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]

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

Domains: Electronics

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⟩