Cluster-CMSS: A Cluster-Based Coordinated Spectrum Sensing in Geographically Dispersed Mobile Cognitive Radio Networks

A coordinated multiband spectrum sensing (CMSS) policy for mobile and geographically dispersed cognitive radio networks (CRNs), referred to as cluster-CMSS, is proposed. The goal is to detect the spectrum holes and to assign each secondary user (SU) a sensing channel with the maximum probability of being empty. In geographically dispersed CRNs, channel availability varies over the space, and this makes the sensing outcomes and sensing assignments location dependent. However, if the SUs are not equipped with location-finding technologies, fusing the sensing outcomes to find the optimal spectrum sensing assignments for the next sensing time becomes challenging for the base station (BS). To tackle this problem, we introduce a metric solely based on the sensing outcomes of SUs. Using this metric, along with a low-complexity clustering algorithm, enables the BS to efficiently divide the network into clusters. Further, we present an adaptive learning algorithm, to learn the dynamic behavior of channel occupancy in the primary network. The proposed learning algorithm considers SUs mobility model to determine the optimal learning window. To determine the sensing assignments, the BS performs a graphtheory-based coordinated multiband spectrum sensing within each cluster. Specifically, a weighted bipartite matching is employed. We have shown that cluster-CMSS significantly increases the spectrum opportunity discovery ratio for SUs at the cost of a slight increase in the energy consumption associated with spectrum sensing.