Reinforcement Learning Approaches and Evaluation Criteria for Opportunistic Spectrum Access

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.
Type de document :
Communication dans un congrès
IEEE ICC'14, Jun 2014, Sydney, Australia. 6 p., 2014
Liste complète des métadonnées

https://hal-supelec.archives-ouvertes.fr/hal-00994933
Contributeur : Myriam Andrieux <>
Soumis le : jeudi 22 mai 2014 - 13:48:21
Dernière modification le : jeudi 5 avril 2018 - 12:30:18

Identifiants

  • HAL Id : hal-00994933, version 1

Citation

Clément Robert, Christophe Moy, Cheng-Xiang Wang. Reinforcement Learning Approaches and Evaluation Criteria for Opportunistic Spectrum Access. IEEE ICC'14, Jun 2014, Sydney, Australia. 6 p., 2014. 〈hal-00994933〉

Partager

Métriques

Consultations de la notice

892