Throughput Prediction in Cellular Networks: Experiments and Preliminary Results

Alassane Samba 1, 2, 3 Yann Busnel 4, 5 Alberto Blanc 1, 3 Philippe Dooze 2 Gwendal Simon 1, 3
1 ADOPNET - Advanced technologies for operated networks
UR1 - Université de Rennes 1, Télécom Bretagne, IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
5 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS
Inria Rennes – Bretagne Atlantique , IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
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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Submitted on : Thursday, May 19, 2016 - 2:43:30 PM
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Alassane Samba, Yann Busnel, Alberto Blanc, Philippe Dooze, Gwendal Simon. Throughput Prediction in Cellular Networks: Experiments and Preliminary Results. 1ères Rencontres Francophones sur la Conception de Protocoles, l’Évaluation de Performance et l’Expérimentation des Réseaux de Communication (CoRes 2016), May 2016, Bayonne, France. ⟨hal-01311158v2⟩

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