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Characterization of Primary Users in Cognitive Radio Wireless Networks using Support Vector Machine
Objectives: This paper aims to solve a problem of existing research for Cognitive Radio networks Unlicensed (specifically for Wi-Fi with centralized network topology), which aims at the spectral making decision stage to efficiently characterize the behavior of users (called PUs) in order to identify spectral opportunities that can be used beneficially by other wireless applications or users. Methods/ Statistical Analysis: The methodology includes capturing the dynamics of channel usage by a user in the 2.4 GHz band, later to apply a pre-processing of the data and reach an estimate for the level of modeling (based training system) and prediction. Findings: The possible solution that was raised during the development of the research was to use a Support Vector Machine (SVM) to estimate the prediction by classification. Application/Improvements: The results suggest that the use of SVM does not correspond to the methodology of artificial intelligence accurately because the level of delivered estimate is not accurate.
Characterization, Cognitive Radio, Prediction, Primary Users, Spectral Decision, Support Vector Machine, Wi-Fi
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