Local Channel Assignments in Cognitive Radio Networks

Cognitive radio networks (CRNs) promise to enable the next generation of communication networks. The channel assignment (CA) problem is one of the most important issues in CRNs. In this paper, our goal is to design highly efficient and localized protocols for CA. In addition, we want to maximize node connectivity after CA, which is important for packet delivery. To this end, we design two basic algorithms and an advanced algorithm framework. Within this framework, we can change the edge priority in CA to meet different requirements. Simulation results show that the proposed framework is fast (two rounds of communication among nodes, regardless of network size) and outperforms an existing method.