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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.