Beyond throughput: a 4G LTE dataset with channel and context metrics

Show simple item record Raca, Darijo Quinlan, Jason J. Zahran, Ahmed H. Sreenan, Cormac J. 2018-07-02T11:37:51Z 2018-07-02T11:37:51Z 2018-06
dc.identifier.citation Raca, D., Quinlan, J. J., Zahran, A. H. and Sreenan, C. J. (2018) 'Beyond Throughput: a 4G LTE Dataset with Channel and Context Metrics', Proceedings of ACM Multimedia Systems Conference (MMSys 2018), Amsterdam, The Netherlands, 12-15 June. doi: 10.1145/3204949.3208123 en
dc.identifier.startpage 1 en
dc.identifier.endpage 6 en
dc.identifier.doi 10.1145/3204949.3208123
dc.description.abstract In this paper, we present a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, bus and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a well-known non-rooted Android network monitoring application, GNetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 4G networks. To supplement our real-time 4G production network dataset, we also provide a synthetic dataset generated from a large-scale 4G ns-3 simulation that includes one hundred users randomly scattered across a seven-cell cluster. The purpose of this dataset 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. In addition to this dataset, we also provide the code and context information to allow other researchers to generate their own synthetic datasets. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Association for Computing Machinery, ACM en
dc.relation.ispartof Proceedings of ACM Multimedia Systems Conference (MMSys 2018)
dc.rights © 2018 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 4G en
dc.subject LTE en
dc.subject ns-3 en
dc.subject Mobility en
dc.subject Throughput en
dc.subject Context information en
dc.subject Adaptive video streaming en
dc.subject Information systems en
dc.subject Multimedia streaming en
dc.subject Networks en
dc.subject Public internet en
dc.subject Wireless access networks en
dc.subject Network measurement en
dc.title Beyond throughput: a 4G LTE dataset with channel and context metrics en
dc.type Conference item en
dc.internal.authorcontactother Jason J. Quinlan, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 en
dc.internal.availability Full text available en 2018-07-02T11:31:25Z
dc.description.version Accepted Version en
dc.internal.rssid 443814877
dc.internal.rssid 470731586
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Proceedings of ACM Multimedia Systems Conference (MMSys 2018) en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Amsterdam, The Netherlands en
dc.internal.IRISemailaddress en
dc.internal.IRISemailaddress 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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