Empirical Mode Decomposition Support Research Articles

Objectives: The analysis of spatial opportunities to reuse frequencies by secondary users (SU) in a Cognitive Radio (CR) network is the main objective of this work. Methods/Statistical Analysis: Here lineal and no-lineal models are developed and evaluated to forecast the received power of different channels base on measurements performed in Bogota Colombia for the global system for mobile communication (GSM) bands. Seasonal autoregressive integrated moving average (SARIMA), generalized autoregressive conditional heteroskedastic (GARCH), Markov, empirical mode decompositionsupport vector regression (EMD-SVR) and wavelet neural models were utilized for the forecasting of the channel occupancy. Findings: The analysis performed shows that the wavelet neural model presents less error for the received power forecast compared to the other models analyzed and developed in this work. This is in particular for the different types of mean absolute error evaluated. In addition, the accuracy percentage reach by the wavelet neural model is greater than 95% for the forecast of the availability and occupancy times of channels. Application/Improvements: Accordingly, this research indicates that wavelet neural model depicts a hopefuloption to CR systems, in order to forecast received power for the detection of spectral opportunities.