Cluster Based Multidimensional Scaling for Irregular Cognitive Radio Networks Localization

In cognitive radio networks (CRNs), localization of primary users (PUs) and secondary users (SUs) can enable several key capabilities such as location aware routing and power control mechanisms for SUs. Therefore, SUs in a network must accurately locate PUs in order to efficiently use spectrum holes without interfering to the PUs. Accurate localization of PUs in CRN is an important but challenging task due to the unique constraint of CRNs, i.e., the non cooperative nature of PUs making the localization algorithm rely solely on sensing results. In this paper we propose cluster based CRN localization using multidimensional scaling (MDS) that improves accuracy, especially for irregular CRNs. Using the traditional MDS approach leads to low localization accuracy and higher computational complexity. Based on this fact, this paper proposes a novel cluster based multidimensional scaling algorithm for CRN localization (CB-MDS). Furthermore Cramér-Rao lower bound (CRLB) is derived to analyze the performance of the proposed algorithm. Moreover, extensive simulations are performed to confirm that the proposed CB-MDS algorithm is robust to noise and performs better than existing algorithms in attaining the CRLB.