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Reinforcement learning application scenario for Opportunistic Spectrum Access

Abstract : We tackle in this work a concrete scenario that illustrates the behavior of Cognitive Radio equipment within an Opportunistic Spectrum Access context. We assume that there exist two sets of users. On the one hand Primary Users who own the spectrum pool of interest. And one the other hand, Secondary Users who aim at exploiting vacant communication opportunities left by Primary Users at a given time in a given band. Moreover, Secondary Users are assumed to have no a priori knowledge on Primary Users behavior and aim at learning missing information while exploiting found communication opportunities. For that purpose, we model Primary Users' frequency bands occupations pattern using an OFDM modulation. While, the introduced secondary user's learning process relies on an energy detector and a reinforcement learning algorithm known as UCB1. The complete model is developed on Simulink to illustrate the behavior of the Primary Network as well as the Secondary User.
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Conference papers
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https://hal-supelec.archives-ouvertes.fr/hal-00606399
Contributor : Myriam Andrieux <>
Submitted on : Wednesday, July 6, 2011 - 1:35:20 PM
Last modification on : Monday, October 5, 2020 - 9:44:02 AM

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  • HAL Id : hal-00606399, version 1

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Wassim Jouini, Robin Bollenbach, Matthieu Guillet, Christophe Moy, Amor Nafkha. Reinforcement learning application scenario for Opportunistic Spectrum Access. 54th IEEE International Midwest Symposium on Circuits & Systems (MWSCAS 2011), Aug 2011, Seoul, North Korea. ⟨hal-00606399⟩

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