Competition: Channel Exploration/Exploitation Based on a Thompson Sampling Approach in a Radio Cognitive Environment

Abstract : Machine learning approaches have been extensively applied in interference mitigation and cognitive radio devices. In this work, we model the spectrum selection process as a multi-arm bandit problem and apply Thompson sampling, a fast and efficient algorithm, to find the best channel in the shortest time interval. The learning algorithm will work on top of a network layer to efficiently route the event information to the sink.
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Communication dans un congrès
EWSN - International Conference on Embedded Wireless Systems and Networks (dependability competition), Feb 2016, Graz Austria
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https://hal.inria.fr/hal-01249135
Contributeur : Viktor Toldov <>
Soumis le : mercredi 30 décembre 2015 - 12:03:07
Dernière modification le : samedi 13 octobre 2018 - 01:07:33
Document(s) archivé(s) le : mardi 5 avril 2016 - 13:44:12

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Arash Maskooki, Viktor Toldov, Laurent Clavier, Valeria Loscrí, Nathalie Mitton. Competition: Channel Exploration/Exploitation Based on a Thompson Sampling Approach in a Radio Cognitive Environment. EWSN - International Conference on Embedded Wireless Systems and Networks (dependability competition), Feb 2016, Graz Austria. 〈hal-01249135〉

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