Beam-based Device Positioning in mmWave 5G Systems under Orientation Uncertainties

High-accuracy positioning based on angles from multiple base-stations (BSs) requires precise knowledge of the BSs’ orientation. Installation errors are bound to occur in practice, and any mismatch between the actual and assumed orientation of the BSs on a given coordinate system leads to systematic errors in the position estimates. In this paper, we consider a system in which BSs transmit reference-signals (RSs) by means of static beams. The directions of such transmissions are known up to an error that is common to all beams for each BS. Different BSs have independent orientation errors. Users determine the reference-signal-received-power (RSRP) of such directional transmissions by employing receive beams. This is termed beam-RSRP (BRSRP) measurements, and they are reported to the network by the users. We propose an algorithm that jointly estimates the 3D positions of users and the orientation errors of the BSs from reported BRSRP measurements. The proposed algorithm is also applicable to the case of a single moving user. The performance of the proposed solution is assessed on a realistic mmW 5G outdoor deployment simulator at 39 GHz and based on ray-tracing propagation modeling. Results show that for a minimum of three BSs in line-of-sight (LoS) to the user, and an orientation uncertainty in azimuth angle of $phi leq 3 ^ circ$ sub-meter positioning accuracy is achieved in 90% of locations. Such orientation uncertainty is estimated with an accuracy of 0.5° in 98% of locations.acceptedVersionPeer reviewe

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

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications