Research On Spectrum Sensing Algorithm Based On Matrix Theory

With the development of new wireless communication technologies and the rapid popularization of wireless devices in recent years,all walks of life depend more and more on spectrum resources,and spectrum resources are increasingly scarce in the rapid development of wireless communication.In order to solve the problem of resource shortage and unreasonable use,cognitive radio(Cognitive Radio,CR)technology came into being.Cognitive radio includes spectrum sensing,spectrum analysis and spectrum decision-making,spectrum sharing and spectrum mobility management.Spectrum sensing is the prerequisite of cognitive radio and is the most important and indispensable part of cognitive radio network.Therefore,the research on spectrum sensing has become one of the hottest technologies in cognitive radio.The main content of this paper is the spectrum sensing algorithm.First of all,this paper introduces the research and development of spectrum sensing at home and abroad,and expounds the basic concepts and core theory of cognitive radio,as well as the structure and application scenarios of cognitive radio platform.Secondly,the theory of stochastic matrix is expounded which is applied to spectrum sensing algorithm,and the overall structure of the algorithm based on random matrix theory is divided.The existing single eigenvalue algorithm and double eigenvalue algorithm are introduced in detail.Through analysis and comparison,explain their respective advantages and disadvantages,and propose a new spectrum sensing algorithm based on eigenvalue bounds.The new algorithm makes full use of all eigenvalues of the matrix,contains more characteristic information.Through simulated and compared to the algorithm with different sampling number,the number of cooperative users,SNR parameters,the theoretical derivation and simulation results show that the performance of the new perceptual algorithm is obviously superior to other algorithms which do not use all the eigenvalues,especially in the noise uncertainty,the primary user signal characteristics,channel and noise power has a good perception of performance.Finally,a new spectrum sensing algorithm based on IQ decomposition is proposed on the basis of matrix theory.The theoretical basis of IQ decomposition of random matrix is expounded,and the principle of the algorithm is analyzed to show that the new algorithm decision threshold.As the number of cooperative users increases,the perceived performance can be improved.By IQ decomposition of the matrix,the number of cooperative users is logically increased,the signal correlation is increased,and the performance of the perceptual algorithm can be theoretically improved.By analyzing the simulation results under different signal-to-noise ratios and different sampling numbers,the algorithm improves the detection performance and verifies the effectiveness of the algorithm.The paper summarizes the characteristics of various algorithms and makes a comprehensive simulation and comparison to explore the future development direction.