A hybrid model for congestion prediction in HF spectrum based on ensemble empirical mode decomposition

Abstract

This paper presents a hybrid model combining AR model with Volterra series expansion that uses Ensemble Empirical Mode Decomposition as preprocessing step for predicting congestion in high-frequency spectrum. In this model, original complex spectral occupancy phenomenon is decomposed into several simpler components among which relatively stable Intrinsic Mode Functions (IMFs) are predicted by AR model and the residue with tendency is modelled by Volterra series expansion; both of AR and Volterra's coefficients are modified by RLS algorithm in a centralized way. We compared the model with stand-alone use of AR model and Volterra adaptive filters for one-step prediction and employed RMSE for performance comparison. The results have demonstrated that the hybrid model enhances the accuracy of prediction to behaviors of spectrum driven from nonlinear and non-stationary processes.