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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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https://hal.inria.fr/hal-01249135
Contributor : Viktor Toldov <>
Submitted on : Wednesday, December 30, 2015 - 12:03:07 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:54 PM
Long-term archiving on: : Tuesday, April 5, 2016 - 1:44:12 PM

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  • HAL Id : hal-01249135, version 1

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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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