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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.

A joint design of the mechanical and the electrical tilts is proposed for millimeter-wave beamspace multiple-input multiple-output systems. Based on the correlation property of the array steering vector, the asymptotic sum rate is derived. In order to maximize the sum rate, the mechanical tilt is designed to maximize the expectations of the antenna gains. Moreover, the electrical tilt is dynamically adjusted to optimize the instantaneous antenna gains. As a result, the proposed joint design of two tilts can achieve higher sum rate than the designs of one tilt, which is confirmed by the simulation results.

The sensor array calibration methods tailored to uniform rectangular array (URA) in the presence of mutual coupling and sensor gain-and-phase errors were addressed. First, the mutual coupling model of the URA was studied, and then a set of steering vectors corresponding to distinct locations were numerically computed with the help of several time-disjoint auxiliary sources with known directions. Then, the optimization modeling with respect to the array error matrix (defined by the product of mutual coupling matrix and sensor gain-and-phase errors matrix) was constructed. Two preferable algorithms (called algorithm I and algorithm II) were developed to minimize the cost function. In algorithm I, the array error matrix was regarded as a whole parameter to be estimated, and the exact solution was available. Compared to some existing algorithms with the similar computation framework, algorithm I can make full use of the potentially linear characteristics of URA’s error matrix, thus, the calibration precision was obviously enhanced. In algorithm II, the array error matrix was decomposed into two matrix parameters to be optimized. Compared to algorithm I, it can further decrease the number of unknowns and, thereby, yield better estimation accuracy. However, algorithm II was incapable of producing the closed-form solution and the iteration operation was unavoidable. Simulation results validate the excellent performances of the two novel algorithms compared to some existing calibration algorithms.