Abstract
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.
Highlights
By employing cognitive radio (CR) technology, the sensor nodes in cognitive radio sensor networks (CRSNs) can change its parameters according to the interactions with the environment
The sensing channel is a Gaussian fading channel, and the average signal-to-noise ratio (SNR) is set as a constant value
The attenuation coefficient of each channel is randomly generated based on the average SNR
Summary
By employing cognitive radio (CR) technology, the sensor nodes in cognitive radio sensor networks (CRSNs) can change its parameters according to the interactions with the environment. As a kind of network with cognitive ability to spectrum resources, CRSN can make more flexible use of the sensing results. For cognitive wireless sensor nodes, limited by hardware conditions, their sensing radius and signal processing ability have great limitations. It is difficult for a single sensor to achieve the accuracy of detection results in line with the requirements of the system especially under the imperfect physical environment. For hardware-limited sensors, their sensing radius and signal processing ability have great limitations It is difficult for a single node to achieve sufficient detection accuracy in the above-mentioned physical environment.
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