Spectrum prediction based on improved-back-propagation neural networks

Abstract

Spectrum prediction in the cognitive radio system attracts more and more attention. It can predict future spectrum holes to save energy of spectrum sensing and to improve the efficiency of spectrum access. The current research on spectrum prediction is to use the prediction model such as back propagation (BP) neural network to predict. However, the performance of conventional spectrum prediction is not satisfied to meet the real system for its using inaccurate spectrum states and defects of the BP neural network. Therefore, we propose a spectrum prediction based on improved-BP neural networks. In the proposed model, the channel power values information instead of the channel states are used as the inputs of the spectrum prediction, the BP neural network optimized by the genetic algorithm and momentum algorithm is utilized in the prediction process, and the threshold interval is applied to determine predicted channel states. Our experimental results demonstrate that the predictive accuracy of the proposed spectrum prediction based on the improved-BP neural network is higher than spectrum prediction based on conventional BP neural network.