Positioning and Location-based Analytics in 5G and Beyond E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
POSITIONING AND LOCATION-BASED ANALYTICS IN 5G AND BEYOND Understand the future of cellular positioning with this introduction The fifth generation (5G) of mobile network technology are revolutionizing numerous aspects of cellular communication. Location information promises to make possible a range of new location-dependent services for end users and providers alike. With the new possibilities of this location technology comes a new demand for location-based analytics, a new paradigm for generating and analyzing dynamic location data for a wide variety of purposes. Positioning and Location-based Analytics in 5G and Beyond introduces the foundational concepts related to network localization, user positioning, and location-based analytics in the context of cutting-edge mobile networks. It includes information on current location-based technologies and their application, and guidance on the future development of location systems beyond 5G. The result is an accessible but rigorous guide to a bold new frontier in cellular technology. Positioning and Location-based Analytics in 5G and Beyond readers will also find: * Contributions from leading researchers and industry professionals * High-level insights into 5G and its future evolution * In-depth coverage of subjects such as positioning enablers, location-aware network management, reference standard architectures, and more Positioning and Location-based Analytics in 5G and Beyond is ideal for researchers and industry professionals with an understanding of network communications and a desire to understand the future of the field.
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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
About the Editors
List of Contributors
Preface
Notes
Acknowledgments
List of Abbreviations
1 Introduction and Fundamentals
1.1 Introduction and Motivation
1.2 Use Cases, Verticals, and Applications
1.3 Fundamentals of Positioning and Navigation
1.4 Fundamentals of Location‐Based Analytics
1.5 Introduction to Architectural Principles
1.6 Book Outline
References
Part I: Positioning Enablers
2 Positioning Methods
2.1 Positioning as Parameter Estimation
2.2 Device‐Based Radio Positioning
2.3 Device‐Free Radio Localization
2.4 AI/ML for Positioning
References
Notes
3 Standardization in 5G and 5G Advanced Positioning
3.1 Positioning Standardization Support Prior to 5G
3.2 5G Positioning
3.3 Hybrid Positioning Technologies
3.4 5G Advanced Positioning
References
Note
4 Enablers Toward 6G Positioning and Sensing
4.1 Integrated Sensing and Communication
4.2 Reconfigurable Intelligent Surfaces for Positioning and Sensing
4.3 Advanced Methods
References
5 Security, Integrity, and Privacy Aspects
5.1 Location Privacy
5.2 Location Security
5.3 3GPP Integrity Support
References
Note
Part II: Location‐based Analytics and New Services
6 Location and Analytics for Verticals
6.1 People‐Centric Analytics
6.2 Localization in Road Safety Applications
References
Note
7 Location‐Aware Network Management
7.1 Introduction
7.2 Location‐Aware Cellular Network Planning
7.3 Location‐Aware Network Optimization
7.4 Location‐Aware Network Failure Management
References
Part III: Architectural Aspects for Localization and Analytics
8 Location‐Based Analytics as a Service
8.1 Motivation for a Dedicated Platform
8.2 Principles
8.3 Platform System Overview
8.4 Platform System Blocks Description
8.5 Functional Decomposition
8.6 System Workflows and Data Schema Analysis
8.7 Platform Implementation: Available Technologies
References
9 Reference Standard Architectures
9.1 Data Analytics in the 3GPP Architecture
9.2 3GPP CAPIF
9.3 3GPP SEAL
9.4 ETSI NFV
9.5 ETSI Zero Touch Network and Service Management (ZSM)
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 List of acronyms.
Chapter 3
Table 3.1 List of acronyms.
Table 3.2 5G standardized methods and corresponding measurements.
Chapter 4
Table 4.1 List of acronyms
Chapter 5
Table 5.1 List of acronyms.
Table 5.2 Feasibility of the IMSI catching attack for different mobile devi...
Chapter 6
Table 6.1 List of acronyms.
Table 6.2 Computation times considering different time intervals from 2 to ...
Table 6.3 Displacement error for ETH/UCY and IncelliSIM datasets [23].
Table 6.4 Example of safety‐critical use cases and service level requiremen...
Chapter 7
Table 7.1 List of acronyms.
Chapter 8
Table 8.1 List of acronyms.
Table 8.2 Persistence data management and message queue functions.
Table 8.3 Indicative list of location‐based analytics functions.
Table 8.4 List of analytics API functions.
Table 8.5 List of control functions.
Chapter 9
Table 9.1 List of Acronyms.
List of Illustrations
Chapter 1
Figure 1.1 Illustration of the categories of use cases for positioning and l...
Figure 1.2 Example approaches to positioning. (a) Positioning based on ToF f...
Figure 1.3 Illustration of the main blocks for estimating the user position ...
Figure 1.4 Pictorial description of the main steps for obtaining location‐ba...
Figure 1.5 Illustration of the high‐level system architecture for location‐b...
Chapter 2
Figure 2.1 Illustration of the prediction (based on measurements up to a tim...
Figure 2.2 Fusion of 3 sensors (camera, 5G, and inertial measurement unit) i...
Figure 2.3 Illustration of a device‐based positioning configuration with g...
Figure 2.4 (Top) InF scenario deployment of network transmission/reception p...
Figure 2.5 Geometrical interpretation of the EFIM in the single‐path scenari...
Figure 2.6 Conceptual diagram of the processing steps for multi‐target track...
Figure 2.7 Scheme of crowd‐centric counting via unsupervised learning.
Figure 2.8 Orientation‐based spectrum and pictorial direction estimation. (a...
Figure 2.9 Graphical representation of the offline/training phase (a) and th...
Figure 2.10 Indicative spatial distribution of measurements and error in met...
Figure 2.11 Pictorial representation of localization techniques based on sin...
Figure 2.12 SI‐based localization performance in 5G networks considering two...
Figure 2.13 Comparison between estimated UAV positions. (a) Localization err...
Chapter 3
Figure 3.1 3GPP 4G and 5G positioning timeline.
Figure 3.2 Functional architecture for 5G localization [13].
