Performance Analysis And Optimization Of K-Rank Fusion Criteria Based Cooperative Spectrum Sensing

With the rapid development of wireless communication services,the demand for spectrum resources of mobile communication equipment is increasing.However,at present,high-quality spectrum resources have been basically allocated globally,and the spectrum resources reserved for emerging services are very limited.The dynamic spectrum access technology proposed by cognitive radio(CR)can effectively improve spectrum utilization.Single user spectrum sensing does not perform well in the face of shadowing and multipath fading.Cooperative spectrum sensing(CSS)also faces many security issues while solving these problems.When there is a primary user emulation attack(PUEA)in the cognitive network,PUEA will mislead the cognitive user(CU)to make a wrong decision,resulting in the failure of the fusion center's decision and ultimately destroying the performance of the entire cognitive network.At the same time,the existence of the dedicated reporting channel greatly increases the system overhead of the cognitive network.The article proposes a cooperative spectrum sensing model without dedicated reporting channel under the Krank optimization criterion.The article studies the problem of cooperative spectrum sensing in cognitive networks in various environments.Through the K-rank optimization criterion and the adjustment of cognitive network parameters,the detection performance of the system is improved.The specific work of the article is introduced as follows:First,the article studies the cooperative spectrum sensing problem of jointly considering the detection channel and the dedicated reporting channel.The article optimizes the cooperative spectrum sensing scheme based on energy detection.On this basis,the goal of minimizing the global average error probability is used to derive the K-rank optimization criterion.Simulation results show that,compared with the traditional fusion criterion,the K-rank optimization criterion effectively improves the detection performance of the cognitive network.There is an optimal detection threshold,which minimizes the global average error probability.Next,the article introduces a new intelligent PUEA model.Taking the CU and PUEA users' false alarm probability as a constraint,the global average error probability of the system is optimized by the K-rank optimization criterion.The simulation results show that the detection threshold and the number of CU will affect the system detection performance.Finally,the article studies the cooperative spectrum sensing technology without dedicated reporting channels under Rayleigh fading channels.The article considers dividing a sensing time slot into a detection time slot and a reporting time slot.Each CU splits the reporting time slot in the time domain and sends a detection report to the fusion center.This solution reduces the system overhead while increasing the interference to PU.By constraining the transmission power of CU and the global detection probability of CU,the interruption probability of PU can be effectively reduced,and the Qo S of PU can be guaranteed.At the same time,the global average error probability is used as the objective function to optimize the fusion criterion.Simulation results show that as the number of CU and the transmit power of CU increase,the objective function continues to decrease,and there is an optimal detection overhead to minimize the objective function value.