Positioning and Location-based Beamforming for High Speed Trains in 5G NR Networks

Abstract

High-accuracy positioning enables emerging of new vertical markets for the forthcoming fifth generation (5G) mobile networks. In this paper, we study network-side positioning and related location-based beamforming for high-speed train (HST) scenario in 5G New Radio (NR) networks. To avoid tight synchronization requirements between the train and the network, we consider Time-Difference-Of-Arrival (TDOA) measurements based on 5G NR uplink sounding reference signals. Moreover, in order to facilitate the location-based beamforming, we introduce methods for removing measurement outliers and for selecting the optimal reference measurement for the time-difference evaluations. The train is tracked by utilizing Extended Kalman Filter (EKF), which is capable to track and predict the train position for the location-based beamformer in real-time. Based on results obtained from extensive 5G NR compliant simulations, the proposed approach is able to achieve a sub-meter positioning accuracy with 90% availability, which is sufficient for many mission-critical positioning applications in the considered HST scenario.