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.

本文提出了一种新的基于核模糊c均值（KFCM）聚类的基于特征值的协作频谱感知（CSS）技术。通过在二维/三维空间中执行聚类，将从测得的特征值得出的测试向量分为通道可用和不可用类。这与现有的基于特征值的频谱感测技术相反，在现有的基于特征值的频谱感测技术中，决策是基于一维的单个测试统计量进行的。尽管在这些技术中使用了多个特征值，但最终会计算出一个测试统计量并将其用于频谱感测。结果表明，提出的在多维空间中使用KFCM聚类的CSS可以提高检测性能。三种不同的频谱感应技术，提出并研究了利用信号协方差矩阵特征值的不同组合的方法。然后将提出的算法与文献中用于频谱感测的一些最新技术进行比较。给出了广泛的仿真结果，突出了所提出算法在检测性能上的改进。