A weighted selection combining schemefor cooperative spectrum prediction in cognitive radio networks

A weighted selection combining(WSC)scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network(GANN)is designed to perform spectrum prediction in consideration of both the characteristics of the primary users(PU)and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis(ISODATA)algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.