Modelling and Predicting the Behaviour of a Secondary User in Cognitive Radio Using Artificial Intelligence Techniques
In the search for performance and adequate quality of service for the communication of users in cognitive wireless networks, spectral transfer is of great importance. The possible and poor use of the spectrum, combined with the increased use of the radiofrequency environment in the last years, has degraded the quality of service for various wireless networks and applications, as for example, in the cellular network. This has led to the development of new research on the access to the dynamic spectrum that converges in the use of "cognitive radio", as an essential parameter for the use of licensed spectrum, well above the currently detected consumption values. This paper presents the procedure and main results of a comparative study based on the use of two computational intelligence tools applied in a task of prediction of a series of chaotic time, which represents the intervention of the Secondary User in the wireless network. The methods of forecasting time series are ANFIS algorithm (Fuzzy Inference System Based on Adaptive Networks) and neural networks. Then the results of this study are discussed based on criteria such as the required processing time and the mean squared error and its root.
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