Soft Decision Cooperative Spectrum Sensing With Entropy Weight Method for Cognitive Radio Sensor Networks

By fully utilizing the spatial gain and exploiting the multiuser diversity, cooperative spectrum sensing can enhance the sensing accuracy. In the actual wireless environment, the effect of shadowing and fading will result in the different features of signals received by the sensing nodes with different distances from primary user. As a result, some cooperative nodes in deep fading will suffer from serious missed detection, which will affect the final results during the fusing operation. To solve the above problems, a soft decision cooperative spectrum sensing with entropy weight method for cognitive radio sensor networks is presented. Initially, the sensor nodes will be organized into logical groups to obtain energy efficiency and improvement of sensing performance. After receiving the soft sensing information from all member nodes, the cluster heads employs the equal gain soft combination for inter-cluster fusion and then forwards the local decision to the fusion center. During the final decision, the entropy weight method is applied to assign optimal weight value to corresponding cluster local decisions. The simulation results show that the proposed method can outperform some typical clustering scheme for cooperative spectrum sensing in terms of the detection probability and the total error probability.