A cooperative spectrum sensing method based on semi-supervised clustering with variational mode decomposition and information geometry

Abstract

In practice, obtaining a large number of sample labels for the supervised learning-based cooperative spectrum sensing (CSS) method is challenging. Additionally, the CSS method based on information geometry (IG) and clustering algorithm exhibited low detection performance in low signal-to-noise ratio (SNR) environment. This paper proposes a cooperative spectrum sensing method based on variational mode decomposition and IG with semi-supervised clustering. First, the secondary users (SUs) reconstruct the received signals using variational mode decomposition. Next, the fusion center combines all the reconstructed signals into a single matrix and calculates its corresponding covariance matrix. Finally, a binary classifier is obtained via training offline on the matrix manifold space using a semi-supervised clustering algorithm based on IG. The simulation results show that increasing the number of SUs and sampling points can improve the CSS performance of the proposed algorithm. Furthermore, the proposed algorithm is compared with other CSS algorithms, demonstrating its superior spectrum sensing performance regardless of whether the SUs are in the same very low SNR environment.