Figure 3.3 LPP and NRPPa configurations within 5G localization architecture ...
Figure 3.4 An illustration of 5G positioning methods and elements such as be...
Figure 3.5 NR DL‐PRS configuration hierarchy [11].
Chapter 4
Figure 4.1 Resource allocation mechanism (a) and example scenario (b) for si...
Figure 4.2 Human activity recognition and person identification via micro‐Do...
Figure 4.3 RIS enabling/enhancing positioning and sensing. (A) RIS enabling ...
Figure 4.4 Benefits of RIS on positioning accuracy as a function of carrier ...
Figure 4.5 Benefits of RIS on spectral efficiency as a function of carrier f...
Figure 4.6 Five examples for advances in model‐based methods toward 6G posit...
Figure 4.7 Improved positioning accuracy provided by
soft information
(
SI
)‐b...
Chapter 5
Figure 5.1 Comparison of anonymity between the proposed algorithm and the Ca...
Figure 5.2 High‐level description of the location security function and its ...
Figure 5.3 Jamming operating scenario.
Figure 5.4 Spoofing/meaconing operating scenario.
Figure 5.5 Example scenario with anchors and one agent in the presence of ...
Figure 5.6 SPEB (dashed) and MSE (solid) varying...
Figure 5.7 SPEB (dashed) and MSE (solid) varying...
Figure 5.8 MSE for different numbers of anchors in the case of two spoofed a...
Figure 5.9 Illustration of the definitions of accuracy, reliability, and int...
Figure 5.10 Positioning integrity KPI fundamentals [25].
Chapter 6
Figure 6.1 Crowd monitoring and group inference for smart cities through wir...
Figure 6.2 Analytics pipeline of CountMeIn. CountMeIn consists of wireless s...
Figure 6.3 Analytics pipeline of Group‐In. Group‐In leverages multiple scann...
Figure 6.4 (a) Distribution of groups by group size observed, (b) (from [4] ...
Figure 6.5 GRU with attention architecture.
Figure 6.6 Measurement of mobility among geographical units.
Figure 6.7 Number of cases vs. mobility (a) and traffic profiles of all the ...
Figure 6.8 Comparison between: (a) predicted and real infections; and (b) pr...
Figure 6.9 Illustration of four use cases with stringent positioning require...
Figure 6.10 C‐ITS position and time entity functional architecture (adapted ...
Figure 6.11 VAM messaging traffic.
Figure 6.12 VRU clustering statistics.
Figure 6.13 VRU clustering impact.
Chapter 7
Figure 7.1 Cellular network management functions and location‐awareness.
Figure 7.2 EMF levels (in V/m) for two different values of localization accu...
Figure 7.3 Network optimization and location information.
Figure 7.4 (a) Visualized cluster centroids and (b) simulation playground fo...
Figure 7.5 RSRP (a) and SINR (b) gains comparison for hybrid clustering vs. ...
Figure 7.6 Throughput gain after the first phase for users throughout the sc...
Figure 7.7 Throughput gain after the second stage for users in normal areas....
Figure 7.8 Throughput gain after the full framework for users in crowded are...
Figure 7.9 Predicted SINR for ...
Figure 7.10 Coverage probability for ...
Figure 7.11 Spatial distribution of UEs in cells...
Figure 7.12 mean user throughput (MUT) inside and outside the hotspot areas....
Figure 7.13 Load balancing system based on SAPTS and PTS [23].
Figure 7.14 SINR map from regular hexagonal 5G Urban Macro scenario using “c...
Figure 7.15 Number of users per serving cell and adjacent cells for venue lo...
Figure 7.16 Classic (a) and 5G/6G (b) coverage scenarios representation.
Figure 7.17 Area‐based automatic generation of indicators.
Figure 7.18 Macro‐averaged F1 score [34].
Figure 7.19 Diagnosis time assessment [34].
Figure 7.20 Proposed framework for NSI use and application.
Figure 7.21 Performance of the image classification methods used.
Figure 7.22 Performance of the proposed CNN approach.
Chapter 8
Figure 8.1 Main platform architecture principles for enabling location‐based...
Figure 8.2 System blocks' categorization for a location‐based Analytics as a...
Figure 8.3 Platform's API block components.
Figure 8.4 Platform's control block components.
Figure 8.5 Platform's core block components.
Figure 8.6 Platform's management, orchestration, and infrastructure componen...
Figure 8.7 Service activation system workflow.
Figure 8.8 Service consumption system workflow.
Figure 8.9 Southbound data collection system workflow.
Figure 8.10 Positioning and analytics service operation system workflow.
Figure 8.11 Available tools and technologies for platform implementation.
Chapter 9
Figure 9.1 NWDAF in the framework for 5G network automation.
Figure 9.2 NWDAF distributed deployment model [4].
Figure 9.3 Architecture for network data analytics in Release 17 [2].
Figure 9.4 NWDAF split in ANLF and MTLF [2].
Figure 9.5 CAPIF architecture – functional model [8].
Figure 9.6 SEAL architecture – generic (on‐network) functional model [10].
Figure 9.7 ETSI NFV architectural framework [11].
Figure 9.8 ETSI ZSM reference architecture [12].
Guide
Cover
Table of Contents
Title Page
Copyright
About the Editors
List of Contributors
Preface
Acknowledgments
List of Abbreviations
Begin Reading
Index
End User License Agreement
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IEEE Press
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Jim Lyke
Hai Li
Brian Johnson
Jeffrey Reed
Diomidis Spinellis
Adam Drobot
Tom Robertazzi
Ahmet Murat Tekalp
Positioning and Location‐based Analytics in 5G and Beyond
Edited byStefania Bartoletti and Nicola Blefari MelazziUniversity of Rome Tor Vergata and CNIT, Italy
Copyright © 2024 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging‐in‐Publication Data Applied for:
Hardback: 9781119911432
Cover Design: Wiley
Cover Image: © Prasit photo/Getty Images
About the Editors
Stefania Bartoletti, PhD, is an assistant professor at the University of Rome Tor Vergata, Italy, and a member of the National Inter‐University Consortium for Telecommunications (CNIT). She has received research funding from the European Commission through an ERC Starting Grant, as Marie‐Skłodowska Curie Global Fellow, and as coordinator of the project LOCUS.
