Fast Multichannel Topology Discovery in Cognitive Radio Networks

Abstract: In Cognitive Radio Networks (CRNs), secondary users (SUs) must efficiently discover each other across multiple communication channels while avoiding interference from primary users (PUs). Traditional multichannel rendezvous algorithms primarily focus on enabling pairs of SUs to find common channels without explicitly considering the underlying network topology. In this paper, we extend the rendezvous framework to explicitly incorporate network topology, introducing the \emph{multichannel topology discovery problem}. We propose a novel \emph{pseudo-random sweep algorithm with forward replacement}, designed to minimize correlation between consecutive unsuccessful rendezvous attempts, thereby significantly reducing the expected time-to-discovery (ETTD). Additionally, we introduce a \emph{threshold-based stick-together strategy} that dynamically synchronizes user hopping sequences based on partially known information, further enhancing discovery efficiency. Extensive simulation results validate our theoretical analysis, demonstrating that the proposed algorithms substantially outperform conventional (sequential) sweep methods.