Logarithmic similarity measure based cooperative spectrum sensing under impulsive noise

Spectrum sensing is one of the key functionalities in cognitive radios which enables opportunistic spectrum access. In this paper, a cooperative spectrum sensing (CSS) algorithm is developed to alleviate the problems of hidden terminals under impulsive noise environments. Firstly, the logarithmic similarity measure detector (LSMD) is constructed to solve the problem of outliers caused by impulsive noise. On the one hand, LSMD contains no free parameters, which is easy to implement. On the other hand, logarithmic similarity measure (LSM) converts logarithmic operations into multiplication operations, and then the computational cost can be greatly reduced. Moreover, original data fusion strategy is designed to reduce the amount of computation of CSS, while the accuracy of CSS is noticeably improved compared with the OR rule CSS. Besides, the solution of the unknown parameter of LSMD is directly given by theoretical analysis, and then the CSS exhibits higher efficiency. Simulation results show that the proposed method achieves much higher detection probability than the existing techniques under various scenarios. © 2020, Electromagnetics Academy. All rights reserved.