Cooperative Spectrum Sensing Algorithm Based on Eigenvalue Fusion

Abstract

A novel algorithm is introduced to improve collaborative spectrum sensing under low cognitive capabilities and insufficient signal-to-noise ratio. The algorithm is based on the difference of random matrix eigenvalues and uses the theory of random eigenvalues and the extreme distribution of the minimum eigenvalue. It makes use of the average, both arithmetic and geometric, as well as the minimum and maximum values of eigenvalues as the detection metric. It calculates the fusion power parameter through local energy spectrum sensing. Simulation results demonstrate that the algorithm outperforms the DMM algorithm and the NMME algorithm under users with low cognitive capabilities and Insufficient signal-to-noise ratio, making it more suitable for low signal-to-noise ratio environments.