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Reinforcement learning demonstrator for opportunistic spectrum access on real radio signals

Abstract : This demonstration presents a proof-of-concept for opportunistic spectrum access. It particularly focuses on reinforcement learning algorithm called UCB (Upper Confidence Bound) designed by the machine learning community to solve the MAB problem (Multi-Armed Bandit). The demonstrator shows the first worldwide implementation of reinforcement learning algorithms for OSA (opportunistic spectrum access) on real radio environment using USRP N210 platforms.
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https://hal-supelec.archives-ouvertes.fr/hal-01262063
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Submitted on : Thursday, February 4, 2016 - 11:25:51 AM
Last modification on : Monday, October 5, 2020 - 9:50:23 AM
Long-term archiving on: : Friday, November 11, 2016 - 4:48:56 PM

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Christophe Moy, Amor Nafkha, Malek Naoues. Reinforcement learning demonstrator for opportunistic spectrum access on real radio signals. 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Sep 2015, Stockholm, Sweden. ⟨10.1109/DySPAN.2015.7343919⟩. ⟨hal-01262063⟩

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