learning to identify the target area known as the highest traffic cluster (HTC) for 5G deployments. We propose a novel approach assisted by real data to determine the appropriate number of clusters k and to identify the HTC. The algorithm, named as NetClustering, determines the HTC and appropriate value of k by fulfilling MNO's requirements on the highest traffic density MB/km2 and the target deployment area in km2. To compare the appropriate value of k and other performance parameters, we use the Elbow heuristic as a benchmark. The simulation results show that the proposed algorithm fulfills the MNO's requirements on the target deployment area in km2 and highest traffic density MB/km2 with significant cost savings and achieves higher network utilization compared to the Elbow heuristic. In brief, the proposed algorithm provides a more meaningful interpretation of the underlying data in the context of clustering performed for network planningen
dc.description.sponsorshipThis work was supported by the Spanish National Project IRENE-EARTH (PID2020-115323RB-C33/AEI/10.13039/501100011033)en
dc.format.extent13
dc.identifier.bibliographicCitationKhan, M. I., Azizi, M., Armada, A. G., & Escudero-Garzas, J. J. (2022). Unsupervised Clustering for 5G Network Planning Assisted by Real Data. IEEE Access, 10, 39269-39281.en
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2022.3165799
dc.identifier.issn2169-3536
dc.identifier.publicationfirstpage39269
dc.identifier.publicationlastpage39281
dc.identifier.publicationtitleIEEE Accessen
dc.identifier.publicationvolume10
dc.identifier.urihttps://hdl.handle.net/10016/37340
dc.identifier.uxxiAR/0000032801
dc.language.isoeng
dc.publisherIEEE
dc.relation.projectIDGobierno de España. PID2020-115323RB-C33es
dc.rights© 2022, the author(s)en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.other5Gen
dc.subject.otherNetwork planningen
dc.subject.otherMachine learningen
dc.subject.otherNetwork clusteringen
dc.subject.otherNetwork data acquisitionen
dc.subject.otherCluster analysisen
dc.subject.otherElbow methoden
dc.titleUnsupervised clustering for 5G network planning assisted by real dataen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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