Robust Spectrum Sensing Via Probability Measure Transform

Abstract

In this paper, we develop a new robust spectrum sensing method for MIMO cognitive radios in the presence of heavy-tailed noise. The proposed sensing technique, called measure-transformed covariance test (MTCT), operates by applying a transform to the probability measure of the data. The considered probability measure transform is structured by a non-negative function, called MT-function, that weights the data points. We show that proper selection of the MT-function, under the class of zero-centered spherically contoured Gaussian functions, can lead to significant mitigation of heavy-tailed noise effects on the sensing performance. Simulation studies illustrate the advantages of the proposed MTCT comparing to other robust MIMO and SIMO spectrum sensing techniques.