Jointly locating the primary and secondary users in cognitive radio networks

In this paper a local geometry alignment algorithm is presented for locating the primary users (PUs) and Secondary users (SUs) in cognitive radio network. Based on the estimated distance between PUs and SUs for the neighbors within certain communication range, the relative configuration of all the users in the network is obtained initially and is refined finally to get the global position of every user in the network. The localization performance of the proposed approach is compared to multidimensional scaling and principal component analysis. Furthermore the lower bound on error i.e., the Cramer Rao lower bound (CRLB) is also derived to check the performance of the proposed algorithm.