Beyond throughput: the next Generation a 5G dataset with channel and context metrics

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dc.contributor.author Raca, Darijo
dc.contributor.author Leahy, Dylan
dc.contributor.author Sreenan, Cormac J.
dc.contributor.author Quinlan, Jason J.
dc.date.accessioned 2020-04-15T10:01:38Z
dc.date.available 2020-04-15T10:01:38Z
dc.date.issued 2020-06
dc.identifier.citation Raca, D., Leahy, D., Sreenan, C. J. and Quinlan, J. J. (2020) 'Beyond Throughput: The Next Generation a 5G Dataset with Channel and Context Metrics', ACM Multimedia Systems Conference (MMSys '20), Istanbul, Turkey, 8-11 June, [To Appear] en
dc.identifier.startpage 1 en
dc.identifier.endpage 6 en
dc.identifier.uri http://hdl.handle.net/10468/9826
dc.description.abstract In this paper, we present a 5G trace dataset collected from a major Irish mobile operator. The dataset is generated from two mobility patterns (static and car), and across two application patterns (video streaming and file download). The dataset is composed of client-side cellular key performance indicators (KPIs) comprised of channel-related metrics, context-related metrics, cell-related metrics and throughput information. These metrics are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 5G networks. To supplement our realtime 5G production network dataset, we also provide a 5G large scale multi-cell ns-3 simulation framework. The availability of the 5G/mmwave module for the ns-3 mmwave network simulator provides an opportunity to improve our understanding of the dynamic reasoning for adaptive clients in 5G multi-cell wireless scenarios. The purpose of our framework is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. Our framework, permits other researchers to investigate this interaction through the generation of their own synthetic datasets. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Association for Computing Machinery en
dc.relation.uri https://2020.acmmmsys.org/
dc.rights © 2020 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. en
dc.subject Dataset en
dc.subject 5G en
dc.subject NR en
dc.subject Mobility en
dc.subject Throughput en
dc.subject Context information en
dc.subject Adaptive video streaming en
dc.subject mmwave en
dc.subject Public Internet en
dc.subject Wireless access networks en
dc.subject Network measurement en
dc.subject Multimedia streaming en
dc.title Beyond throughput: the next Generation a 5G dataset with channel and context metrics en
dc.type Conference item en
dc.internal.authorcontactother Jason Quinlan, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: j.quinlan@cs.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2020-04-15T09:45:11Z
dc.description.version Accepted Version en
dc.internal.rssid 510475393
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Istanbul, Turkey en
dc.internal.IRISemailaddress j.quinlan@cs.ucc.ie en
dc.internal.bibliocheck In press. Add doi, uri,volume, pages, update citation. en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/ en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Investigator Programme/13/IA/1892/IE/An Internet Infrastructure for Video Streaming Optimisation (iVID)/ en


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