Reinforcement Learning Approaches and Evaluation Criteria for Opportunistic Spectrum Access

Clément Robert; Christophe Moy; Cheng-Xiang Wang

doi:10.1109/ICC.2014.6883535

Conference papers

Reinforcement Learning Approaches and Evaluation Criteria for Opportunistic Spectrum Access

Clément Robert ¹ Christophe Moy ¹ Cheng-Xiang Wang ²

1 IETR - Institut d'Électronique et des Technologies du numéRique

2 HWU - Heriot-Watt University [Edinburgh]

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.

Keywords : cognitive radio OSA reinforcement learning UCB weight-driven decision making convergence

Document type :

Conference papers

Domain :

Engineering Sciences [physics] / Electronics

Complete list of metadatas

https://hal-supelec.archives-ouvertes.fr/hal-00994933
Contributor : Myriam Andrieux <>
Submitted on : Thursday, May 22, 2014 - 1:48:21 PM
Last modification on : Tuesday, October 6, 2020 - 3:10:03 AM

Reinforcement Learning Approaches and Evaluation Criteria for Opportunistic Spectrum Access

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