Positioning and Location-based Analytics in 5G and Beyond

AvStefania Bartoletti,Stefania Bartoletti

Inbunden, Engelska, 2023

1 331 kr

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Beskrivning

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 professionalsHigh-level insights into 5G and its future evolutionIn-depth coverage of subjects such as positioning enablers, location-aware network management, reference standard architectures, and morePositioning 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.

Produktinformation

Utgivningsdatum:2023-09-28
Mått:152 x 229 x 18 mm
Vikt:558 g
Format:Inbunden
Språk:Engelska
Antal sidor:288
Förlag:John Wiley & Sons Inc
ISBN:9781119911432

Utforska kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik

Mer om författaren

Stefania Bartoletti, PhD, is an Assistant Professor with the University of Rome Tor Vergata, Italy, and member of the National Inter-University Consortium for Telecommunications (CNIT). She has received research funding from the European Commission as a Marie-Skłodowska Curie Global Fellow and as coordinator of the project LOCUS. Nicola Blefari Melazzi, PhD, is a Professor with 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.

Innehållsförteckning

About the Editors xiiList of Contributors xiiiPreface xivAcknowledgments xxList of Abbreviations xxi1 Introduction and Fundamentals 1Stefania Bartoletti, Eduardo Baena, Raquel Barco, Giacomo Bernini, Nicola Blefari Melazzi, Hui Chen, Sergio Fortes, Domenico Giustiniano, Mythri Hunukumbure, Fan Jiang, Emil J. Khatib, Oluwatayo Kolawole, Aristotelis Margaris, Sara Modarres Razavi, Athina Ropodi, Gürkan Solmaz, Kostas Tsagkaris, and Henk Wymeersch1.1 Introduction and Motivation 11.2 Use Cases, Verticals, and Applications 21.2.1 Emergency Calls 21.2.2 Public Safety and Natural Disasters 31.2.3 ITS and Autonomous Vehicles 41.2.4 IIoT, Construction Sites, and Mines 41.2.5 Commercial and Transport Hubs 51.2.6 Internet-of-Things 51.2.7 Education and Gaming 61.3 Fundamentals of Positioning and Navigation 71.3.1 Position-Dependent Measurements 71.3.2 Positioning Methods 81.3.3 AI/ML for Positioning 91.4 Fundamentals of Location-Based Analytics 101.5 Introduction to Architectural Principles 121.5.1 5G Architecture and Positioning 131.5.2 Location-Based Analytics Platform 131.6 Book Outline 16Part I Positioning Enablers 192 Positioning Methods 21Stefania Bartoletti, Carlos S. Álvarez-Merino, Raquel Barco, Hui Chen, Andrea Conti, Yannis Filippas, Domenico Giustiniano, Carlos A. Gómez Vega, Mythri Hunukumbure, Fan Jiang, Emil J. Khatib, Oluwatayo Y. Kolawole, Flavio Morselli, Sara Modarres Razavi, Athina Ropodi, Joerg Widmer, Moe Z. Win, and Henk Wymeersch2.1 Positioning as Parameter Estimation 222.1.1 The Snapshot Positioning Problem 222.1.2 Fisher Information and Bounds 242.1.3 Tracking and Location-Data Fusion 252.1.3.1 Practical Aspects 272.2 Device-Based Radio Positioning 282.2.1 Theoretical Foundations 282.2.1.1 Signal Model 282.2.1.2 Equivalent Fisher Information Matrix 292.2.1.3 Interpretation 302.2.2 Signal Processing Techniques 302.2.3 Example Results of 5G-Based Positioning in IIoT Scenarios 312.3 Device-Free Radio Localization 332.3.1 Theoretical Foundations 342.3.1.1 Signal Model 342.3.1.2 EFIM for DFL 352.3.1.3 Interpretation 362.3.2 Signal Processing Techniques 372.3.3 Experimental Results on 5G-Based DFL 382.4 AI/ML for Positioning 402.4.1 Fingerprinting Approach 412.4.2 Soft