Clustering algorithms for Cognitive Radio networks: A survey

Abstract

Cognitive Radio (CR) networks enable unlicensed or Secondary Users (SUs) to sense for and operate in the underutilized spectrum (or white spaces) owned by licensed or Primary Users (PUs) without causing unacceptable interference to the PUs activities. Clustering, which is a topology management mechanism, organizes nodes into logical groups in order to provide network-wide performance enhancement. Clustering aims to achieve network scalability and stability, as well as to support cooperative tasks, such as channel sensing and channel access, which are essential to CR operations. While clustering has been well investigated in traditional networks such as mobile ad hoc networks, similar investigations in CR networks remain in the infancy stage. New clustering algorithms must be designed to address new challenges associated with the intrinsic characteristics of CR, namely the dynamicity of channel availability that changes with time and location. This article reviews clustering algorithms, and they are characterized by clustering objectives, metrics and the number of hops in each cluster. We also present complexity analysis, performance enhancements achieved by the clustering algorithms, as well as open issues, in order to establish a foundation for further research and to spark new research interests in this area. © 2014 Elsevier Ltd.