Clasificación de la ocupación espectral para la toma de decisiones en redes inalámbricas cognitivas implementando extracción de características y aprendizaje automático

Abstract

Abstract: This study assesses spectral occupancy classification for decision-making processes by implementing feature extraction and classification rules. Cognitive radio (CR) is a technology that seeks to maximize the application of frequency resources by allowing unlicensed users to opportunistically access spectrum bands. The decision-making process is analyzed by classifying three traffic levels and by using three cost metrics and one benefit metric. The results show that the classifier using support vector machines presents the best performance, followed by KnNC (K-nearest Neighbor Classifier) and DAC (Discriminant Analysis Classifier). The worst performance, with the most deficient indicators’ performance, is obtained by using BDT (Binary Decision Tree). It is concluded that CR offers a set of solutions that allows using the spectrum dynamically.