Research On Spectrum Sensing Algorithm Based On Random Matrix Theory

Spectrum sensing has been widely used in many fields, and has aroused much attention among the society. Spectrum sensing refers to the cognitive users through a variety of signal detection and processing means to obtain the spectrum in a wireless network using information, which is the primary user signal is occupied by the band. If primary user does not occupied, cognitive users can use the frequency band, whereas it cannot use the band.There are many ways to detect the presence of primary user signals, and we used the random matrix theory to research the spectrum sensing method due to the scarcity of spectrum resources. We used sampling matrix to present the signal information, by which can we decide whether a primary user signal through the variation analysis of matrix and observation matrix characteristics. Based on the above ideas, the formation of a singular Gauss random matrix value based GSV(Gaussian Singular Value) algorithm, and study the detection probability and false alarm probability under different conditions and the number of antennas under different SNR. Then, according to the algorithm to improve the GSV algorithm, the formation of different algorithm based on GSV algorithm. At the same time, we simulate the performance of the detection algorithm and study the detection probability and false alarm probability. In fact, the algorithm can accurately judge whether the primary user signal, so as to make full use of spectrum resources.Then we study the communication scene which the primary user signal using multiple antennas, try to establish the contact between multiple antenna and single antenna in GSV algorithm. We make improvements in GSV algorithm, which can be applied to multiple antennas situation. At the same time, we simulate the performance of the detection algorithm and study the detection probability and false alarm probability. In addition, in the light of the contradiction of the detection probability and false alarm probability, we proposed a index called the credibility of detection, which can comprehensively evaluate detection performance and false alarm performance these two index of detection algorithm. By comparing those algorithms can we draw conclusion that the improvements of the GSV algorithm are successful.