A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio

The Cognitive Radio (CR) technology enables the unlicensed users to share the spectrum with the licensed users on a non-interfering basis. Spectrum sensing is an important function for the unlicensed users to determine availability of a channel in the licensed user's spectrum. However, spectrum sensing consumes considerable energy which can be reduced by employing predictive methods for discovering spectrum holes. Using a reliable prediction scheme, the unlicensed users will sense only those channels which are predicted to be idle. By achieving a low probability of error in predicting the idle channels, the spectrum utilization can also be improved. Since the traffic characteristics of most licensed user systems encountered in real life are not known a priori, we design the spectrum predictor using the neural network model, multilayer perceptron (MLP), which does not require a prior knowledge of the traffic characteristics of the licensed user systems. The performance of the spectrum predictor is analyzed through extensive simulations.