Modelo inteligente de decisión de espectro que mejora el desempeño en redes de radio cognitiva

Abstract

This document proposes the design of a dynamic decision-making model in cognitive wireless networks that allows secondary users to opportunely harness the spectrum and use channels without affecting the traffic of primary users. The implemented model includes an initial decision-making algorithm with multiple criteria that classifies the best channels according to the spectrum characterization and a final algorithm for spectral occupancy prediction that allows the secondary user to change channels when the current channel is requested by a primary user. This algorithm was chosen after evaluating three prediction techniques. This work manages to integrate two decision methods that contribute to reducing the amount of channel changes that the secondary user must perform. The analysis of the results from prediction techniques indicate that the Grey Rational Analysis (GRA) algorithm in combination with the Support Vector Machine (SVM) algorithm presents the best performance in terms of choosing an available channel, reducing the primary user’s interference and diminishing the rate of necessary handoffs.