Research On Key Technologies Of Cooperative Spectrum Sensing Based On Cognitive Radio

Although modern wireless communication uses advanced technology to continuously break through the limit of data rate,spectrum resources are still a scarce and valuable resource.Firstly,most of the spectrum suitable for wireless communication is fixed and authorized,but its spectrum efficiency and utilization rate is low;Secondly as the number of users continues to increase,the spectrum becomes more and more crowded,and various competitions and conflicts continue to occur.In order to solve the shortage of spectrum resources,the researchers proposed cognitive radio technology,which allows unauthorized users to use dynamic access to idle spectrum to maximize the utilization of spectrum resources.Spectrum sensing technology is an important part of cognitive radio,and it is a prerequisite to ensure the dynamic distribution of spectrum resources and reconstruct network parameters.Cooperative spectrum sensing technology can effectively overcome the limitations of the current single-node spectrum sensing technology,so this paper focuses on the key technologies of cooperative spectrum perception in-depth research.Aiming at the problem that the existing collaborative spectrum perceives fixed time slot detection and the influence of noise is more serious,the collaborative sequential detection method based on clustering and the collaborative cyclic spectrum sensing method based on Intelligent algorithm optimization are proposed respectively.Due to the fixed sample detection method wastes channel resources,and sequential detection can dynamically adjust the detection sample data to reduce the characteristics of detection time slot,aiming at the different perceived performance deterioration of different cognitive users and the uncertain location of authorized users under the large scale fading channel,a collaborative sequential detection algorithm based on clustering is proposed.This method selects the cluster head according to the LEACH protocol,and the method of calculating the likelihood ratio statistics in parallel of each cluster is tested sequentially,and the success of the judgment in any cluster is the end of the detection,and the cognitive Radio Network is a two-layer network,which greatly shortens the detection time.The relationship between the optimal throughput of the system and the false alarm probability and the detection time slot is given through theoretical techniques and simulation experiments.Finally,the simulation results show that the proposed algorithm can effectively reduce the detection time compared with the present ordered detection.Based on the study of the stationary characteristics of signal cycle,aiming at the complexity of noise uncertain environment and channel environment,this paper deeply studies the collaborative spectrum sensing technology based on cyclic stationary feature,and puts forward a collaborative cyclic stationary feature detection algorithm based on particle swarm optimization.The cyclic stationary characteristics of BPSK modulation signal are analyzed theoretically and simulated,and the feasibility of signal detection through the cyclic frequency point of signal is verified.Based on the progressiveχ~2 optimal detection algorithm,the simulation experiment compares the difference of detection performance of energy detection under different noise uncertainty,and the results show the superiority of the algorithm.Then,a linear combined expression of soft fusion detection statistics is given by'double torque quasi-legal'.Aiming at the limitation of EGC and MRC Fusion strategy to nonlinear model,this paper puts forward the strategy of optimizing fusion weights by using particle swarm algorithm,and finally verifies the performance superiority of the algorithm compared with other cooperative strategies through simulation experiments.