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dc.contributor.authorSchwarzmann, Susanna
dc.contributor.authorMarquezan, Clarissa
dc.contributor.authorBosk, Marcin
dc.contributor.authorLiu, Huiran
dc.contributor.authorTrivisonno, Riccardo
dc.contributor.authorZinner, Thomas Erich
dc.date.accessioned2020-03-20T12:28:09Z
dc.date.available2020-03-20T12:28:09Z
dc.date.created2019-12-20T15:24:52Z
dc.date.issued2019
dc.identifier.isbn978-1-4503-6927-5
dc.identifier.urihttps://hdl.handle.net/11250/2647858
dc.description.abstractCompared to earlier mobile network generations, the 5G system architecture has been significantly enhanced by the introduction of network analytics functionalities and ex- tended capabilities of interacting with third party Application Functions (AFs). Combining these capabilities, new features for Quality of Experience (QoE) estimation can be designed and introduced in next generation networks. It is, however, unclear how 5G networks can collect monitoring data and application metrics, how they correlate to each other, and which techniques can be used in 5G systems for QoE estimation. This paper studies the feasibility of Machine Learning (ML) techniques for QoE estimation and evaluates their performance for a mobile video streaming use-case. A simulator has been implemented with OMNeT++ for generating traces to (i) examine the relevance of features generated from 5G monitoring data and (ii) to study the QoE estimation accuracy (iii) for a variable number of used features.en_US
dc.language.isoengen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.ispartofProceedings of the 4th Internet-QoE Workshop on QoE-based Analysis and Management of Data Communication Networks
dc.titleEstimating Video Streaming QoE in the 5G Architecture Using Machine Learningen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.identifier.doihttps://doi.org/10.1145/3349611.3355547
dc.identifier.cristin1763478
dc.description.localcode© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here, https://doi.org/10.1145/3349611.3355547en_US
cristin.unitcode194,63,30,0
cristin.unitnameInstitutt for informasjonssikkerhet og kommunikasjonsteknologi
cristin.ispublishedtrue
cristin.fulltextpostprint


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