Research On Spectrum Sensing Technology Based On Generalized Likelihood Ratio Algorithm In Low SNR Environment

In recent years,with the rapid development of wireless communication,the allocation of unlimited spectrum resources has become increasingly scarce.In order to realize the reuse of non-renewable spectrum resources and alleviate the contradiction between the increasing shortage of spectrum resources and the growing demand for wireless spectrum,cognitive radio,a new communication concept,has attracted extensive attention of researchers in the field of communication.Therefore,the development of new technologies to provide more available spectrum resources for new services has become a new research direction.Spectrum sensing technology is the primary and core technology to realize the application of cognitive radio and construct cognitive radio system.It is also an important prerequisite to protect authorized users from harmful interference and improve the utilization rate of spectrum resources of radio system itself.According to the different detection methods of sensing users,the existing spectrum sensing technologies are divided into the following three categories: spectrum detection based on transmitter,spectrum detection based on authorized user receiver and spectrum detection based on multi-sensing user cooperation.Since most of the spectrum detection algorithms based on user receivers are in passive receiving state,it is difficult to determine the specific location of users,so spectrum detection algorithms based on authorized user transmitter and spectrum detection algorithms based on multi-sensor user cooperation have become the mainstream direction of current spectrum sensing technology research.Spectrum sensing algorithms have achieved remarkable results for different detection environments.For example,spectrum detection algorithms based on authorized user transmitter mainly include matched filter detection,energy detection,periodic stationary detection,wavelet detection and eigenvalue correlation algorithms.Although these algorithms realize spectrum sensing technology to a certain extent,in the process of detection,there are different degrees of information such as system environmental noise and authorized user signal characteristics.Dependence.In this paper,the problems of traditional sensing algorithms are studied,and the generalized likelihood ratio(GLR)algorithm is introduced to solve the spectrum sensing in low noise environment without the information of authorized users.The simulation design is carried out to verify the conclusion.The main contents of this paper are as follows:Firstly,the paper introduces the research background and significance of the generalized likelihood ratio algorithm,and expounds the research status at home and abroad based on the generalized likelihood ratio algorithm and analyzes the generalized likelihood ratio algorithm flow.Then this paper analyzes the use of spectrum resources at home and abroad and the management methods of current spectrum,introduces cognitive radio technology,and conducts in-depth research on the key technology spectrum sensing technology of cognitive radio.The three algorithms of matched filter algorithm,energy detection algorithm and cyclostationary feature detection algorithm are analyzed,and the applicable environment and its advantages and disadvantages of different algorithms are summarized.Then the generalized likelihood ratio algorithm is introduced to solve the spectrum sensing under the condition of unknown authorized user information in the low SNR environment.The generalized likelihood ratio algorithm is compared with the energy detection algorithm and the matched filter algorithm,and the simulation is carried out to effectively draw conclusions.In the wireless environment with low SNR,the generalized likelihood ratio algorithm has good perceptual performance.Finally,the paper studies the problems existing in traditional spectrum sensing algorithms.According to different constraints,the improvement of the algorithm and its prospects are summarized.