Abstract
In this paper, a novel method based on data fusion scheme and genetic algorithm is proposed to improve the cooperative spectrum sensing (CSS) performance. Firstly, to reduce the interference of abnormal secondary users (SUs), a geodesic projection method based on information geometric framework is developed to fuse signal received by all SUs, which maps multiple SUs to a representative point on the manifold. Subsequently, the sample set is calculated by signal fusion method, and divided into training set and test set. Finally, the improved genetic algorithm based on Kullback-Leibler divergence is designed to cluster the fused signal on the manifold space, which can obtain a classifier to judge the status of primary user. Simulation results illustrate the developed CSS scheme effectiveness when compared with other CSS methods.
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