Abstract : This paper deals with the learning and decision making issue for cognitive radio (CR). Two reinforcement-learning algorithms proposed in the literature are compared for opportunistic spectrum access (OSA): Upper Confidence Bound (UCB) algorithm and Weight Driven (WD) algorithm. This paper also introduces two new metrics in order to evaluate the machine learning algorithm performance for CR: effective cumulative regret and percentage of successful trials. They provide a fair evaluation means for CR performance.
https://hal-supelec.archives-ouvertes.fr/hal-00994933
Contributeur : Myriam Andrieux
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Soumis le : jeudi 22 mai 2014 - 13:48:21
Dernière modification le : jeudi 5 avril 2018 - 12:30:18
