Research On Cognitive Wireless Spectrum Sensing Technology

With the rapid development of wireless communication technology,the number of wireless devices has increased sharply.As the main carrier of information transmission between wireless devices,the available resources are increasingly scarce.Fixed spectrum allocation strategy greatly reduces the utilization of spectrum resources.Cognitive radio,as an efficient spectrum management technology,can effectively improve the utilization of spectrum resources.As the basis of cognitive radio technology,spectrum sensing has become a hot topic of research by many scholars.This thesis mainly studies the spectrum sensing based on generalized stochastic resonance and the spectrum sensing under the fading channel.The research work and innovation of this thesis are as follows:(1)In order to solve the problem that the detection performance of spectrum sensing algorithm is not ideal at low SNR,a dual threshold cooperative spectrum sensing algorithm(DCSSR)based on generalized stochastic resonance is proposed in this thesis.The proposed algorithm is based on generalized stochastic resonance theory and uses noise uncertainty to set double threshold to overcome the influence of noise uncertainty on spectrum sensing algorithm.In addition,the proposed algorithm takes the error probability as the basis to judge the performance of the algorithm,and optimizes the number of users participating in the collaboration in the collaborative spectrum sensing.The algorithm meets the minimum error probability and the detection probability is also the best.The experimental simulation results show that in the case of single user,the proposed DCSSR algorithm,single threshold stochastic resonance algorithm,double threshold energy detection algorithm and single threshold energy detection algorithm,when the SNR is-15 dB and the false alarm probability is 10%,the detection probability is 96%,89%,75% and 51%,respectively.The detection probability of the proposed DCSSR algorithm is significantly improved compared with the previous algorithms.In the case of multi-user collaboration,when the SNR is-20 dB,the detection probability of the proposed DCSSR algorithm is as high as 91%,which is higher than the single threshold stochastic resonance algorithm,single threshold energy detection and double threshold energy detection by optimizing the number of collaborative users.(2)In order to solve the problem that the spectrum sensing algorithm is difficult to detect in the complex fading channel and that the users with low SNR in the cooperative spectrum sensing algorithm have negative influence on the sensing result,this thesis proposes a dynamic threshold cooperative spectrum sensing algorithm in the k-u fading channel.Firstly,the average detection probability based on the energy detection algorithm under the common fading channel is analyzed.Then,a dynamic threshold cooperative spectrum sensing algorithm in k-u fading channel is proposed.The SNR of each cooperative user is used to dynamically change the decision threshold.Reduce the decision threshold of high SNR users and increase their detection probability;The decision threshold of low SNR users is improved to reduce the influence of the decision result on the overall detection performance.The experimental simulation results show that in k-u channel,the performance of spectrum sensing algorithm in Rayleigh,Nakagami-M and Rician fading channels can be analyzed by setting different channel parameters k and u.In addition,the experimental analysis shows that in the collaborative spectrum sensing algorithm based on AND and OR data fusion,the performance of the proposed dynamic threshold collaboration algorithm is also greatly improved compared with the single threshold cooperative algorithm.