Nicola Blefari Melazzi, PhD, is a professor at the University of Rome Tor Vergata, Italy; President of the National Inter‐University Consortium for Telecommunications (CNIT); and President of the RESTART Foundation. He has received research funding from Italian Ministries, world‐leading telecommunications companies, and the European Commission as coordinator of seven European projects. He has been appointed by the Italian Ministry of Research as the Italian representative to the European Smart Networks and Services Joint Undertaking.
List of Contributors
Zwi Altman
Orange Labs
Châtillon
France
Carlos S. Álvarez‐Merino
Telecommunication Research Institute (TELMA)
University of Malaga
E.T.S.I. de Telecomunicación
Málaga
Spain
Eduardo Baena
Telecommunication Research Institute (TELMA)
University of Malaga
E.T.S.I. de Telecomunicación
Málaga
Spain
and
Telecommunication Research Institute (TELMA)
Universidad de Málaga
Málaga
Spain
Raquel Barco
Telecommunication Research Institute (TELMA)
University of Malaga
E.T.S.I. de Telecomunicación
Málaga
Spain
Stefania Bartoletti
Department of Electronic Engineering and CNIT
University of Rome Tor Vergata
Rome
Italy
Giacomo Bernini
Nextworks
Pisa
Italy
Giuseppe Bianchi
Department of Electronic Engineering and CNIT
University of Rome Tor Vergata
Rome
Italy
Hui Chen
Department of Electrical Engineering
Chalmers University of Technology
Gothenburg
Sweden
Luca Chiaraviglio
Department of Electronic Engineering and CNIT
University of Rome Tor Vergata
Rome
Italy
Wassim B. Chikha
Orange Labs
Châtillon
France
Andrea Conti
Department of Engineering and CNIT
University of Ferrara
Ferrara
Italy
Isabel de la Bandera
Telecommunication Research Institute (TELMA)
University of Malaga
E.T.S.I. de Telecomunicación
Málaga
Spain
Nicolò Decarli
National Research Council – Institute of Electronics
Computer and Telecommunication Engineering and WiLab‐CNIT
Bologna
Italy
Yannis Filippas
Incelligent P.C.
Athens
Greece
Sergio Fortes
Telecommunication Research Institute (TELMA)
University of Malaga
E.T.S.I. de Telecomunicación
Málaga
Spain
Domenico Garlisi
Department of Mathematics and Computer Science and CNIT
University of Palermo
Palermo
Italy
Andrea Giani
Department of Engineering and CNIT
University of Ferrara
Ferrara
Italy
Domenico Giustiniano
IMDEA Networks Institute
Madrid
Spain
Carlos A. Gómez Vega
Department of Engineering and CNIT
University of Ferrara
Ferrara
Italy
Imed Hadj‐Kacem
Orange Labs
Châtillon
France
Mythri Hunukumbure
Communications Research
Samsung Electronics R&D Institute UK
Staines‐upon‐Thames
England
United Kingdom
Sana B. Jemaa
Orange Labs
Châtillon
France
Fan Jiang
Department of Electrical Engineering
Chalmers University of Technology
Gothenburg
Sweden
Emil J. Khatib
Telecommunication Research Institute (TELMA)
University of Malaga
E.T.S.I. de Telecomunicación
Málaga
Spain
Oluwatayo Y. Kolawole
Communications Research
Samsung Electronics R&D Institute UK
Staines‐upon‐Thames
England
United Kingdom
Tomasz Mach
Communications Research
Samsung Electronics R&D Institute UK
Staines‐upon‐Thames
England
United Kingdom
Aristotelis Margaris
Incelligent P.C.
Athens
Greece
Barbara M. Masini
National Research Council – Institute of Electronics
Computer and Telecommunication Engineering and WiLab‐CNIT
Bologna
Italy
Marie Masson
Orange Labs
Châtillon
France
Nicola Blefari Melazzi
Department of Electronic Engineering and CNIT
University of Rome Tor Vergata
Rome
Italy
Flavio Morselli
Department of Engineering and CNIT
University of Ferrara
Ferrara
Italy
Danilo Orlando
University “Niccolò Cusano”
Rome
Italy
Ivan Palamà
Department of Electronic Engineering and CNIT
University of Rome Tor Vergata
Rome
Italy
Sara Modarres Razavi
Ericsson Research
Ericsson AB
Stockholm
Sweden
Athina Ropodi
Incelligent P.C.
Athens
Greece
Gurkan Solmaz
NEC Laboratories Europe
Heidelberg
Germany
Gianluca Torsoli
Department of Engineering and CNIT
University of Ferrara
Ferrara
Italy
Kostas Tsagkaris
Incelligent P.C.
Athens
Greece
Joerg Widmer
IMDEA Networks Institute
Madrid
Spain
Moe Z. Win
Laboratory for Information and Decision Systems (LIDS)
Massachusetts Institute of Technology
Cambridge
MA
USA
Henk Wymeersch
Department of Electrical Engineering
Chalmers University of Technology
Gothenburg
Sweden
Preface
Ubiquitous 5G rollout is a main priority for Europe, as it the connectivity basis for the digital and green transformation of our economy.
Early reflection about the evolution of mobile communication networks “beyond 4G” started soon after the first deployment of a 4G commercial network in Sweden in 2010. In those days, it was already apparent that the very fast growth of mobile traffic, between 50% and 100% increase on a yearly basis, as well as the prospects to serve innovative Internet of Things (IoT) applications would drive further R&D in the mobile communication domain.
Taking note of these developments, the European Commission1 initiated visionary EU‐funded research activities already in 2012. This eventually led to the setup of the European 5G Public Private Partnership (5G PPP). The 5G PPP implemented under the European Horizon 2020 programme with about €700 Million of public support over the 2014–2020 period, the largest 5G R&D initiative in the world.
