Abstract
Positioning is a vital capability in different radio systems for extracting situational awareness, with path loss (PL)-based positioning playing a crucial role due to its widespread use in wireless standards. In this work, we propose an idle-mode PL-based positioning approach without line-of-sight (LOS) detection that is suitable for millimeter-wave (mmWave) urban networks with directive beams in the base stations (BSs). With the beam gain significantly affecting the observed PLs, we divide the data and models relative to the beam direction rather than to LOS and non-line-of-sight (NLOS) BSs. Different approaches for obtaining the PL model parameter estimates are proposed, including model fitting taking into account the influence of the noise limit and direct optimization based on positioning accuracy using the training data set. In addition to the PLs, azimuth- and elevation-of-departure (AoD and EoD) are estimated, modeled, and used in the positioning calculations, building on maximum likelihood (ML) estimation. Comprehensive numerical results and performance assessments are provided, harnessing ray-tracing (RT) data in a 28GHz urban microcellular environment. The demonstrated median positioning error is under 20m reflecting an improvement of 50%-70% compared to the classical CellID method. Additionally, the results show that the proposed method outperforms machine learning based reference solutions. Finally, the methods and the resulting positioning performance are shown to be robust against variations in the underlying technical parameters, such as the BS transmit beam-width, as well as errors or imperfections in the assumed BS locations and BS orientation information.
NETL
NSTL
IEEE