5G Positioning: An Analysis of Early Datasets

: Global Navigation Satellite Systems (GNSSs) are nowadays the prevailing technology for positioning and navigation. However, with the roll-out of 5G technology, there is a shift towards 'hybrid positioning': indeed, 5G time-of-arrival (ToA) measurements can provide additional ranging for positioning, especially in environments where few GNSS satellites are visible. This work reports a preliminary analysis, the processing, and the results of field measurements collected as part of the GINTO5G project funded by ESA's EGEP programme. The data used in this project were shared by the European Space Agency (ESA) with the DICA of Politecnico di Milano as part of a collaboration within the ESALab@PoliMi research framework established in 2022 between the two organizations. The ToA data were collected during a real-world measurement campaign and they cover a wide range of user environments, such as indoor areas, outdoor open sky, and outdoor obstructed scenarios. Within the test area, eleven self-made replica 5G base stations were set up. A trolley, carrying a self-made 5G receiver and a data storage unit, was moved along predefined trajectories; the trolley's accurate trajectories were determined by a total station, which provided benchmark positions. In the present work, the 5G data are processed using the least squares method, testing and comparing different strategies. Therefore, the primary goal is to evaluate algorithms for position determination of a user based on 5G observations, and to empirically assess their accuracy. The results obtained are promising, with positional accuracy ranging from decimeters to a few meters in the worst cases

Protecting the skies: GNSS-less aircraft navigation with terrestrial cellular signals of opportunity

This paper shows how to protect our skies from harmful radio frequency interference (RFI) to global navigation satellite system (GNSS) signals, by offering terrestrial cellular signals of opportunity (SOPs) as a viable aircraft navigation system backup. An extensive flight campaign was conducted by the Autonomous Systems Perception, Intelligence, and Navigation (ASPIN) Laboratory in collaboration with the United States Air Force (USAF) to study the potential of cellular SOPs for high-altitude aircraft navigation. A multitude of flight trajectories and altitudes were exercised in the flight campaign in two different regions in Southern California, USA: (i) rural and (ii) semi-urban. Samples of the ambient downlink cellular SOPs were recorded, which were fed to ASPIN Laboratory's MATRIX (Multichannel Adaptive TRansceiver Information eXtractor) software-defined receiver (SDR), which produced carrier phase measurements from these samples. These measurements were fused with altimeter data via an extended Kalman filter (EKF) to estimate the aircraft's trajectory. This paper shows for the first time that at altitudes as high as about 11,000 ft above ground level (AGL), more than 100 cellular long-term evolution (LTE) eNodeBs can be reliable tracked, many of which were more than 100 km away, with carrier-to-noise ratio (C/N0) exceeding 40 dB-Hz. The paper shows pseudorange and Doppler tracking results from cellular eNodeBs along with the C/N0 and number of tracked eNodeBs over the two regions, while performing ascending, descending, and grid maneuvers. In addition, the paper shows navigation results in the semi-urban and rural regions, showing a position root mean-squared error of 9.86 m and 10.37, respectively, over trajectories of 42.23 km and 56.56 km, respectively, while exploiting an average of about 19 and 10 eNodeBs, respectively.This work was supported in part by the Office of Naval Research (ONR) under Grant N00014-19-1-2511 and Grant N00014-19-1-2613, in part by the National Science Foundation (NSF) under Grant 2240512, in part by the U.S. Department of Transportation (USDOT) under Grant 69A3552047138 for the CARMEN University Transportation Center (UTC), and in part by the Air Force Office of Scientific Research (AFOSR) under Grant FA9550-22-1-0476. This work was also supported in part by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. SAND2022-13901

Contextually and identity aware 5G services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe fifth generation (5G) mobile networks aim to be ten times faster than the existing 4G connection, whilst providing low latency, and flexibility. Hence, various alterations are planned to the existing network infrastructure to be able to reach the 5G expected performance levels. The main technologies that were used, to ensure high performance, flexible network, and efficient resource allocation, are Software Defined Network and Network Function Virtualization. As these technologies are replacing the device-based architecture with, a service-based architecture. This thesis provides a design of location database interactive web interface and interactive mobile application. The implementation of real time video streaming location server, the streaming system's performance parameters demonstrated a high level of QoS (0.07ms jitter and 9.53ms delay). In regard to experimental examination, it measured the localisation coverage, accuracy measurements and a highly scalable security solution. The localisation coverage and accuracy measurements were achieved through the mmWave and VLC link transmitters. The proposed simulated annealing algorithm aimed at data optimisation for location measurements accuracy showed results of the average location error of x and y which showed significant improvement from x= 22.5 and y=21.6 to x=11.09 and y= 11.63. The proposed indoor location security solution showed significant results, as it provides a high scalability solution using the VNF. The solution showed that it was not 100% effective, as some of the fake discover packets still reached the DHCP server. This was due to the high load of traffic passing through the network. Nonetheless, 90% of the fake DHCP discover packets never reached the DHCP server because the scripts began blocking all fake discover packets after realising it was an attack. This conveys that the proposed system was able to run successfully without crashing or overloading the controller. Overall, the main challenges facing 5G have been addressed with their proposed solutions, which showed promising results. Conclusively showing that there is a lot more space for technological advancements to support the future of mobile networks.European Union’s Horizon 2020 research program - the Internet of Radio-Light (IoRL) project H2020-ICT 761992

