Efficient channel assignment for cooperative sensing based on convex bipartite matching

In this paper, cooperative sensing for multi-channel cognitive radio networks (CRNs) is studied, whereby the secondary users (SUs) cooperate with each other to sense the multiple channels owned by the primary users (PUs). The objective is to better protect the primary system while satisfying the SUs' requirement on the expected access time. A general scenario is considered, where the channels present different usage characteristics and the detection performance of individual SUs varies due to the channel conditions between the PUs and SUs. With the dynamics in the channel usage characteristics and the detection capacities, each SU chooses one channel for sensing to minimize the interference to the PUs. The problem is formulated as a nonlinear integer programming problem which is NP-complete in general. To find the solution efficiently, the original problem is transformed into a variant of convex bipartite matching problem by constructing a complete bipartite graph and defining proper weight vectors. Based on the problem transformation, a channel assignment algorithm is proposed for computing in polynomial time the solution in terms of the number of SUs, the number of channels, and the maximum value of weights. Simulation results are presented to validate the performance of the proposed algorithm.