Title :
Non-Asymptotic Analysis of Scaled Largest Eigenvalue Based Spectrum Sensing
Author_Institution :
Dept. of Commun. & Networking, Aalto Univ., Aalto, Finland
Abstract :
In this paper, we analyze the non-asymptotic performance of scaled largest eigenvalue based detection, which is an optimal detector in the presence of a single primary user. Exact distributions of the test statistics have been derived, which lead to finite-dimensional characterizations of the false alarm probability. These results are obtained by taking advantage of the properties of the Mellin transform for products of independent random variables. Simulations are provided to verify the derived results, and to compare with the asymptotic result in literature.
Keywords :
antenna arrays; cognitive radio; eigenvalues and eigenfunctions; radio spectrum management; signal detection; statistical distributions; transforms; Mellin transform; cognitive radio networks; dynamic spectrum access; exact test statistic distributions; false alarm probability; finite-dimensional characterizations; independent random variables; multiantenna spectrum sensing; nonasymptotic analysis; scaled largest eigenvalue based detection; scaled largest eigenvalue based spectrum sensing; single primary user; Approximation methods; Covariance matrices; Detectors; Eigenvalues and eigenfunctions; Noise; Transforms;
Conference_Titel :
Vehicular Technology Conference (VTC Spring), 2013 IEEE 77th
Conference_Location :
Dresden
DOI :
10.1109/VTCSpring.2013.6692651
