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Poster communications

Multi-Armed bandit Learning in Iot Networks (MALIN)

Remi Bonnefoi 1, 2, 3, 4 Lilian Besson 1, 4, 5, 2, 3 Christophe Moy 1, 3, 6
3 SCEE - Signal, Communication et Electronique Embarquée
IETR - Institut d'Electronique et de Télécommunications de Rennes
5 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : With the advent of the Internet of Things (IoT), unlicensed band are going to be shared by a large number of devices with dissimilar caracteristics. In such context, solutions are required to allow the coexistence of devices and to avoid performance drop due to interference. In this demonstration, we show that reinforcement learning algorithms and in particular Multi-Armed Bandit algorithms can be used as a means of improving the performance of IoT communications.
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Poster communications
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Contributor : Lilian Besson Connect in order to contact the contributor
Submitted on : Monday, February 11, 2019 - 1:45:46 PM
Last modification on : Monday, January 24, 2022 - 2:05:33 PM
Long-term archiving on: : Sunday, May 12, 2019 - 1:53:27 PM


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  • HAL Id : hal-02013866, version 1


Remi Bonnefoi, Lilian Besson, Christophe Moy. Multi-Armed bandit Learning in Iot Networks (MALIN). ICT 2018 - 25th International Conference on Telecommunications, Jun 2018, Saint-Malo, France. ⟨hal-02013866⟩



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