Modeling 5G-NR-Based Positioning Accuracy in Urban Environments

Modeling phenomena influencing radiocommunication networks, such as 5G-NR, is a key aspect during their development and further optimization. In this paper, one proposes a model, for three different scenario types in urban environments, for user localization accuracy. The model is supported by measurements of real 5G-NR downlink signals carried out in three different urban environments, corresponding to the typical user localization. The main goal of the paper is to present a model for estimating the relation between the environment type, i.e., its statistical parameters, and the estimated localization accuracy in outdoor, light-indoor and deep-indoor environments. Results show that the localization accuracy may degrade from 5 to 295 m when the localization process is performed, just by moving the terminal from outside a building to its inside. This can make the localization service invalid, not only from a user-related perspective but also as a method to adjust beamforming parameters in downlink.