Cooperative weighted centroid localization for cognitive radio networks

Abstract

Localization of primary users (PUs) is a feature that can be very helpful for all the functional tasks of a cognitive radio (CR) network. Among the possible algorithms, weighted centroid localization (WCL) appears as the best candidate being a simple and robust range free localization technique that does not require any knowledge on the PU signal and on the radio environment parameters. We analyze the adoption of different weighting strategies considering in particular the dependence of the root mean square error (RMSE) on the position of the PU within the area considered. Numerical results show that weighting strategies that emphasize the difference between higher and lower weights are more robust to situations in which the PU moves toward the side of the area. Two secondary user (SU) selection strategies are also proposed to alleviate the border and noisy measurements effects in harsh propagation environments.