These initiatives materialize the importance of 5G networks for Europe. They are considered by the European Commission as a strategic asset for the digital society and to support the digital transformation of the industry and the public sector.2
The 5G PPP White Paper describing a “European Vision for the 6G Network Ecosystem”3 highlighted that “6G is expected to play a key role in the evolution of the society towards the 2030s, as the convergence between the digital, physical and personal worlds will increasingly become a reality.”
The White Paper recommended public and private R&I investment to focus on key 6G technologies, “such as programmability, integrated sensing and communication, trustworthy infrastructure, scalability and affordability, as well as AI/ML, microelectronics (at least in design), photonics, batteries (e.g., for mobile devices), software, and other technologies that may help to reduce the energy footprint.”
Addressing the White Paper's recommendations, the Commission with EU industry set up for Horizon Europe, the new Framework Programme that started 2021, a Joint Undertaking on Smart Networks and Services, beyond 5G and toward 6G, in order to maintain Europe's technology leadership and ensure its technological sovereignty in the longer term. The Smart Networks and Services Joint Undertaking aims to foster European technological capacities as regards smart networks and services value chains. In this context, the aim is to enable European players to develop the R&I capacities for 6G technologies as a basis for future digital services in the period to 2030. The initiative also aims to foster the development of lead markets for 5G infrastructure and services in Europe. Both sets of activities (for 5G infrastructure deployment and 6G R&I) will foster the alignment of future smart networks and services with EU policy and societal needs, including competitiveness, robust supply chains, energy efficiency, privacy, ethics, and cybersecurity.4
In addition, the Path to the Digital Decade5 recognizes that a sustainable digital infrastructure for connectivity is “an essential enabler for taking advantage of the benefits of digitisation, for further technological developments and for Europe's digital leadership.” It, therefore, aims to achieve all populated areas covered by 5G by 2030. The SNS JU is expected to help lead markets for 5G infrastructure and services to develop in Europe.
Global standardization and spectrum harmonization are important success factors for 6G technology and focus of SNS. Both future users and suppliers need to shape key technology standards in the field of radio communications based on existing and future spectrum bands for wireless broadband, but also in next‐generation network architecture to ensure the delivery of advanced service features, e.g. through the effective use of software technologies and open interfaces, while meeting energy‐efficiency requirements.
For 6G, as is already the case with 5G, the European Commission supports the emergence of a single comprehensive standard ensuring interoperability, cybersecurity, and the necessary economies of scale in an area where R&D investments are massive. While the 5G standardization process is still ongoing, it has been assessed that several hundreds of industry contributions to 3GPP originate from results of projects supported under the 5G PPP initiative, notably for what concerns (i) the Radio Access Network architecture (RAN) and (ii) the service‐oriented architecture of the new core network.
For 6G, the Work Programme of the SNS JU specifically includes activities designed to support the 6G standardization phase (target 2025 with first batch of 6G Study Items).
From a European perspective, it is important to continue to follow closely and support the 5G/6G standardization process so that EU policies are taken into account, maintaining strong presence of European stakeholders enlarging it to new participants, notably from the verticals, that are today little present in 3GPP debates. An inclusive standardization process is indeed a prerequisite for a global approach to standards coping with a certain divergence of market needs in the different regions. To seize the strategic opportunities for the strong industrial sectors in Europe, the standardization agenda needs to address further important use cases other than higher capacity and data rates.6
The Commission has been urging the standardization bodies, notably the 3GPP, and the concerned industrial actors to step‐up their efforts for the rapid development of 5G standards addressing more immediate market needs, while driving a clear strategy for a 5G global standard. In line with the EU strategy targeting 5G developments in support of “vertical” industries and the wider objectives of digitizing the European industry, benefits are expected to a wide range of industrial use cases.
Several of the features in Release 17 are intended to enhance network performance for existing services and use cases, while others address new use cases and deployment options. 5G Advanced will build on Release 17, providing intelligent network solutions and covering numerous new use cases in addition to previously defined use cases and deployment options.
New Radio has supported positioning since Release 15 through the use of LTE positioning (for non‐standalone deployments) and radio‐access technology independent positioning (Bluetooth, wireless LAN, pressure sensors, and so on). Release 16 introduced time‐based positioning methods for NR standalone deployments (multi‐round‐trip time (RTT), Downlink and Uplink Time Difference of Arrival), as well as an angle‐of‐arrival and angle‐of‐departure‐based positioning measurements, which can be used in combination with timing‐based solutions to achieve higher accuracy.
In Release‐17, NR positioning is further improved for specific use cases such as factory automation by targeting 20–30 cm location accuracy for certain deployments. Release‐17 also introduces further enhancements to latency reduction to enable positioning in time‐critical use cases such as remote‐control applications.7
Aside from high‐positioning accuracy, industrial Internet of Things (IIoT) and automotive use cases also demand integrity protection of the location information. From a higher layer point of view, Release‐17 introduces key performance indicators to indicate the reliability/integrity of the measurement report limited to the global navigation satellite system (GNSS) positioning procedure.
Precise positioning is often considered as a Satellite‐based feature, with the popular use of GPS or Galileo systems. Cellular networks, sensors, local or personal area wireless technologies, and even Artificial intelligence (AI) are complementary technologies which can help provide more robust and seamless location awareness in challenging environments like indoor positioning. These use cases are getting increasingly important, as 5G intends to support demanding verticals like manufacturing processes in factories, where sub‐cm positioning precision emerges as an important requirement. While positioning was not part of the essential requirements for 5G as outlined in ITU recommendation M.2083, it has become an essential feature for 5G later releases, 5G advanced, and 6G.
