A Semi Range-Based Iterative Localization Algorithm for Cognitive Radio Networks

In cognitive radio networks (CRNs), knowledge of the position of the primary users (PUs) is important as it can be used to avoid harmful interference to the primary network while at the same time be exploited to improve the spectrum utilization. In this paper, a semi range-based localization algorithm is proposed for secondary users (SUs) in CRNs to estimate the positions of the PUs. The basic idea of the proposed algorithm is to take advantage of the estimated detection probabilities, which can be obtained from the binary detection indicators of the SUs, to estimate the distances between themselves and the PUs. Moreover, the accuracy of the proposed localization algorithm is further improved by introducing both a weighted least-squares method and an iterative procedure. The Cramer-Rao lower bound (CRLB) of the mean-square error (MSE) of the proposed localization estimator is also derived. A scenario with malicious users (MUs) is further considered, where the proposed method is modified to detect MUs. To illustrate the benefits of localization, we design a location-aware medium access control (MAC) protocol and show that significant throughput gains can be realized over conventional MAC protocols.