Matching-Based Channel Assignment for Cooperative Sensing in Full-Duplex Cognitive Radio Networks

In cognitive radio networks (CRNs), cooperative spectrum sensing (CSS) has been widely used to increase the sensing accuracy. Also, in recent years, full duplex techniques have been introduced to CRNs, which allows secondary users (SUs) to send data while sensing the spectrum. In this work, we investigate the problem of cooperative spectrum sensing in multi-channel full-duplex (FD) CRNs. Specifically, we study the problem of channel assignment for CSS in FD-CRNs, which is optimizing the assignment of SUs for cooperatively sensing different channels. And an optimization problem is formulated for maximizing the throughput of FD-CRNs. The optimization problem is an NP-hard non-linear integer programming problem. For large scale CRNs, getting an accurate solution of the original problem in a predictable time is difficult. Inspired by matching theory, we model the formulated problem as a matching game and propose an efficient algorithm for obtaining the solution. We analyze the complexity of the algorithm and prove that a stable matching can be reached. Numerical results show that the proposed matching algorithm is more efficient with the increasing number of SUs when comparing with the other three algorithms.