Throughput Prediction in Cellular Networks: Experiments and Preliminary Results

Abstract : Throughput has a strong impact on user experience in cellular networks. The ability to predict the throughput of a connection, before it starts, will bring new possibilities, particularly to the Internet service providers. They could adapt contents to the quality of service really reachable by users, in order to enhance their experience. First this study highlights the prediction capabilities thanks to different algorithms and data gathered at different network levels. Then we propose a simple approach based on machine learning to predict the throughput using a few data related to the context of use.
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https://hal.archives-ouvertes.fr/hal-01311158
Contributor : Alassane Samba <>
Submitted on : Tuesday, May 10, 2016 - 5:04:47 PM
Last modification on : Thursday, November 15, 2018 - 11:57:44 AM
Long-term archiving on : Tuesday, May 24, 2016 - 7:30:56 PM

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  • HAL Id : hal-01311158, version 1

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Alassane Samba, Yann Busnel, Alberto Blanc, Philippe Dooze, Gwendal Simon. Throughput Prediction in Cellular Networks: Experiments and Preliminary Results. CoRes 2016, May 2016, Bayonne, France. ⟨hal-01311158v1⟩

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