Abstract
In this article, the multi-antenna cooperative spectrum sensing problem in cognitive radio networks is investigated over Riemannian manifold. At the beginning, a signal matrix is constructed by using the sensing signals from secondary users (SUs) and the corresponding covariance matrix is calculated. Subsequently, the covariance matrices are transmitted to the fusion center and mapped to points on the manifold. In order to reduce the impact of aberrant SUs, a data fusion scheme based on Riemannian mean shift algorithm is developed. After data fusion, the representative points are obtained to train a classifier. In order to realize clustering directly over Riemannian manifold, a novel Riemannian distance based particle swarm optimization (RDPSO) algorithm, is proposed to train a classifier, which is employed to determine the state of primary user (PU). Finally, in simulation part, the validity of the proposed scheme is verified under different scenarios.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Cognitive Communications and Networking
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.
