Riemannian Distance-Based Fast K-Medoids Clustering Algorithm for Cooperative Spectrum Sensing

Abstract

Spectrum sensing is a fundamental component in a cognitive radio network to utilize the frequency bands effectively. In this article a fast Riemannian distance-based K-medoids (FRDK)-based cooperative spectrum sensing (CSS) method is developed to identify the state of primary user (PU). In particular, two CSS scenarios are considered, one is secondary users (SUs) with a single antenna and the other is SUs with multiple antennas. In the multiantenna case, a Riemannian mean-based data fusion method is proposed to fuse sensing data from SUs with multiple antennas. To implement CSS, we propose a FRDK-based framework, where SUs collect sensing data, preprocess, and upload these data to an appointed fusion center (FC). Then, the FC transforms these data as samples on a manifold and uses the FRDK algorithm to train a classifier for identifying the state of PU. Furthermore, the convergence and the complexity analysis of training process are presented. Finally, the effectiveness of the proposed FRDK-based CSS method is verified under different conditions.