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dc.contributor.authorAmalorpava Mary Rajee, S.
dc.contributor.authorMerline, A.
dc.date.accessioned2020-10-14T07:07:55Z
dc.date.available2020-10-14T07:07:55Z
dc.date.issued2020-09cs
dc.identifier.citationRadioengineering. 2020 vol. 29, č. 3, s. 555-562. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/195217
dc.description.abstractMillimeter wave (mmWave) links such as 28 GHz and 60 GHz propose high data rates and capacity needed in 5G Heterogeneous network (Hetnet) real-time system. The key factors in network planning of Hetnet are the locations and links of base stations, and their coverage, transmitted power, antenna angle, orientation etc. How-ever, large-scale blockages like static buildings, human etc. affect the performance of urban Hetnets especially at mmWave frequencies. A mathematical framework to model dynamic blockages is adapted and their impact on cellular network performance is analyzed. A machine learning approach based on Q-learning with Epsilon-Greedy algo¬rithm is proposed to solve the blockage problem in such complex networks. The proposed results are evident and show the positive effect of increasing the base station den¬sity linearly with the blockage density to maintain the net¬work connectivity. The performance of the proposed Epsi¬lon-Greedy algorithm is compared with Epsilon-Soft algo-rithm. The performances of above said mmWave links are compared in terms of their coverage probability and throughput. The results show that an Epsilon-Greedy algo¬rithm outperforms an Epsilon-Soft algorithm.en
dc.formattextcs
dc.format.extent555-562cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2020/20_03_0555_0562.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectHeterogeneous networken
dc.subjectmillimeter waveen
dc.subjectdynamic blockageen
dc.subjectQ-Learningen
dc.subjectepsilon-greedy algorithmen
dc.titleMachine Intelligence Technique for Blockage Effects in Next-Generation Heterogeneous Networksen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue3cs
dc.coverage.volume29cs
dc.identifier.doi10.13164/re.2020.0555en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International license