Performance analysis of weighted centroid algorithm for primary user localization in cognitive radio networks

Abstract

Information about primary user (PU) location is crucial in enabling several key capabilities in cognitive radio networks, including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. The weighted centroid localization (WCL) scheme uses only the received signal strength information, which makes it simple and robust to variations in the propagation environment. In this paper we present the first theoretical framework for WCL performance analysis in terms of its localization error distribution parameterized by node density, shadowing variance and correlation distance. Using this analysis, we quantify the robustness of WCL to various physical conditions and conclude that the performance gain by increasing node number in uncorrelated shadowing environment tends to saturate at large node density, and including more nodes in correlated shadowing environments can be harmful to the localization accuracy.