Riemannian Mean Shift-Based Data Fusion Scheme for Multi-Antenna Cooperative Spectrum Sensing
Yongwei Zhang, Shunchao Zhang, Yonghua Wang, Jiawei Zhuang, Pin Wan · IEEE Transactions on Cognitive Communications and Networking · 2021
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.