Research On Spectrum Sensing Technology In Cognitive Radio

With the rapid development and commercialization of 5G technology in the 21 st century,the supply of spectrum resources is far less than demand.Cognitive radio was proposed to solve this problem.Secondary users dynamically perceive the working status of nearby spectrum resources to determine whether the primary user exists,so as to achieve organic access to the spectrum for secondary users.Currently,most spectrum sensing algorithms have two main problems: the detection performance is easily affected by environmental factors,the detection probability is low,and the complexity is high.In response to the above problems,this paper has carried out the research of spectrum sensing technology in cognitive radio,and the main innovations are as follows:First,in the current wireless communication environment,the spectrum sensing algorithm has high computational complexity and low detection probability.A cooperative spectrum sensing algorithm based on the combination of random matrix and energy is proposed.According to the false alarm probability,the threshold threshold is derived by formula.The simulation results show that the proposed algorithm has higher detection efficiency and higher detection probability than the energy detection algorithm,improved algorithm for ratio of eigenvalues,and the MME algorithm.When the signal-to-noise ratio is-12 d B and the false alarm probability is 0.05,the proposed algorithm compares the energy detection algorithm,improved algorithm for ratio of eigenvalues,the MME algorithm,and the detection probability is increased by 171% and 54.2%,112.2%,respectively;In the case of a signal-to-noise ratio of-5d B and a false alarm probability of 0.01,the detection probability of the proposed algorithm is basically consistent with the energy detection algorithm.Compared with the MME algorithm and improved algorithm for ratio of eigenvalues,the detection probability increased by 18.8% and 10.6% respectively.Then,in view of the fact that single-node spectrum sensing is susceptible to path loss,malicious attacks and other factors,a cooperative spectrum sensing algorithm for the ratio of eigenvalues based on evidence theory(REVET)is proposed.The algorithm uses the eigenvalues ratio detection method as the local perception.By calculating the basic probability distribution function and weighting coefficient of each secondary user,the actual basic probability distribution function is obtained and sent to the fusion center.Based on the evidence theory,the fusion center performs data fusion on the basic probability distribution function and obtains the final result.The simulation results show that the proposed algorithmhe is higher detection probability than collaborative energy detection algorithm based on evidence theory,the eigenvalue ratio algorithm based on the "AND" criterion,and the eigenvalue ratio algorithm based on the "OR" criterion.and.when the false alarm probability is 0.01 and the signal-to-noise ratio is-13 d B,compared with the above three algorithms,and the detection probability of the REVET algorithm is increased by 22%,30%,and7.6% respectively;the fusion center will be attacked with a probability of 30% in the case of a malicious attack with an intensity of 2,the REVET algorithm has improved detection probabilities by 150%,17.8%,and 11.5% respectively.,compared with the other three algorithms.Finally,in view of the fact that the current spectrum sensing algorithm is susceptible to noise uncertainty,multipath effects,hidden terminals and other factors,a selective cooperative spectrum sensing algorithm based on covariance matrix(a selective cooperative spectrum sensing algorithm based on covariance matrix,SCCMA).The algorithm calculates the entropy value and weighting coefficient of each secondary user through the entropy function according to the environmental factors in which each node is located,and finally obtains a comprehensive score.The nodes with higher scores are selected for covariance detection,and the fusion center adopts the AND criterion to The local perception result is judged.Simulation results show that,compared with local perception,the proposed SCCMA algorithm adopts the selection cooperation algorithm of the feature value improvement algorithm,the energy detection algorithm based on the entropy function,and the feature value improvement algorithm.When the signal-to-noise ratio is less than-8d B,the detection probability is higher,and at the same time The proposed algorithm has low computational complexity and is more suitable for the harsh perception environment in the future.When the false alarm probability is 0.1 and the signal-to-noise ratio is-11 d B,the detection probability of the eigenvalue improvement algorithm and the energy detection algorithm based on the entropy function are all 0.The SCCMA spectrum sensing algorithm adopts the selection cooperation of the eigenvalue improvement algorithm compared with the local perception.Algorithm,the detection probability is increased by 3.13 times.