Accurate and reliable positioning solution is an important requirement for many applications, for instance, emergency services and vehicular-related use cases. Positioning using cellular signals has emerged as a promising solution in Global Navigation Satellite System (GNSS) challenging environments, such as deep urban canyons. However, harsh working conditions of urban scenarios, such as with dense multipath and Non-Line of Sight (NLoS), remain as one of the key factors causing the detriment of the positioning estimation accuracy. This paper demonstrates that the use of joint Uplink Time Difference of Arrival (UTDoA) and Angle of Arrival (AoA) gives a significant improvement in the position accuracy thanks to the use of antenna arrays. The new advances of this technology enable more accurate user locations by exploiting angular domains of propagation channel in combination with time measurements. Moreover, it is shown that a better localization is achieved by combining the joined UTDoA and AoA with a base-station selective exclusion method that is able to detect and eliminate measurements affected by NLoS. The proposed approach has been tested through simulations based on a deep urban deployment map, which comes with an experimental data file of the user’s position. A sounding reference signal of 5G new radio operating in the centimeter-wave band is used. The obtained results add value to the use of advance antennas in 5G positioning. In addition, they contribute towards the fulfillment of high-accuracy positioning requirements in challenging environments when using cellular networks.

Smartphone positioning methods relying solely on global navigation satellite system (GNSS) are susceptible to significant errors in urban environments due to challenging observation conditions. To enhance position accuracy, this article introduces a novel fifth-generation (5G) observation model based on unit vectors and evaluates its performance using several user locations and precision observation data. The results demonstrate that the unit vector method, compared to the traditional angle of departure (AOD) approach, effectively reduces the nonlinearity of the observation model, leading to a substantial improvement in the 5G system’s positioning accuracy. Furthermore, we conduct experiments to investigate the impact of the geometric distribution of 5G base station (BS) height on positioning accuracy. The results reveal a significant influence, highlighting the importance of considering BS height in the positioning process. In the urban environment, introducing 5G BS angle observations as an aid to GNSS significantly enhances the reliability of smartphone positioning, achieving positioning errors of less than one meter. Notably, this approach outperforms the introduction of 5G distance observations, providing valuable insights for the development of seamless indoor and outdoor positioning systems in the future.

The advent of 5G technology has significantly advanced the capability of centimeter-level positioning, with Angle of Arrival (AoA) estimation emerging as a key technique to achieve this level of accuracy. This paper presents a software receiver based on Universal Software Radio Peripheral (USRP) devices, designed for precise AoA estimation using 5G New Radio (NR) signals, specially in challenging environments. The proposed receiver addresses critical issues such as hardware and antenna calibration, synchronization, and signal processing, enabling accurate angle estimation in both outdoor and indoor environments. The study explores the impact of hardware imperfections, antenna errors and environmental factors, including multipath propagation, on angle estimation accuracy. Our approach combines AoA estimation with calibration techniques, demonstrating the feasibility of delivering reliable, high-precision location-based services with 5G signals. Through a combination of simulation and real-world experiments, we assess the performance of the system in scenarios with and without multipath interference, including the influence of reflectors on AoA measurements. The results provide a valuable foundation for advancing the understanding and implementation of precise AoA estimation and calibration using 5G NR signals and USRP devices.

The increasing demand for global connectivity has accelerated the integration of non-terrestrial networks (NTNs), particularly low Earth orbit (LEO) satellite constellations, into next-generation position navigation and time (PNT) systems. While LEO-based PNT offers low-latency and high-accuracy potential, challenges such as high path loss and limited ground-level signal diversity remain. Reconfigurable intelligent surfaces (RISs) have emerged as a cost-effective solution to enhance localization performance by providing controllable reflections with minimal infrastructure. Building on prior work in single-RIS NTN scenarios, this paper investigates RIS-aided localization in a single-LEO PNT setting with multiple RISs. We introduce a detailed signal model and multi-stage processing framework that estimates both the satellite and RIS-assisted paths, enabling accurate receiver localization. Simulations assess the trade-offs in coverage and accuracy, providing insights into the feasibility and optimization of RIS-assisted NTN PNT solutions as a complementary alternative to global navigation satellite system (GNSS).