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A learning strategy for distributed channel selection in Cognitive Radio networks is proposed. This strategy helps Quality of Service (QoS) provisioning such that competing secondary users cooperatively converge to their rank-optimal channels while channel availability statistics are initially unknown. By this convergence, collision reaches zero since users eventually work on their own orthogonal channels. The rank-optimal channel for each user is identified based on the user's QoS demands. The authors believe that this learning and allocation policy provides a better level of QoS for secondary users since evaluation results represent order optimality in terms of the average throughput.
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