5G breaks technology barriers with key innovations for precise positioning already in the 3GPP Release 16 standard, with enhancements in Release 17 and in 5G Advanced. The wider bandwidths in 5G allow for finer timing resolutions. Time resolution is further improved by integrating methods for measuring, reporting, and compensating for processing delays into the radio protocol. The large number of antenna elements in massive MIMO, for mid‐bands and mmWave, generates narrower radio beams which allow for finer angular resolution. With comprehensive work on time, distance, and angular precision, the most advanced versions of 5G will provide cm‐level accuracy while 6G may go below that. This will be very much needed to realize the 6G vision with a massive real‐time twinning of the physical and the digital worlds, which require significant progress on two aspects less explored in 5G: positioning and deterministic communications.
The 5G PPP LOCUS8 project has led pioneering work in that domain, through improvements of the functionality of 5G infrastructures to provide accurate and ubiquitous location information as a network‐native service and to derive complex features and behavioral patterns out of raw location and physical events, and expose them to applications via simple interfaces. Localization, together with analytics, and their combined provision “as a service” increase the overall value of the 5G ecosystem, allowing network operators to better manage their networks and expand the range of offered applications and services. This highly valuable work has much contributed to the production of this book, and we remain indebted to the authors for making it possible through their undivided commitment and dedication.
Bernard Barani and Achilleas Kemos
European Commission, DG CONNECT‐E1
Notes
1
The views expressed in this article are those of the authors and shall not be considered as official statements of the European Commission.
2
BARANI B. & STUCKMANN P.; Leading‐edge 5G Research and Innovation: An undivided commitment of Europe, 5G in Italy White Book.
3
https://5g-ppp.eu/european-vision-for-the-6g-network-ecosystem/
.
4
6G SNS Draft WORK PROGRAMME 2023.
5
COM (2021) 574 final.
6
KEMOS A., BARANI B., and STUCKMANN P. “5G Standardisation”, Enjeux numériques ‐ N.5 ‐ mars 2019.
7
EKUDEN E. “5G evolution toward 5G advanced: An overview of 3GPP releases 17 and 18”,
https://www.ericsson.com/en/reports-and-papers/ericsson-technology-review/articles/5g-evolution-toward-5g-advanced
.
8
https://5g-ppp.eu/locus/
.
Acknowledgments
This book was supported, in part, by the LOCUS Project through the European Union's Horizon 2020 Research and Innovation Programme under Grant 871249.
The authors wish to thank Bernard Barani and Achilleas Kemos from the European Commission, DG CONNECT‐E1, for their valuable contributions to this book. Their insights and expertise have provided a valuable perspective on the subject matter and helped set the tone for the book.
List of Abbreviations
Acronyms
Definitions
3GPP
3rd Generation Partnership Project
5G
Fifth‐generation
AI
Artificial intelligence
ANN
Artificial neural network
AoA
Angle‐of‐arrival
AoD
Angle‐of‐departure
API
Application programming interface
AR
Augmented reality
CAPIF
Common API framework for 3GPP northbound APIs
ETSI
European Telecommunications Standards Institute
FCC
Federal Communications Commission
GSM
Global system for mobile communications
IIoT
Industrial Internet‐of‐Things
IoT
Internet‐of‐Things
ITS
Intelligent transportation system
K‐NN
K‐nearest neighbors
KPI
Key performance indicator
LTE
Long‐term evolution
LTE‐M
Long‐term evolution machine‐type communication
ML
Machine learning
Acronyms
Definitions
NB‐IoT
Narrowband Internet‐of‐Things
NWDAF
Network data analytics function
PCA
Principal component analysis
RAN
Radio access network
RAT
Radio access technology
RSSI
Received signal strength indicator
SA
System aspects
SDK
Software development kit
SVM
Support vector machine
TDoA
Time‐difference‐of‐arrival
ToF
Time‐of‐flight
TSG
Technical specification group service
UE
User equipment
URLLC
Ultra‐reliable low‐latency communications
XR
Extended reality
V2X
Vehicle‐to‐everything
1Introduction and Fundamentals
Stefania Bartoletti1, Eduardo Baena2, Raquel Barco2, Giacomo Bernini3, Nicola Blefari Melazzi1, Hui Chen4, Sergio Fortes2, Domenico Giustiniano5, Mythri Hunukumbure6, Fan Jiang4, Emil J. Khatib2, Oluwatayo Kolawole6, Aristotelis Margaris7, Sara Modarres Razavi8, Athina Ropodi7, Gürkan Solmaz9, Kostas Tsagkaris7 and Henk Wymeersch4
1Department of Electronic Engineering and CNIT, University of Rome Tor Vergata, Rome, Italy
2Telecommunication Research Institute (TELMA), University of Malaga, E.T.S.I. de Telecomunicación, Málaga, Spain
3Nextworks, Pisa, Italy
4Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
5IMDEA Networks Institute, IMDEA, Madrid, Spain
6Communications Research, Samsung Electronics R&D Institute UK, Staines‐upon‐Thames, England, United Kingdom
7Incelligent P.C., Athens, Greece
8Ericsson Research, Ericsson AB, Stockholm, Sweden
9NEC Laboratories Europe, Heidelberg, Germany
1.1 Introduction and Motivation
The ever‐growing demand for location‐ and navigation‐based services has made it difficult to imagine life without the support of positioning systems. Thanks to the enhancements in 5G and other radio access technology (RAT)‐independent technologies, positioning and location‐based analytics are expected to have an even larger impact on many society and industry use cases today and in the future.
In cellular networks, positioning was initiated based on estimates of distance and/or direction between base stations and devices mainly to support connection establishment. The network maintained a very crude position estimate of the most recent known position of a device from global system for mobile communication (GSM) [1]. This was in order to fulfill the regulation requirements of the emergency services [2]. Since then, each generation of cellular technology has improved the level of achievable accuracy and hence enabled new applications and use cases. 3GPP developed its own positioning methods and the related localization architecture since LTE Release 9 (in 2010). In 3GPP, the term “localization” is related to the architectural and service definitions in the Service and System Aspects (SA) Technical Specification Group (TSG), and the term ‘positioning’ is related to the methods and implementation definitions in the Radio Access Network (RAN) TSG.
