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dc.contributor.authorSchwarzmann, Susanna
dc.contributor.authorMarquezan, Clarissa Cassales
dc.contributor.authorTrivisonno, Riccardo
dc.contributor.authorNakajima, Shinichi
dc.contributor.authorZinner, Thomas
dc.date.accessioned2021-02-25T14:21:20Z
dc.date.available2021-02-25T14:21:20Z
dc.date.created2020-12-06T13:56:15Z
dc.date.issued2020
dc.identifier.isbn978-1-7281-5089-5
dc.identifier.urihttps://hdl.handle.net/11250/2730488
dc.description.abstractSince their first release, 5G systems have been enhanced with Network Data Analytics Functionalities (NWDAF) as well as with the ability to interact with 3rd parties' Application Functions (AFs). Such capabilities enable a variety of potentials, unimaginable for earlier generation networks, notable examples being 5G built-in Machine Learning (ML) mechanisms for QoE estimation, subject of this paper. In this work, an ML-based mechanism for video streaming QoE estimation in 5G networks is presented and evaluated. The mechanism relies on an ML algorithm embedded in NWDAF, the collection of 5G network KPIs, and the collection of QoE information from video streaming service provider, i.e., the 3rd party AF. The mechanism has been evaluated in terms of QoE estimation accuracy against the cost in terms of required input sources and data for the estimation, and its performance has been compared to alternative methodologies not making use of ML. The evaluation, via simulation activity, clearly highlights the benefits of the proposed mechanism. Based on the derived results, the required input sources are ranked with respect to their importance.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofICC 2020 - 2020 IEEE International Conference on Communications (ICC)
dc.titleAccuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G Networksen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.identifier.doi10.1109/ICC40277.2020.9148685
dc.identifier.cristin1856604
dc.description.localcode© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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