Collaborative spectral opportunity forecasting for cognitive radio

Abstract

Combining spectrum sensing (SS) and primary user (PU) traffic forecasting provides a cognitive radio network (CRN) with a platform from which informed and proactive operational decisions can be made. The success of these decisions is largely dependent on prediction accuracy. Since individual SUs may suffer from SS and prediction inaccuracies due to poor channel conditions, allowing secondary users (SU) to perform these predictions in a collaborative manner allows for an improvement in the accuracy of this process. A collaborative approach to forecasting PU traffic, that combines SS and forecasting through SU cooperation, was proposed in this paper. A sub-optimal cooperative forecasting algorithm was presented to minimise cooperative prediction error. The algorithm was used to investigate the cooperative prediction performance of a group of ten SUs experiencing different channel conditions. Simulation results indicated that cooperative prediction lead to a significant improvement in prediction accuracy and illustrated how diversity, both in terms of SS accuracy and individual prediction performance, can positively impact the prediction process.