This book will cover the fundamentals of network localization, user positioning, and location‐based analytics and applications in the 5G ecosystem and beyond. First, we will explore the primary verticals and relevant use cases, defining the key performance indicators (KPIs) and requirements, including those defined by the 3rd Generation Partnership Project (3GPP). The primary technologies will be described, and the foundations and signal processing approaches for accurate localization will be discussed. Architectural principles for the provision of location‐based analytics will be introduced and the two main classes of such analytics will be presented: analytics for network management and analytics for verticals. Practical examples of novel solutions and applications leveraging enhanced localization and location‐based analytics will be provided together with performance evaluations.
1.2 Use Cases, Verticals, and Applications
This section is organized as follows. It starts elaborating on the use cases and applications benefiting from location information, followed by the fundamentals of positioning and navigation. It then explains the fundamentals of location‐based analytics and the architectural principles for the positioning solutions in 3GPP and other global initiatives.
While the first positioning use case was the location of emergency calls in prior cellular generations, currently the advances in the 5G network make it possible to target accurate and timely positioning for safety‐critical intelligent transport systems (ITS) applications, such as advance warning systems and vulnerable road user protection or industrial internet of things (IIoT) scenarios. More advanced technologies such as positioning in extended reality (XR) use cases can be provisioned with 5G (B5G) and 6G.
A set of key categories of use cases, verticals, and applications that will benefit from positioning services are represented in Figure 1.1. We can identify: emergencies, Internet of Things, IIoT, construction sites and mines, Public safety and natural disasters, ITS and Autonomous Vehicles, Commercial and transport hubs, and Education and Gaming. These are detailed in the next sections.
Figure 1.1 Illustration of the categories of use cases for positioning and location‐based analytics.
1.2.1 Emergency Calls
The main use case that derived the positioning study in 3GPP standardization was to localize emergency calls. The Federal Communication Commission (FCC) regulatory requirements in the US mandated in 1996 that by October 2001 mobile phones calling the emergency number 911 had to be localized within 100 m for 67% of cases. In 2015, the horizontal positioning target for the user equipment (UE) was set to below 50 m for 80% of all 911 calls by April 2021. In a more recent report from FCC in 2020, the target for vertical positioning accuracy was also set, targeting a floor‐level accuracy of 3 m in the 80% of indoor wireless 911 calls.
1.2.2 Public Safety and Natural Disasters
A key factor for successful emergency operations is reliable communication and real‐time access to critical information. The ability to locate the victims is of course the primary goal of a rescue operation; in addition, it is critical to accurately locate first responders and/or the equipment being used throughout a rescue operation. Indeed, natural disasters and emergency events can start anywhere over large areas, and reliable and accurate fixed positioning infrastructure over the whole area is often costly and impractical. Instead, temporary and dynamic deployments for positioning can be a solution. Redundancy in the positioning technology is particularly important in these cases where, e.g. satellite signals can be obscured by vegetation or smoke. Positioning of personnel and other resources (in both horizontal and vertical dimensions) is essential for an efficient response to many kinds of emergency situations, including floods and earthquakes.
1.2.3 ITS and Autonomous Vehicles
The evolution of vehicular systems is moving toward ever more connected and fully automated vehicles. Such high level of autonomy leverages two main enablers, among others: location‐awareness based on accurate positioning and sensing, and ultra‐reliable low‐latency communication (URLLC) among vehicles within a shared network infrastructure [3–7]. These functionalities allow vehicles to develop a shared perception of their surroundings and make decisions based on local views and expected maneuvers from nearby users. The combination of URLLC with accurate positioning and sensing leads the way toward a safer transportation system with the goal of achieving zero road deaths and a better traffic flow. Given such unprecedented combination of URLLC and high localization accuracy, 5G is the first technology that has the potential to meet some of the very stringent requirements of road safety applications [8].
Besides accuracy and latency, positioning integrity, i.e. the measure of trust that can be placed on the correctness of information supplied by a positioning system, is required for use cases such as rail and maritime, unmanned autonomous vehicles (drones), autonomous driving, and vehicle‐to‐everything (V2X) to minimize safety hazards, accidents, and erroneous legal decisions that involve liabilities. It is also essential for other mission‐critical applications where positioning errors could cause harm, including emergency services, e‐health, and many IIoT scenarios.
1.2.4 IIoT, Construction Sites, and Mines
IIoT use cases are characterized by ambitious system requirements for positioning accuracy in many verticals. For example, on the factory floor, it is important to locate assets and moving objects such as forklifts, or parts to be assembled. Similar needs exist in transportation and logistics, for example. The deployment scenario for different indoor industrial environments has a significant impact on the positioning performance in terms of both accuracy and availability of the service. The impact of the various objects that are present in a factory hall is also implicitly impacting the path loss and multipath parameters.
Tracking of tools and materials on factories, construction sites, and other industrial scenarios is of interest for increased efficiency and resource utilization. Each of the different scenarios will impose different conditions, challenges, and requirements. For instance, a construction site, in contrast to a factory, develops over time, and a supporting telecommunication infrastructure is not always available from the start. A mobile deployment of, for example, 5G base stations for positioning can quickly be put to use and be adjusted to changing needs. The conditions in the mining industry are similar in many respects: fixed infrastructure does not often exist, the environment is constantly changing, and the specific parts of the mine, where on‐going work requires positioning services, vary over time.
