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

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.

Multi-Response Weighted Adaptive Sampling Approach Based on Hybrid Surrogate Model

“…where j  is the weight coefficient of th j response which is obtained by the entropy weight approach based on the initial sample data [37]. When t=1, it is the single response version of the weighted prediction variance.…”

Research on the Reliability Allocation Method for a Wind Turbine Generator System Based on a Fuzzy Analytic Hierarchy Process Considering Multiple Factors

“…In terms of the involved factors, evaluations and judgments are made as objectively as possible when determining the allocation weight via the FAHP; however, the uncertainty and fuzziness of the information cause experts to evaluate them with subjective feelings. The EWM [25] is an evaluation method used to determine the weight based on the amount of information contained in each factor. The basic idea behind this method is to determine the objective weight according to the size of the index variability, thus avoiding focusing only on the subjective judgment of decision makers and making the above-mentioned allocation weight vector more objective.…”

Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

“…Rayleigh fading channel is the main cause of low SNR in practical, so many related works have been suggested for the obligation of users' cooperation to cope with the problem of cognitive radio detection. Cooperative sensing achieves diversity gain and can reduce the detection time and thus increase the overall spectrum agility [18]. However, the cooperative sensing performance can be severely degraded due to fading reporting/control channel [19], where usually the low SNR is used.…”