Competition: Channel Exploration/Exploitation Based on a Thompson Sampling Approach in a Radio Cognitive Environment
Résumé
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.
| Origine | Fichiers produits par l'(les) auteur(s) |
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