Prediction of spectrum based on improved RBF neural network in cognitive radio

Abstract

Spectrum prediction is a key technology of cognitive radio, which can help unlicensed users to determine whether the licensed user's spectrum is idle. Based on radial-basis function (RBF) neural network, this paper proposed a spectrum prediction algorithm with K-means clustering algorithm (K-RBF). This algorithm could predict the spectrum holes according to the historical information of the licensed user's spectrum. It not only increases the veracity of spectrum sensing, but also improves the efficiency of spectrum sensing. Simulation results showed that this prediction algorithm can predict the spectrum accessing of the licensed user accurately and the prediction error is only one-third of that of the RBF neural network.