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Analysis and optimization of 5G coverage predictions using a beamforming antenna model and real drive test measurements

2021-07-15Journal article Open access
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dc.contributor.authorSousa, Marco
dc.contributor.authorAlves, André
dc.contributor.authorVieira, Pedro
dc.contributor.authorQueluz, Maria Paula
dc.contributor.authorRodrigues, António
dc.date.accessioned2021-08-30T13:00:41Z
dc.date.available2021-08-30T13:00:41Z
dc.date.issued2021-07-15
dc.description.abstractThe ability to estimate radio coverage accurately is fundamental for planning and optimizing any wireless network, notably when a new generation, as the 5(th) Generation (5G), is in an early deployment phase. The knowledge acquired from radio planning of previous generations must be revisited, particularly the used path loss and antennas models, as the 5G propagation is intrinsically distinct. This paper analyses a new beamforming antenna model and distinct path loss models - 3(rd) Generation Partnership Project (3GPP) and Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications (mmMAGIC) - applying them to evaluate 5G coverage in 3-Dimensional (3D) synthetic and real scenarios, for outdoor and indoor environments. Further, real 5G Drive Tests (DTs) were used to evaluate the 3GPP path loss model accuracy in Urban Macro (UMa) scenarios. For the new antenna model, it is shown that the use of beamforming with multiple vertical beams is advantageous when the Base Station (BS) is placed below the surrounding buildings; in regular UMa surroundings, one vertical beam provides adequate indoor coverage and a maximized outdoor coverage after antenna tilt optimization. The 3GPP path loss model exhibited a Mean Absolute Error (MAE) of 21.05 dB for Line-of-Sight (LoS) and 14.48 dB for Non-Line-of-Sight (NLoS), compared with real measurements. After calibration, the MAE for LoS and NLoS decreased to 5.45 dB and 7.51 dB, respectively. Moreover, the non-calibrated 3GPP path loss model led to overestimations of the 5G coverage and user throughput up to 25% and 163%, respectively, when compared to the calibrated model predictions. The use of Machine Learning (ML) algorithms resulted in path loss MAEs within the range of 4.58 dB to 5.38 dB, for LoS, and within the range of 3.70 dB to 5.96 dB, for NLoS, with the Random Forest (RF) algorithm attaining the lowest error.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSOUSA, Marco; [et al] – Analysis and optimization of 5G coverage predictions using a beamforming antenna model and real drive test measurements. IEEE Access. eISSN 2169-3536. Vol. 9 (2021), pp. 101787-101808pt_PT
dc.identifier.doi10.1109/ACCESS.2021.3097633pt_PT
dc.identifier.eissn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.21/13663
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationProject AI4GREEN 16/SI/2019—I&DT Empresarial (Projetos Copromoção) - COMPETE/Fundo Europeu de Desenvolvimento Regional (FEDER)pt_PT
dc.relationInternational Project CELTIC-NEXT/EUREKA under Grant C2018/1-5pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9486925pt_PT
dc.subject5Gpt_PT
dc.subjectmmWavespt_PT
dc.subject3D propagationpt_PT
dc.subjectPath loss modelspt_PT
dc.subjectAntenna modelspt_PT
dc.subjectBeamformingpt_PT
dc.subjectCalibrationpt_PT
dc.subjectMachine learningpt_PT
dc.titleAnalysis and optimization of 5G coverage predictions using a beamforming antenna model and real drive test measurementspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage101808pt_PT
oaire.citation.startPage101787pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume9pt_PT
person.familyNameSousa
person.familyNameVieira
person.familyNameQueluz
person.givenNameMarco
person.givenNamePedro
person.givenNameMaria Paula
person.identifier.ciencia-idCB11-BB4E-3C79
person.identifier.ciencia-id071B-9A70-15B8
person.identifier.ciencia-idC210-DAC9-D03A
person.identifier.orcid0000-0002-2471-170X
person.identifier.orcid0000-0003-0279-8741
person.identifier.orcid0000-0003-0266-4022
person.identifier.scopus-author-id57202674941
person.identifier.scopus-author-id7004567421
person.identifier.scopus-author-id6602528040
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication22a6fbb3-76b6-4e5f-a8c9-207bdb2ada78
relation.isAuthorOfPublication51ae3527-d4ea-46f4-b6c9-62c7b77ac728
relation.isAuthorOfPublication59d5af5d-9c01-4147-a3a9-c1ea74ef3a4d
relation.isAuthorOfPublication.latestForDiscovery22a6fbb3-76b6-4e5f-a8c9-207bdb2ada78

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