Spectrum prediction in cognitive radio systems using a wavelet neural network
Ashraf A. Eltholth · 2016
Accurate Spectrum Prediction is a promising solution to save sensing energy and time, it also helps to increase throughput in cognitive radio systems. In this paper, we investigate the use of wavelet neural networks (WNN) in predicting future channel occupancy status. The proposed work builds upon existing concepts in open literature, gathered in a general framework for applying WNN. An experimental measurement in the 100-200 MHz band, are used for either training or validation of the proposed model. The trade-off between accuracy, utilization and parameters initialization have been explained, that helped in selecting a near optimal model. Computer simulations show the effectiveness of using WNN in spectrum prediction as compared to previous works in literature, in terms of predictive accuracy and stability in prediction.