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Conference Papers Year : 2014

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.
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Dates and versions

hal-00994933 , version 1 (22-05-2014)

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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., ⟨10.1109/ICC.2014.6883535⟩. ⟨hal-00994933⟩
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