Wireless spectrum prediction model based on time series analysis method
Proceedings of the 2009 ACM workshop on Cognitive radio networks
| Hi-index | 0.00 |
Support Vector Regression (SVR) is now a well-established method for non-stationary series forecasting, because of its good generalization ability and guaranteeing global minima. However, only using SVR hardly get satisfied accuracy for complicated frequency spectrum prediction in frequency monitor system (FMS) of High Frequency radar. Empirical mode decomposition (EMD) is perfectly suitable for nonlinear and non-stationary signal analysis. By using EMD, any complicated signal can be decomposed into several time series that have simpler frequency components and thus are easier and more accuracy to be forecasted. Therefore, in this paper, a novel prediction algorithm called EMD-SVR is proposed. Experiment results illustrate that EMD-SVR model significantly outperform conventional AR model and common SVR model in FMS frequency spectrum series prediction.