Opportunistic Spectrum Access: Online Search of Optimality

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Executive Summary

This paper presents an online tuning approach for the ad-hoc reinforcement learning algorithms which are used for solving the exploitation-exploration dilemma of the opportunistic spectrum access, in dynamic environments. These algorithms originate from a well-known problem in computer science: the Multi-Armed Bandit (MAB) problem and they have provided evidence to be viable solutions for the detection and exploration of white spaces in opportunistic spectrum access. Previous work has shown that the reinforcement learning solutions of the MAB problem are very sensitive to the statistical properties of the wireless medium access and therefore need careful tuning according to the dynamic variations of the wireless environment.

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