Eigenvalue-based cooperative spectrum sensing using kernel fuzzy c-means clustering

Abstract

In this paper, novel techniques of eigenvalue-based cooperative spectrum sensing (CSS) using Kernel fuzzy c-means (KFCM) clustering are proposed. Test vectors derived from measured eigenvalues are categorized into channel available and unavailable class by performing clustering in two/three dimensional space. This is in contrast to existing eigenvalue-based spectrum sensing techniques, where sensing decision is made on the basis of a single test statistic in one dimension. Though multiple eigenvalues are used in those techniques, finally a single test statistic is computed and used for spectrum sensing. It is shown that proposed CSS using KFCM clustering in multidimensional space provides improvement in detection performance. Three different spectrum sensing techniques, utilizing different combinations of eigenvalues of the signal covariance matrix, are proposed and studied. Proposed algorithm is then compared to some of the recent techniques for spectrum sensing in literature. Extensive simulation results are given which highlight improvement in detection performance offered by the proposed algorithm.