Eigenvalue-Based Multiple Antenna Spectrum Sensing: Higher Order Moments

The problem of multiple antenna spectrum sensing
in cognitive radio (CR) networks is studied in this paper.
We propose two new invariant constant false-alarm rate
eigenvalue-based (EVB) detectors, using the higher order
moments of the sample covariance matrix eigenvalues, by exploiting the separating function estimation test framework. We find
closed-form expressions for the false-alarm and detection probabilities of the proposed detectors by providing moment-based
approximations of their statistical distributions. The accuracy
of the obtained closed-form expressions is validated by Monte
Carlo simulations. In addition, we compare the performance of
the proposed detectors with that of their two counterparts, i.e.,
John’s and the arithmetic to geometric mean (AGM) detectors,
in terms of the asymptotic relative efficiency. This comparison
enables us to demonstrate the superiority of our proposed
detectors over those detectors within the typical range of signalto-noise ratio in CR application. The comparative simulation
results also illustrate the superiority of the proposed detectors
over John’s and the AGM detectors as well as some other
state-of-the-art EVB algorithms given in the literature.