Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation

Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the radio-based sensing and environment mapping prospect with specific emphasis on the user equipment (UE) side. We first describe an efficient l1-regularized least-squares (LS) approach to obtain sparse range--angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. We provide extensive numerical indoor mapping results at the 28~GHz band deploying OFDM-based 5G NR uplink waveform with 400~MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks

Power Allocation and Parameter Estimation for Multipath-based 5G Positioning

We consider a single-anchor multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system with imperfectly synchronized transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position based on the received reference signals. The Tx, having (imperfect) prior knowledge about the Rx location and the surrounding geometry, transmits the reference signals based on a set of fixed beams. In this work, we develop strategies for the power allocation among the beams aiming to minimize the expected Cram\'er-Rao lower bound (CRLB) for Rx positioning. Additional constraints on the design are included to ensure that the line-of-sight (LOS) path is detected with high probability. Furthermore, the effect of clock asynchronism on the resulting allocation strategies is also studied. We also propose a gridless compressed sensing-based position estimation algorithm, which exploits the information on the clock offset provided by non-line-of-sight paths, and show that it is asymptotically efficient.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless Communication

High-accuracy joint position and orientation estimation in sparse 5G mmWave channel

Abstract With the emergence of new 5G radio networks, high-accuracy positioning solutions are becoming extensively more important for numerous 5G-enabled applications and radio resource management tasks. In this paper, we focus on 5G mm-wave systems, and propose a method for high-accuracy estimation of the User Equipment (UE) position and antenna orientation. Based on the sparsity of the mm-wave channel, we utilize a compressive sensing approach for estimating the departure and arrival angles as well as the time-of-arrival for each observed radio propagation path. After this, in order to obtain statistical descriptions of the unknown parameters, we analytically derive a set of sampling distributions, which enable utilization of an iterative Gibbs sampling method. As shown by the obtained simulation results, the proposed method is able to achieve centimeter-level positioning accuracy with degree-level orientation accuracy, even in the absence of a line-of-sight path

Localization and tracking of high-speed trains using compressed sensing based 5G localization algorithms

Dissertation (MSc(Electronic Engineering))--University of Pretoria, 2022.Complex systems are in place for the localization and tracking of High Speed Trains. These methods tend to perform poorly under certain conditions. Localization using 5G infrastructure has been considered as an alternative solution for the positioning of trains in previous studies. However, these studies only consider localization using Time Difference of Arrival measurements or using Time of Arrival and Angle of Departure measurements. In this paper an alternate compressed sensing based 5G localization method is considered for this problem. The proposed algorithm, paired with an Extended Kalman Filter, is implemented and tested on a 3GPP specified high speed train scenario. The proposed algorithm is tested in two different scenarios. The first is a straight track scenario and the second is a part of a real-life track between Shanghai and Beijing using data from OpenStreetMaps with the map points joined using cubic Bezier curves. The algorithm achieves sub-meter accuracy on the straight track scenario using just one Remote-Radio-Head. For the map trajectory generated using cubic Bezier curves, an accuracy of 1.05~m is achieved with a 99\% availability using only one Remote-Radio-Head, and sub-meter accuracy is achieved when using two Remote-Radio-Heads. The performance requirements set out by 3GPP for the use case of machine control and intelligent transportation are met with just one Remote-Radio-Head.Electrical, Electronic and Computer EngineeringMsc(Electronic Engineering)Unrestricte

GNSS/5G Hybridization for urban navigation

Over the past few years, the need for positioning, and thus the number of positioning services in general, has been in constant growth. This need for positioning has been increasingly focused on constrained environments, such as urban or indoor environments, where GNSS is known to have significant limitations: multipath as well as the lack of Line-of-Sight satellite visibility degrades the GNSS positioning solution and makes it unsuitable for some urban or indoor applications. In order to improve the GNSS positioning performance in constrained environments, many solutions are already available: hybridization with additional sensors or the use of signals of opportunity for example. Concerning SoO, mobile communication signals, such as the 4G Long Term Evolution or 5G, are naturally envisioned for positioning. Indeed, a significant number of users are expected to be “connected-users” and 5G systems offers promising opportunities. 5G technology is being standardized at 3GPP; the first complete release of 5G specifications, Release-15, was provided to the community in June 2018. 5G is an emerging technology and its positioning performance, as well as a potential generic receiver scheme to conduct positioning operations, is still under analysis. In order to study the potential capabilities provided by 5G systems and to develop a 5Gbased generic positioning module scheme, the first fundamental step is to develop mathematical models of the processed 5G signals at each stage of the receiver for realistic propagation channel models: the mathematical expression of the useful received 5G signal as well as the AWG (Additive White Gaussian) noise statistics. In the Ph.D., the focus is given to the correlation operation which is the basic function implemented by typical ranging modules for 4G LTE signals, DVB signals, and GNSS. In fact, the knowledge of the correlation output mathematical model could allow for the development of optimal 5G signal processing techniques for ranging positioning. Previous efforts were made to provide mathematical models of received signals at the different receiver signal processing stages for signals with similar structures to 5G signals – Orthogonal ²Frequency Division Multiplexing (OFDM) signals as defined in 3GPP standard. OFDM signal-type correlator output mathematical model and acquisition techniques were derived. Moreover, tracking techniques were proposed, analyzed and tested based on the correlator output mathematical model. However, these models were derived by assuming a constant propagation channel over the duration of the correlation. Unfortunately, when the Channel Impulse Response (CIR) provided by a realistic propagation channel is not considered to be constant over the duration of the correlation, the correlator output mathematical models are slightly different from the mathematical models proposed in the literature