Non cooperative primary users-localization in cognitive radio networks

Abstract

Detection of spectrum holes in Cognitive Radio Networks (CRNs) is possible using theory of compressive sensing (CS). At the same time, these unlicensed Cognitive secondary users (SUs) must reliably detect the presence of licensed users or owners of spectrum holes to avoid interference. Precise localization of the transmitting nodes is a complicated but important problem for operation of Cognitive Radio Networks. To ensure the effectiveness of spectrum sharing, it is desirable to locate primary users, secondary users, and unauthorized users in a non-interactive fashion based on limited measurement data at receivers. Localization of non-cooperative objects refers to the process of locating an object that is not intentionally participating in the localization process but still interferes with the radio spectrum. In this paper, we have proposed a hardware scheme to perform precise localization of non-cooperative primary users based on the measurement of Received Signal Strength Indicator (RSSI) at the receiver. Among all non cooperative localization algorithms, we have chosen the Weighted Centroid Localization (WCL) scheme. To improve the localization accuracy, a classical propagation model with correction factor has been introduced. A modified WCL scheme has been used for precise calculation of Unknown PUs' Coordinates. The main challenge in RSSI based localization is its high sensitivity to the changes possible in radio frequency (RF) environment. The nature of RSSI measurements limits the accuracy in the estimation. To improve the localization accuracy, a classical RF propagation model namely log-normal shadowing is used. Usually to model Cognitive Radio (CR) the shadowing effects cannot be neglected. If the shadowing effect is neglected, the Path Loss is simply a straight line and becomes unrealistic. To make our model more realistic, a correction factor by way of a standard deviation-σ has been added to the classical model. Experiment has been performed to simulate different environmental scenarios by keeping all the node positions fixed. Calculation of unknown PUs' distance has been plotted with and without the correction factor for comparison.