Primary user localization in cognitive radio networks using sectorized antennas

Information about primary user (PU) location can enable several key capabilities in cognitive radio (CR) networks. In this paper we consider PU localization using received-signal-strength (RSS) and direction-of-arrival (DoA) estimates from sectorized antenna. Abstracting from practical antenna types, we define a sectorized antenna as an antenna that can be set to different operating modes, each of which resulting in a selectivity of those signals that arrive from within a certain, continuous range of angles, i.e. a sector. We propose a low complexity algorithm, the MaxE algorithm, that provides coarse RSS and DoA estimates, and derive the asymptotic bounds for its root mean square error (RMSE) as a function of the antenna parameters. We then propose a modified Stansfield algorithm with a novel RSS-based weighting scheme based on the Stansfield DoA fusion method, which obtains PU location estimates from measurements of the MaxE algorithm. The modified Stansfield algorithm improves the accuracy of the Stansfield algorithm with equal weights. Simulation results studying the impact of various system parameters, such as number of sectors, number of samples and signal-to-noise ratio, on the DoA/RSS estimation and localization accuracy are presented to provide design guidelines for localization systems based on sectorized antennas.