The 5G network is considered as the essential underpinning infrastructure of manned and unmanned autonomous machines, such as drones and vehicles. Besides aiming to achieve reliable and low-latency wireless connectivity, positioning is another function provided by the 5G network to support the autonomous machines as the coexistence with the Global Navigation Satellite System (GNSS) is typically supported on smart 5G devices. This paper is a pilot study of using 5G uplink physical layer channel sounding reference signals (SRSs) for 3D user equipment (UE) positioning. The 3D positioning capability is backed by the uniform rectangular array (URA) on the base station and by the multiple subcarrier nature of the SRS. In this work, the subspace-based joint angle-time estimation and statistics-based expectation-maximization (EM) algorithms are investigated with the 3D signal manifold to prove the feasibility of using SRSs for 3D positioning. The positioning performance of both algorithms is evaluated by estimation of the root mean squared error (RMSE) versus the varying signal-to-noise-ratio (SNR), the bandwidth, the antenna array configuration, and multipath scenarios. The simulation results show that the uplink SRS works well for 3D UE positioning with a single base station, by providing a flexible resolution and accuracy for diverse application scenarios with the support of the phased array and signal estimation algorithms at the base station.

This work showcases the design, implementation, and verification of a vector antenna (VA) capable of direction finding. By adopting the tapered slot (also known as Vivaldi) element as the constitutive VA element and a four-season layout, the VA in this work can identify the radio source in a wide spectrum range in the three-dimensional space. Practical issues, such as steering vector calibration, exploitation of polarization, and angle estimation with partial electromagnetic measurements, are addressed to ensure the functionality of the proposed VA in practice. A four-channel phase-locked software-defined radio platform and quasi-real-time angle estimation algorithms are designed and implemented to verify the direction-finding (DF) capability of the VA for directly propagated, reflected, and diffracted radio signals, respectively. The results of the experiment prove the effectiveness of the theory and our practical VA measures for 3D direction finding under various conditions and potentially inspire localization, mapping, and navigation in short-range scenarios.

This paper investigates the optimization of reconfigurable intelligent surfaces (RIS) for near-field user equipment (UE) localization in the presence of channel spatial non-stationarity (SNS) across elements and the involved deliberate model misspecification. Traditional far-field localization techniques struggle to maintain accuracy in near-field scenarios characterized by spherical wavefronts (SWFs) while algorithms relying on a full-fledged near-field model with SNS and SWF necessitate long transmission durations to estimate a large number of parameters. To address these challenges, we propose a novel low-complexity localization algorithm based on a misspecified model that reduces the number of unknown channel parameters with limited impact on accuracy. A misspecified Cramér-Rao bound (MCRB) analysis is also performed to evaluate theoretical performance degradation due to the involved model misspecification. Additionally, we propose a codebook design that leverages coarse UE location information to enhance localization accuracy. Numerical simulations validate the effectiveness of the proposed estimation method and highlight the advantages of using the proposed codebook.