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

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Date
2020-06
Authors
Raca, Darijo
Leahy, Dylan
Sreenan, Cormac J.
Quinlan, Jason J.
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Association for Computing Machinery
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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.
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Dataset , 5G , NR , Mobility , Throughput , Context information , Adaptive video streaming , mmwave , Public Internet , Wireless access networks , Network measurement , Multimedia streaming
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]
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© 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.