58 research outputs found

    DoA and ToA Estimation, Device Positioning and Network Synchronization in 5G New Radio : Algorithms and Performance Analysis

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    Location information plays a significant role not only in our everyday life through various location-based services, but also in emerging technologies such as virtual reality, robotics, and autonomous driving. In contrast to the existing and earlier cellular generations, positioning has been considered as a key element in future cellular networks from the very beginning of the fifth generation (5G) standardization process. Even though the earlier generations are capably of providing coarse location estimates, the achieved accuracy is far from the expected even sub-meter positioning accuracy envisioned in the context of 5G networks. In general, 5G new radio (NR) networks provide a convenient infrastructure for positioning by means of wider bandwidths, larger antenna arrays, and even more densely deployed networks especially at high millimeter wave (mmWave) frequencies. Building on dense 5G NR networks, this thesis focuses on the development of novel network-centric positioning frameworks by exploiting the existing NR reference signals. The contributions in this thesis can be grouped into topics based on the considered frequency ranges and the employed beamforming (BF) schemes therein. First, novel cascaded algorithms for sequential device positioning are proposed assuming 5G NR networks operating at the lower sub-6 GHz frequency range and equipped with digital BF capabilities. In the first stage of the cascaded solution, two sequential estimators are proposed for joint direction of arrival (DoA) and time of arrival (ToA) estimation facilitating the received reference signals. Thereafter, the second-stage sequential estimators employing the obtained DoA and ToA estimates are proposed for joint positioning and network synchronization resulting in not only device location estimates, but also clock parameter estimates that are obtained as a valuable by-product. Such a choice stems from the fact that the ToA estimates are not feasible for positioning as such due to the clock instabilities in low-cost devices and the insufficient level of synchronization in the cellular networks. Second, a similar cascaded algorithm for joint positioning and network synchronization is proposed in the context of dense mmWave 5G networks and fundamentally different analog BFs. In particular, a novel joint DoA and ToA estimator is proposed by fusing information from multiple received beams based on a novel beam-selection method. In addition, the theoretical performance limits are derived and compared to those obtained using the digital BFs. The cascaded framework is completed with the second-stage positioning solution in a similar manner as in the case of digital BFs. The performance of both frameworks is evaluated and analyzed in various scenarios using extensive computer simulations relying on the latest 5G NR numerology and a ray-tracing tool. Overall, this thesis provides valuable insights into practical positioning algorithms and their performance when relying solely on the 5G NR networks and available signalling therein. The obtained results in this thesis indicate that the envisioned sub-meter positioning accuracy is technically feasible using NR-based solutions

    Effects of Time Synchronization Errors in IoT Networks

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    Internet of Things is a term referring to the wireless connection of people and devices, briefly referred to as ‘things’. The growth of technology has become so rapid, that people are finding various ways and means to communicate to each other in a fast and reliable way. Industries and other organizations such as hospitals, military, schools and so on, are demanding better, easy and cheaper way to communicate or pass out information. Time and frequency synchronization are basic demands for all wireless communication system to work accurately. In time synchronization, the receiver terminal determines the correct time at which to sample the incoming signal. For two or more systems to function at same time with high speed, accuracy and reliability, they must be well synchronized, and time sensitive enough so that it will not experience failure at some point in time. This thesis focuses on the characteristics of IoT technologies, how time-sensitive an IoT network can be, and what time and frequency synchronization solutions there exist. A simulation study is also performed using Binary Phase Shift Keying (BPSK) modulation and Narrowband (NB) and Ultra-Narrowband (UNB) signals. The simulation-based analysis is done with three error models (constant, random and clock) using MATLAB simulation, where a plot of Bit-Error-Rate (BER) versus Signal-to-Noise-Ratio (SNR) is drawn to investigate the effects of the time synchronization errors with the NB and UNB signals

    Positioning in 5G and 6G Networks—A Survey

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    Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios

    High-Performance Modelling and Simulation for Big Data Applications

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    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

    Get PDF
    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

    GNSS/5G Hybridization for urban navigation

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    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
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