This letter proposes a novel censor-based scheme for cooperative spectrum sensing on Cognitive Radio Sensor Networks. A Takagi-Sugeno's fuzzy system is proposed to make the decision on the presence of the licensed user's signal based on the observed energy at each cognitive sensor node. The local spectrum sensing results are aggregated to make the final sensing decision at the fusion center after being censored to reduce transmission energy and reporting time. Simulation results show that significant improvement of the spectrum sensing accuracy, and saving energy as well as reporting time are achieved by our scheme.

This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.

Cooperation is an attractive approach to improving the spectrum sensing performance of cognitive systems experiencing deep shadowing and fading. In this letter, an efficient weight-based cooperative spectrum sensing scheme is proposed. Simulation results show that the proposed scheme has better accuracy than "AND," "OR," and "half-voting" combination schemes and has similar spectrum sensing accuracy but with lower computational and communication complexity in comparison to the "optimal data fusion" rule.