Spectrum prediction based on fuzzy system and wavelet network

The future channel state is predicted based on the historical state of the channel, which partly solves the problem of robustness and reliability in spectrum sensing. Precise spectrum prediction is an effective way to conserve perceived energy and time and can increase the throughput of cognitive radio systems. In order to improve the accuracy of the spectral state prediction of cognitive radio systems, the feasibility of spectrum prediction using fuzzy wavelet neural network (FWNN) is studied and verified. Firstly, The use of wavelet to generate fuzzy rules of the system, the gradient algorithm is used to learn the parameters of the fuzzy system. Secondly, the time series is predicted by fuzzy wavelet neural network. Lastly, in the prediction of cognitive radio spectral states, simulation results show that the FWNN algorithm can get better predictive accuracy than other algorithm, and the throughput of secondary user increased by 15.3% than the general spectral prediction algorithm. The improvement of prediction accuracy will promote the application of spectrum prediction in cognitive radio networks, and helpful to solve the problem in robustness and reliability of spectrum sensing.