Research On Primary User Signal Detection Algorithm Based On Signal Correlation In Cognitive Radio

The rapid development and wide application of radio communication technology lead to the shortage of spectrum resource,while the traditional fixed spectrum allocation mode leads to the uneven use and low utilization rate of spectrum.In the context of the contradiction between the supply and demand of spectrum resources,a completely new radio communication technology—cognitive radio,has emerged.Cognitive radio based on spectrum detection technology,in a certain time and space detects the spectrum hole,and grasps the opportunity to use the free frequency bands for communication,so as to improve the purpose of spectrum utilization.In order to improve the efficiency of cognitive radio spectrum detection,this paper makes an in-depth study of radio spectrum detection technology and designs a primary user signal detection algorithm based on signal correlation by utilizing the correlation characteristics of primary user signals.The first chapter introduces the background and significance of this thesis and introduces the concept of cognitive radio and its research status.The second chapter introduces one of the key technologies of cognitive radio spectrum detection.Firstly,the spectrum detection theory,detection algorithm classification,binary hypothesis detection model and detection performance technical index are briefly introduced.Then the classical single-user energy detection algorithm,matched filtering algorithm,cyclostationary feature detection algorithm and multi-user cooperative detection algorithm are introduced in detail.Finally,the advantages and disadvantages of each algorithm and the application scenarios are briefly compared.In the third chapter,a collaborative detection algorithm based on eigenvalue distribution is proposed to overcome the shortcomings of traditional detection methods and considering the relevance of primary user signals.In this algorithm,the difference of sampling covariance matrix is used to construct the detection statistic based on the ratio of the maximum eigenvalue of the covariance matrix to the noise energy,and the new detection threshold is derived based on the random matrix theory under the constant false alarm probability.Simulation results show the effectiveness of the proposed algorithm.The algorithm depends on the covariance of noise and belongs to the semi-blind algorithm.Considering the problem of "signal-to-noise ratio wall" in the traditional energy detection algorithm and the limitations of the semi-blind algorithm proposed in chapter 3,in the fourth chapter further studies the correlation of the primary user signals and proposes a multi-antenna spectrum detection algorithm based on the rank criterion by using the multi-antenna technology.This algorithm based on the fact that the statistical covariance matrix of the received signals of multiple antennas is the rank-1 matrix,decompose the sampling matrix into the ideal matrix of the unknown rank and the noise perturbation matrix,then uses the rank criterion to estimate the optimal dimension of the received signal subspace,and finally the value of the optimal dimension is used to judge the channel state.Compared with the energy algorithm,the new method does not need to know prior signals such as master user signal type,wireless channel condition and noise variance in advance,so it is a completely blind detection algorithm.The algorithm is robust to noise uncertainty,and the simulation results show its superiority.In the fifth chapter we briefly review the previous research work and look forward to the future research work of spectrum detection algorithm.