Satellite Systems in the Era of 5G Internet of Things

In recent years, IoT applications have drawn a great deal of attention, both in academia and industry. A crucial requirement of any infrastructure serving the IoT market will be to guarantee ubiquitous connectivity to the low-cost, low-powered devices distributed all over the globe. It is widely accepted that this requirement will not be met by the terrestrial network alone. There will be, in fact, vast areas of the globe where the terrestrial infrastructure deployment will be unfeasible or not economically viable, thus leaving those areas un- or under-served. For this reason, several studies and projects are addressing the use of a Non-Terrestrial Network component to seamlessly complement and extend the terrestrial network coverage in future systems. The design of these extremely complex systems requires manifold analyses at different levels of abstraction, from satellite constellation and ground segment architecture aspects, to the evaluation of the air interface behaviour, in order to evaluate the system performance. The aim of this work is to perform a detailed analysis of the SatCom system aspects, trying to be as accurate as possible but without getting lost in unnecessary details, in order to provide a comprehensive set of tools, organised in a simulation platform, to support the design and performance evaluation of the system. Notably, simulation softwares play an important role in this framework; however, a full-featured simulation tool does not yet exist for the evaluation of the described emerging technologies. ESA M2M Simulator (ESiM2M) is a System-Level Simulator, developed in collaboration with the European Space Agency, which is intended for closing this gap, as a tool for the design and analysis, from a system-level point of view, of Satellite-IoT systems. This work is primarily focused on the description of the ESiM2M simulation tool and the results derived with the latter from analyses on Satellite-IoT systems

D6.6 Final report on the METIS 5G system concept and technology roadmap

This deliverable presents the METIS 5G system concept which was developed to fulfil the requirements of the beyond-2020 connected information society and to extend today’s wireless communication systems to include new usage scenarios. The METIS 5G system concept consists of three generic 5G services and four main enablers. The three generic 5G services are Extreme Mobile BroadBand (xMBB), Massive Machine- Type Communications (mMTC), and Ultra-reliable Machine-Type Communication (uMTC). The four main enablers are Lean System Control Plane (LSCP), Dynamic RAN, Localized Contents and Traffic Flows, and Spectrum Toolbox. An overview of the METIS 5G architecture is given, as well as spectrum requirements and considerations. System-level evaluation of the METIS 5G system concept has been conducted, and we conclude that the METIS technical objectives are met. A technology roadmap outlining further 5G development, including a timeline and recommended future work is given.Popovski, P.; Mange, G.; Gozalvez -Serrano, D.; Rosowski, T.; Zimmermann, G.; Agyapong, P.; Fallgren, M.... (2014). D6.6 Final report on the METIS 5G system concept and technology roadmap. http://hdl.handle.net/10251/7676

