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An Experimental Assessment of Channel Selection in Cognitive Radio Networks

Abstract : The management of future networks is expected to fully exploit cognitive capabilities that embrace knowledge and intelligence, increasing the degree of automation, making the network more self-autonomous and enabling a personalized user experience. In this context, this paper presents the use of knowledge-based capabilities through a specific lab experiment focused on the Channel Selection functionality for Cognitive Radio Networks (CRN). The selection is based on a supervised classification that allows estimating the number of interfering sources existing in a given frequency channel. Four different classifiers are considered, namely decision tree, neural network, naive Bayes and Support Vector Machine (SVM). Additionally, a comparison against other channel selection strategies using Q-learning and game theory has also been performed. Results obtained in an illustrative and realistic test scenario have revealed that all the strategies allow identifying an optimum solution. However, the time to converge to this solution can be up to 27 times higher according to the algorithm selected.
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Submitted on : Friday, June 22, 2018 - 2:13:54 PM
Last modification on : Friday, January 7, 2022 - 11:00:13 AM
Long-term archiving on: : Monday, September 24, 2018 - 5:15:17 PM


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Anna Umbert, Oriol Sallent, Jordi Perez-Romero, Juan Sánchez-González, Diarmuid Collins, et al.. An Experimental Assessment of Channel Selection in Cognitive Radio Networks. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. pp.78-88, ⟨10.1007/978-3-319-92016-0_8⟩. ⟨hal-01821319⟩



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