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Channel Selection with Rayleigh Fading: a Multi-Armed Bandit Framework

Abstract : Channel Selection in fading environments with no prior information on the channels' quality is a challenging issue. In the case of "Rayleigh channels" the measured Signal-To-Noise Ratio follows exponential distributions. Thus, we suggest in this paper a simple algorithm that deals with resource selection when the measured samples are drawn from exponential distributions. This strategy, referred to as Multiplicative Upper Confidence Bound Algorithm (MUCB), associates a utility index to every available arm, and then selects the arm with the highest index. For every arm, the associated index is equal to the product of a multiplicative factor by the sample mean of the rewards collected by this arm. We show that MUCB policies are order optimal. Moreover simulations illustrate and validate the stated theoretical results.
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Contributor : Myriam Andrieux <>
Submitted on : Thursday, July 26, 2012 - 1:53:19 PM
Last modification on : Monday, October 5, 2020 - 9:50:15 AM


  • HAL Id : hal-00721010, version 1


Wassim Jouini, Christophe Moy. Channel Selection with Rayleigh Fading: a Multi-Armed Bandit Framework. SPAWC 2012, Jun 2012, Çeşme, Turkey. 5 p. ⟨hal-00721010⟩



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