Beam-Switching-Based Joint DOA and TOA Acquisition and Tracking Using Commercial 5G NR Signals for Outdoor Positioning

Abstract

The proliferation of 5G wireless devices creates a pressing need for high-precision positioning services. This paper presents an integrated acquisition–and–tracking framework that leverages physical broadcast channel (PBCH) transmissions from commercial 5G New Radio (NR) base stations (BSs). To select the strongest downlink beam without prior knowledge of the BS radiation pattern, we use an adaptive beam-switching strategy based on reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-noise ratio (SNR). For coarse acquisition, direction-of-arrival (DOA) and time-of-arrival (TOA) estimates are produced by a two-stage procedure that combines three-dimensional sparse Bayesian learning (SBL) with gradient-ascent off-grid refinement. A closed-loop tracker then continuously refines these estimates through azimuth-locked loop (ALL), elevation-locked loop (ELL), and delay-locked loop (DLL) modules. We further derive information-theoretic limits that bound acquisition and tracking and elucidate the key factors shaping these limits. The complete framework is implemented on a software-defined radio (SDR) 5G receiver and validated in outdoor field trials. Results indicate robust performance, with an average TOA root-mean-square error (RMSE) of 3.58 m, an azimuth RMSE of 5.77°, and an elevation RMSE of 2.74°, while requiring only 2.64 switching events per minute.