Cognitive Radio Channel Selection Strategy Based on Experience-Weighted Attraction Learning

In this paper, an innovative proposed channel selection algorithm based on Experience-Weighted Attraction (EWA) learning allows Cognitive Radio (CR) to learn radio environment communication channel characteristics online. By accumulating the history channel experience, it can predict, select and change the current optimal communication channel, dynamic ensure the quality of communication links and finally reduce system communication outage probability. Validation and reliability have been strictly verified by Matlab simulations. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3900

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