Collaborative Techniques for Indoor Positioning Systems

The demand for Indoor Positioning Systems (IPSs) developed specifically for mobile and wearable devices is continuously growing as a consequence of the expansion of the global market of Location-based Services (LBS), increasing adoption of mobile LBS applications, and ubiquity of mobile/wearable devices in our daily life. Nevertheless, the design of mobile/wearable devices-based IPSs requires to fulfill additional design requirements, namely low power consumption, reuse of devices’ built-in technologies, and inexpensive and straightforward implementation. Within the available indoor positioning technologies, embedded in mobile/wearable devices, IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE) in combination with lateration and fingerprinting have received extensive attention from research communities to meet the requirements. Although these technologies are straightforward to implement in positioning approaches based on Received Signal Strength Indicator (RSSI), the positioning accuracy decreases mainly due to propagation signal fluctuations in Line-of-sight (LOS) and Non-line-of-sight (NLOS), and the heterogeneity of the devices’ hardware. Therefore, providing a solution to achieve the target accuracy within the given constraints remains an open issue. The motivation behind this doctoral thesis is to address the limitations of traditional IPSs for human positioning based on RSSI, which suffer from low accuracy due to signal fluctuations and hardware heterogeneity, and deployment cost constraints, considering the advantages provided by the ubiquity of mobile devices and collaborative and machine learning-based techniques. Therefore, the research undertaken in this doctoral thesis focuses on developing and evaluating mobile device-based collaborative indoor techniques, using Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), for human positioning to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. The methodology followed during the research consists of four phases. In the first phase, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to identify the key design aspects and evaluations used in/for CIPSs and the main concerns, limitations, and gaps reported in the literature. In the second phase, extensive experimental data collections using mobile devices and considering collaborative scenarios were performed. The data collected was used to create a mobile device-based BLE database for testing ranging collaborative indoor positioning approaches, and BLE and Wi-Fi radio maps to estimate devices’ position in the non-collaborative phase. Moreover, a detailed description of the methodology used for collecting and processing data and creating the database, as well as its structure, was provided to guarantee the reproducibility, use, and expansion of the database. In the third phase, the traditional methods to estimate distance (i.e., based on Logarithmic Distance Path Loss (LDPL) and fuzzy logic) and position (i.e., RSSI-lateration and fingerprinting–9-Nearest Neighbors (9-NN)) were described and evaluated in order to present their limitations and challenges. Also, two novel approaches to improve distance and positioning accuracy were proposed. In the last phase, our two proposed variants of collaborative indoor positioning system using MLP ANNs were developed to enhance the accuracy of the traditional indoor positioning approaches (BLE–RSSI lateration-based and fingerprinting) and evaluated them under real-world conditions to demonstrate their feasibility and benefits, and to present their limitations and future research avenues. The findings obtained in each of the aforementioned research phases correspond to the main contributions of this doctoral thesis. Specifically, the results of evaluating our CIPSs demonstrated that the first proposed variant of mobile device-based CIPS outperforms the positioning accuracy of the traditional lateration-based IPSs. Considering the distances among collaborating devices, our CIPS significantly outperforms the lateration baseline in short distances (≤ 4m), medium distances (>4m and ≤ 8m), and large distances (> 8m) with a maximum error reduction of 49.15 %, 19.24 %, and 21.48 % for the “median” metric, respectively. Regarding the second variant, the results demonstrated that for short distances between collaborating devices, our collaborative approach outperforms the traditional IPSs based on BLE–fingerprinting and Wi-Fi–fingerprinting with a maximum error reduction of 23.41% and 19.49% for the “75th percentile” and “90th percentile” metric, respectively. For medium distances, our proposed approach outperforms the traditional IPSs based on BLE–fingerprinting in the first 60% and after the 90% of cases in the Empirical Cumulative Distribution Function (ECDF) and only partially (20% of cases in the ECDF) the traditional IPSs based on Wi-Fi–fingerprinting. For larger distances, the performance of our proposed approach is worse than the traditional IPSs based on fingerprinting. Overall, the results demonstrate the usefulness and usability of our CIPSs to improve the positioning accuracy of traditional IPSs, namely IPSs based on BLE– lateration, BLE–fingerprinting, and Wi-Fi–fingerprinting under specific conditions. Mainly, conditions where the collaborative devices have short and medium distances between them. Moreover, the integration of MLP ANNs model in CIPSs allows us to use our approach under different scenarios and technologies, showing its level of generalizability, usefulness, and feasibility.Cotutelle-yhteistyöväitöskirja

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

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

Context-Aware Self-Healing for Small Cell Networks

These can be an invaluable source of information for the management of the network, in a way that we have denominated as context-aware SON, which is the approach proposed in this thesis. To develop this concept, the thesis follows a top-down approach. Firstly, the characteristics of the cellular deployments are assessed, especially for indoor small cell networks. In those scenarios, the need for context-aware SON is evaluated and considered indispensable. Secondly, a new cellular architecture is defined to integrate both context information and SON mechanisms in the management plane of the mobile network. Thus, the specifics of making context an integral part of cellular OAM/SON are defined. Also, the real-world implementation of the architecture is proposed. Thirdly, from the established general SON architecture, a logical self-healing framework is defined to support the context-aware healing mechanisms to be developed. Fourthly, different self-healing algorithms are defined depending on the failures to be managed and the conditions of the considered scenario. The mechanisms are based on probabilistic analysis, making use of both context and network data for detection and diagnosis of cellular issues. The conditions for the implementation of these methods are assessed. Their applicability is evaluated by means of simulators and testbed trials. The results show important improvements in performance and capabilities in comparison to previous methods, demonstrating the relevance of the proposed approach.The last years have seen a continuous increase in the use of mobile communications. To cope with the growing traffic, recently deployed technologies have deepened the adoption of small cells (low powered base stations) to serve areas with high demand or coverage issues, where macrocells can be both unsuccessful or inefficient. Also, new cellular and non-cellular technologies (e.g. WiFi) coexist with legacy ones, including also multiple deployment schemes (macrocell, small cells), in what is known as heterogeneous networks (HetNets). Due to the huge complexity of HetNets, their operation, administration and management (OAM) became increasingly difficult. To overcome this, the NGMN Alliance and the 3GPP defined the Self-Organizing Network (SON) paradigm, aiming to automate the OAM procedures to reduce their costs and increase the resulting performance. One key focus of SON is the self-healing of the network, covering the automatic detection of problems, the diagnosis of their causes, their compensation and their recovery. Until recently, SON mechanisms have been solely based on the analysis of alarms and performance indicators. However, on the one hand, this approach has become very limited given the complexity of the scenarios, and particularly in indoor cellular environments. Here, the deployment of small cells, their coexistence with multiple telecommunications systems and the nature of those environments (in terms of propagation, coverage overlapping, fast demand changes and users' mobility) introduce many challenges for classic SON. On the other hand, modern user equipment (e.g. smartphones), equipped with powerful processors, sensors and applications, generate a huge amount of context information. Context refers to those variables not directly associated with the telecommunication service, but with the terminals and their environment. This includes the user's position, applications, social data, etc