1.2.5 Commercial and Transport Hubs
Large and open‐air shopping malls, consisting of large walking and common areas with multiple shops and establishments, are a growing trend and expected to be predominant in the near future. Similarly, transport hubs, such as airports or train stations, also commonly consist of common areas together with individual shops. The network service here is highly conditioned by people's mobility and crowd aspects impacting the optimization of all network resources, together with cutting‐edge applications and services. The use of ultra‐dense networks in such scenarios together with the dynamic nature of the users' movement make them a very high mobility scenario, with continuous need for handovers and load balancing adaptations to cover the demand. Moreover, these are highly dense areas in terms of devices and radio equipment, where a number of heterogeneous radio access technologies are available together with potentially Internet of Things (IoT) devices. Both public and private networks (e.g. deployed by shopping malls for the customer or for the employees and logistics) may coexist. Multiple cutting‐edge and very network‐demanding applications are expected. XR and holographic communications, both for leisure activities in the mall as well as part of the shop activities, marketing, and customer support (e.g. augmented reality (AR)‐supported navigation and recommendations). Shop logistics (e.g. goods tracking), autonomous delivery systems, environmental and security monitoring of the shopping area, flow tracking, as well as different requirements to support customers, entertainment areas, etc., make localization key to support the differentiated needs of the users and services in a cost‐efficient and reliable manner. Here, enrichment information (e.g. including user position information and social‐awareness [9, 10]) will be crucial to guide advanced traffic steering algorithms that will need to cope with a multi‐RAT and multi‐tenant scenario over heterogeneous network elements providing varied computation, energy, radio, networking, and data resources for both edge and cloud.
1.2.6 Internet‐of‐Things
Positioning is one of the main concepts in enabling the low‐power wide‐area IoT connectivity which provides a fundamental paradigm shift for people, businesses, and society. There are many applications that can benefit from IoT positioning. Some of these devices are expected to be positioned with high accuracy such as wearables, machinery control, safety monitoring, gaming gadgets, smart bicycles, medical equipment, and parking sensors. Another set of applications require position tracking while moving over a geographic area, such as assets in logistics, pets, white goods and appliances, and live stock. Moreover, there are a wide range of IoT applications where the devices have a fixed position during most of their life cycle. Some examples can be environment monitoring, soil, temperature sensors, smoke detectors, gas, water, and electricity metering, which may not have high positioning accuracy demands [1]. The advent of reduced capability (RedCap) devices, which addresses broadband IoT use cases and provides larger bandwidth than older technologies such as narrowband Internet of Things (NB‐IoT) and LTE‐M, will further help increasing the accuracy of IoT services.
1.2.7 Education and Gaming
Education and gaming are two very important markets that are expected to grow in the upcoming years. Education is one of the key pillars of modern society, from very early ages to university education and even mid‐career training. Novel methods such as gamification and the use of XR are being explored to better engage students. These methods, along with other networked technologies (videoconferencing with tele‐presence and holography, streaming, activity/sentiment recognition), have extreme performance requirements which need to be supported by the 6G infrastructure. These techniques will have parallel applications also in the gaming market. Education and gaming will mainly require high bandwidths with low latency for communication and processing while also maintaining privacy and security to achieve a high degree of trustworthiness. These characteristics must be achieved in several different environments. While remote classes are a major novelty in the last years, with typical end‐user challenges (including mobility, indoor and outdoor communications), the physical classroom will still play a very important role in the future of education. This will concentrate many users with similar extreme requirements in a small indoor area, producing very high spatial traffic densities. It will also offer some opportunities for reusing resources, such as rendered 3D objects that will be shared by all students. This proposed scenario presents significant challenges in terms of network management, including the need to balance network capacity, define active offloading strategies, and even implement in‐network caching techniques. Extreme requirements are expected among the varied education applications, creating a challenge for resource allocation in an environment with multiple outdoor and indoor separate areas (e.g. classrooms, corridors).
1.3 Fundamentals of Positioning and Navigation
Wireless positioning systems estimate the location of a target node, i.e. a UE, by leveraging the communication between the UE, in unknown location, and one or multiple network access nodes, usually in known location. The estimation of the UE position relies on two main phases: (i) collection of measurements of position‐dependent features performed by single or multiple nodes processing the communicated signals; and (ii) processing of such measurements employing one or multiple positioning methods, i.e. the measurements are the input of a positioning algorithm that provides the position estimate as output. There also exist advanced methods where the received signal samples are processed directly by the positioning algorithm without requiring two different steps.
1.3.1 Position‐Dependent Measurements
The main measurement types used by modern positioning systems belong to the following non‐exhaustive list (see Figure 1.2):
Time‐of‐flight (ToF): Time taken for a signal to propagate from a target UE to a network access node (uplink) or from the node to the UE (downlink). This measurement is also referred to as
time‐of‐arrival
(
ToA
).
Time‐difference‐of‐arrival (TDoA): Difference between the ToF measured by different pairs of nodes (e.g. two network access nodes paired with the same UE). This method requires synchronization of the nodes.
Angle‐of‐arrival (AoA): Direction from which a signal is received at the network access node; synchronization between the devices is not required.
Angle‐of‐departure (AoD): Direction from which the signal is transmitted from a network access node to the target UE. No synchronization between the devices is required.
Received signal strength indicator (RSSI): This technique measures the intensity of signal received at the network access node.
Figure 1.2 Example approaches to positioning. (a) Positioning based on ToF from several access nodes, constraining the UE to lie on the intersections of circles (or hyperbola in case of differential measurements). (b) Positioning based on a combination of time and angle measurements. (c) Positioning based on a fingerprint vector of received signal strengths. (d) Positioning based on internal sensors such as a compass and a gyroscope. (e) Positioning with a camera sensor.
In some applications, positioning is implemented based on internal sensors embedded in the UE, such as a compass and a gyroscope, sometimes complemented with one or more of the wireless measurements presented above. In other contexts, in addition to such measurements, computer vision analysis can be used, where vision‐based systems with diverse characteristics, e.g. in the case of image sensors employed to estimate the position of the target UE. The quality of the measurement and its statistical characterization depend on the network intrinsic properties, including the nodes deployment, the signal structure, the wireless propagation conditions, and the processing itself.
In some cases, the target of a positioning system is not a UE (i.e. a device‐based target that communicates with the access node), rather it is a device‐free target that does not communicate with the access nodes. In such a case, the target position is inferred from signals emitted by the access nodes, backscattered by the target object, and received back by the access nodes, following a radar‐like configuration. The type of measurements extracted from the received signals are similar to the device‐based case (e.g. ToF, TDoA, RSSI), while the processing techniques to extract such measurements from the received signal change, as the target must be first detected while the clutter for undesired scatterers should be filtered out.
