Spectral Vacancy Prediction Using Time Series Forecasting for Cognitive Radio Applications

Abstract

An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings.From the simulation, it can be inferred that Weibull fading outperforms all the other fading models considered. To accomplish missed detection probability of 1% in the Rayleigh channel, the false alarm probability obtained is almost 0.8 however to obtain the same missed detection probability in the Weibull channel, false alarm probability is less than 0.1 which is very favorable for both indoor and outdoor scenarios. Numerical analyses are carried out here to predict Primary User (PU) channel condition using Hidden Markov Model with the help of Time series forecasting learning method. It is evident that the prediction performance has reached 100% as the result of using the Weibull Fading Model for a period of 200 ms when compared to the Rayleigh model which is achieving only 84.5% accuracy in prediction.

An Overview of Cognitive Radio Technology and Its Applications in Civil Aviation

“…A new method to identify unfilled PU spectrum was proposed in [22], which analyzed the collaboration between users using machine learning methods. Learning methods are applied to construct classifiers, appropriate fusion algorithms are selected for the environment under consideration, and efficient out of band sensing is performed.…”