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

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Date
2018-06
Authors
Raca, Darijo
Quinlan, Jason J.
Zahran, Ahmed H.
Sreenan, Cormac J.
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Association for Computing Machinery, ACM
Published Version
10.1145/3204949.3208123
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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.
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Keywords
Dataset , 4G , LTE , ns-3 , Mobility , Throughput , Context information , Adaptive video streaming , Information systems , Multimedia streaming , Networks , Public internet , Wireless access networks , Network measurement
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
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http://www.mmsys2018.org
URI
https://hdl.handle.net/10468/6400
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Computer Science - Conference Items
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© 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.