Whitespace measurement and virtual backbone construction for Cognitive Radio Networks: From the social perspective

Abstract

The existing works on analyzing/utilizing spectrum whitespace in Cognitive Radio Networks (CRNs) are either empirical studies lacking of theoretical guarantee, or local primary network information based inducing inaccurate analysis and estimation, or overlooking the spectrum whitespace details. Therefore, we propose to systematically analyze the spectrum whitespace in CRNs from a social network perspective. Our main contributions include four parts. First, we propose a novel metric named centrality score to measure the active weights of Primary Users (PUs) by considering each PU's topological importance in the primary network and the global primary network running and traffic information. Subsequently, based on the centrality scores of PUs, we derive the whitespace in CRNs under different social patterns of primary activities. Since we consider both the primary network topological structure and the global network running and traffic information, our whitespace analysis is more accurate compared with the existing works. Third, according to our whitespace analysis, we design a Virtual Backbone (VB) construction algorithm, which aims to improve the spectrum utilization efficiency in CRNs. Finally, we conduct extensive simulations to validate our whitespace analysis and the VB construction algorithm. The simulation results demonstrate that our social attributes based whitespace analysis can accurately characterize the whitespace in CRNs and the VB construction algorithm significantly improves the performance of the existing VB-based CRN protocols.