A Distance-weighed Algorithm Based on Maximum-minimum Eigenvalues for Cooperative Spectrum Sensing

In cognitive radio, spectrum sensing is a fairly crucial task for spectrum sharing. In the literature, there are several classical methods in detecting spectrum holes such as the matched filter, the cyclostationary and the energy detection. Recently due to the advances in random matrix theory (RMT), many schemes relied on the eigenvalues of the covariance matrix of the received signal are introduced. However, these models based on the RMT are ideal without considering different positions of cognitive radios. As we know, different distances of cognitive users to the primary user can cause various signal-to-noise ratios (SNRs) of secondary users and signal-to-noise ratios greatly affect the reliabilities of the detection result. Thus in this paper, we propose an improved cooperative spectrum sensing method based on the maximum and the minimum eigenvalues of the signal's covariance matrix considering different distances of cognitive users to the primary user. We do some simulations of this algorithm, and its results verify a better performance of this method.