1.3.2 Positioning Methods
Once the measurements are collected by a single or multiple pairs of nodes, positioning methods are employed to process such measurements and fuse them together in a centralized or distributed manner to obtain the UE position estimate, as illustrated in Figure 1.3.
Figure 1.3 Illustration of the main blocks for estimating the user position in a cellular network.
Many positioning methods leverage measurement models, i.e. models that describe the measurement collected. For example, these can include their statistical characterization, based on prior knowledge on the signal structure and wireless propagation conditions.
In some cases, a positioning system is able to collect measurements of different types and leverage them through hybrid positioning methods. The choice of the positioning method affects the quality of the final position estimate as well as the complexity of the computation.
Navigation and tracking refer to the case where the position estimate is updated over time, considering the spatial correlation between two consecutive estimation, e.g. based on mobility models.
1.3.3 AI/ML for Positioning
Artificial intelligence (AI) and machine learning (ML) algorithms have the ability to make decisions effectively using observed data in the absence of accurate mathematical models. For example, the measurement models might be unknown to a certain degree or the solution to the problem would require prohibitively complex computations. Both these problems can be solved through AI/ML techniques, which may bring several advantages:
Scalability: They might be used for large‐scale positioning problems when large training datasets are available.
Adaptability: They are flexible and can be adapted to dynamically changing environments and in the presence of multi‐dimensional and heterogeneous data applications, which are common in positioning use cases.
Extendibility: They can be applied to fuse the information of different positioning technologies and methods, and as each positioning technology and technique has its own advantages and disadvantages, fusing them can further improve the positioning accuracy.
There are already positioning problems such as non‐line‐of‐sight (NLOS) classification and mitigation, enhanced fingerprinting, avoiding RSSI fluctuations, trajectory learning and navigation, and fusing technologies and features that have been tackled with AI/ML algorithms with positive outcomes. The selection of which ML algorithm is suitable to be used depends on the nature and characteristics of the positioning problem and the data in hand. It is a common practice that the performance of few of these ML algorithms is compared to each other for one particular positioning problem.
Both supervised learning such as K‐Nearest Neighbor (K‐NN), Support Vector Machine (SVM), decision tree, random forest and artificial neural network (ANN), and unsupervised learning algorithms such as K‐means and Principal Component Analysis (PCA) have been applied to positioning problems. The main distinction between the two approaches is the use of labeled datasets, meaning that supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not. Moreover, reinforcement learning, deep learning, and transfer learning have also been widely attempted recently to overcome the positioning problem challenges with reasonable success [11, 12].
1.4 Fundamentals of Location‐Based Analytics
Once user positioning information is available through localization, one key challenge is its application to specific services, for end‐users and third parties as well as for the management of the cellular network itself. To this end, location information must be processed and combined with other information sources and variables for both user‐related (e.g. people‐centric) and network‐related (e.g. network‐centric) activities in order to generate enriched data. The resulting location‐based analytics integrates positional, network, and other contextual information for mobility monitoring and to provide predictions that can be then exploited for optimizing user‐centric or network‐centric services through automation, actuation, and decision‐making in the smart environments and the infrastructure.
Figure 1.4 Pictorial description of the main steps for obtaining location‐based network‐centric (left) and user‐centric (right) analytics using positioning and localization data. The user positioning can include 5G localization.
In this way, Figure 1.4 illustrates a possible classification among user‐centric and network‐centric analytics in terms of their application. The user‐centric analytics considers improving the quality of life of people through analytics services. Use cases such as smart cities, smart buildings, and human mobility can be considered in the context of user‐centric analytics, where the user is the consumer of the analytics services. Differently, the network‐centric analytics focus on managing the network resources, e.g. for enhancing the 5G infrastructure itself for optimal use.
The location data can be in the format of real or virtual coordinates in 2D or 3D, as well as in the format of fingerprints that indirectly indicate the location of the people in a given environment. In certain scenarios, ground‐truth data is available for training AI/ML models, whereas in most real‐world setups (in‐the‐wild) the ground‐truth is partially available and the system relies on the previous learning or configuration.
There have been various techniques for preprocessing of such location data sources and making it useful for the utilization by the AI/ML models. The preprocessing steps include data integration and linking, data cleaning, data sampling, normalization, data encoding, and feature selection.
User‐centric (people‐centric) analytics leverage different AI/ML methods for predictions and insight generation, ranging from unsupervised clustering techniques to weakly or semi‐supervised models and fully supervised deep learning such as recurrent neural networks and generative adversarial networks. Real‐world datasets from small indoor setups to large regional scales can be leveraged for understanding the performance of these statistical methods, their applicability, and scalability. This book presents a set of AI/ML techniques and performance evaluation metrics and describes their advantages as well as their drawbacks for real‐world scenarios.
Below are two examples of real‐world scenarios for people‐centric analytics:
People movement: Crowd mobility analytics applications and COVID‐19 contact tracing
Vehicular mobility: Road safety through connected vehicular system applications and their standardization including Vulnerable Road Users clustering.
The accuracy and granularity of the location data are critical for people‐centric applications, e.g. for road safety or epidemic monitoring [813–15]. The benefits of the above‐listed applications are significant for improving the services for people using shared places, such as urban areas in cities, airports, and campus environments. Furthermore, the location‐based analytics applications are useful for optimizing transportation and safety in the vehicular domain.
The insights generated from user‐centric analytics are typically visualized in dashboards and consumed by either end‐users (e.g. student at a university campus) or decision makers (e.g. building management services or city administration). Thus, the behaviors of people can be better understood and potentially influenced thanks to the location‐based user‐centric analytics.
Network‐centric analytics have been classically based on UE traces: geolocated reports gathered by the network or the terminals themselves, as part of drive tests. The resulting analytics are applied to the analysis of the network performance for its planning, optimization, and failure management. In this context, one of the main challenges is the application of positioning information, classically 2D or 3D, to the approaches commonly followed in network management, that are typically based on events/alarms and time‐series (e.g. counters, KPIs) analysis [16, 17
