Research On Credibility-Based Cooperative Spectrum Sensing Algorithm

With the rapid development of wireless communication technology, the contradiction betweenthe demand and supply for spectrum resources is becoming increasingly fierce. However, the fixedspectrum resources allocation policy makes the utilization of spectrum resources is low. Cognitiveradio has been widely recognized as one of the effective means of intellisense spectrumenvironment, since it can dynamically utilize spectrum spaces which are vacant and effectivelyimprove the utilization of the spectrum resources.Spectrum sensing is one key technology of cognitive radio，the fundamental role of it is tomonitor the usage of spectrum timely and reliably. Cooperative spectrum sensing among multipleusers is one of the effective methods to weaken the effect on the sensing performance of theshadowing and multi-path in wireless channels and is attracting increasing attention.On the basis of researching the existing cooperative spectrum sensing algorithms, a nodeselection-based cooperative algorithm for spectrum sensing is analyzed. In this algorithm, in orderto select the nodes to participate in the cooperative sensing, the cluster head node is set. Eachsecondary user obtains the local spectrum detection by eigenvalue detection algorithm andcalculates the reliability according to the probability of detection and false alarm probability，andsends the reliability to the cluster head node. Then, the cluster head node selects the nodes with highreliability to participate in the global sensing. Then the weighted factors are derived fromcreditability values, and the global decision is made by weighted fusion at fusion center. Thesimulation analysis show that the proposed algorithm only need to select half of the nodes toparticipate in the collaboration in order to meet the requirement of the detect performance of thesystem. It greatly save the overhead of the control channel and reduce the computational complexityof the fusion center. Simultaneously, the algorithm provides better performance improvement whencompared to equal weight and SNR-based weight fusion schemes.