Information-Based Approach 442.4.3 AI/ML to Mitigate Practical Impairments 463 Standardization in 5G and 5G Advanced Positioning 51Sara Modarres Razavi, Mythri Hunukumbure, and Domenico Giustiniano3.1 Positioning Standardization Support Prior to 5G 513.1.1 GNSS and Real-Time Kinematics (RTK) GNSS Positioning 523.1.2 WiFi/Bluetooth-Based Positioning 553.1.3 Terrestrial Beacon System 563.1.4 Sensor Positioning 563.1.5 RAT-Dependent Positioning Prior to 5G 563.1.5.1 Enhanced CID (eCID) 573.1.5.2 Observed Time-Difference-of-Arrival (OTDoA) 583.1.5.3 Uplink Time-Difference-of-Arrival (UTDoA) 583.1.6 Internet of Things (IoT) Positioning 583.1.7 Other Non-3GPP Technologies 583.1.7.1 UWB 583.1.7.2 Fingerprinting 593.2 5G Positioning 593.2.1 5G Localization Architecture 603.2.2 Positioning Protocols 623.2.3 RAT-Dependent NR Positioning Technologies 643.2.3.1 Downlink-Based Solutions 643.2.3.2 Uplink-Based Solutions 643.2.3.3 Downlink- and Uplink-Based Solutions 643.2.4 Specific Positioning Signals 653.2.4.1 Downlink Positioning Reference Signal 663.2.4.2 Uplink Signal for Positioning 673.2.5 Positioning Measurements 683.3 Hybrid Positioning Technologies 693.3.1 Outdoor Fusion 693.3.2 Indoor Fusion 703.4 5G Advanced Positioning 714 Enablers Toward 6G Positioning and Sensing 75Joerg Widmer, Henk Wymeersch, Stefania Bartoletti, Hui Chen, Andrea Conti, Nicolò Decarli, Fan Jiang, Barbara M. Masini, Flavio Morselli, Gianluca Torsoli, and Moe Z. Win4.1 Integrated Sensing and Communication 764.1.1 ISAC Application: Joint Radar and Communication with Sidelink V2X 774.1.1.1 V2X and Its Sensing Potential 794.1.1.2 V2X Target Parameter Estimation and Signal Numerology 804.1.1.3 V2X Resource Allocation 814.1.2 ISAC Application: Human Activity Recognition and Person Identification 824.1.2.1 Beyond Positioning 824.1.2.2 System Aspects 834.1.2.3 Processing Chain 834.2 Reconfigurable Intelligent Surfaces for Positioning and Sensing 854.2.1 RIS Enabling and Enhancing Positioning 864.2.1.1 RIS Enabling Positioning 874.2.1.2 RIS Enhancing Positioning 874.2.1.3 Use Cases 884.2.2 RIS for Sensing 884.3 Advanced Methods 904.3.1 Model-Based Methods 904.3.2 AI-Based Methods 924.3.2.1 Use Case 935 Security, Integrity, and Privacy Aspects 99Stefania Bartoletti, Giuseppe Bianchi, Nicola Blefari Melazzi, Domenico Garlisi, Danilo Orlando, Ivan Palamá, and Sara Modarres Razavi5.1 Location Privacy 1005.1.1 Overview on the Privacy Implication 1005.1.2 Identification and Authentication in Cellular Networks 1025.1.3 IMSI Catching Attack 1035.1.4 Enhanced Privacy Protection in 5G Networks 1045.1.5 Location Privacy Algorithms 1065.1.6 Location Privacy Considered Model 1085.1.7 Location Privacy Tested Approach 1085.2 Location Security 1105.2.1 Location Security in 4G/5G Networks 1125.2.2 Threat Models and Bounds 1135.2.2.1 Formal Model 1145.2.2.2 Error Model for the Spoofing Attack 1155.2.2.3 Threat Model Example Case Study: Range-Based Localization Using RSSI 1165.2.2.4 Error Bound Under Spoofing Attack 1165.2.2.5 Case Study 1175.3 3GPP Integrity Support 119References 121Part II Location-based Analytics and New Services 1256 Location and Analytics for Verticals 127Gürkan Solmaz, Raquel Barco, Stefania Bartoletti, Andrea Conti, Nicolò Decarli, Yannis Filippas, Andrea Giani, Emil J. Khatib, Oluwatayo Y. Kolawole, Tomasz Mach, Barbara M. Masini, and Athina Ropodi6.1 People-Centric Analytics 1276.1.1 Crowd Mobility Analytics 1276.1.1.1 Introduction and RelatedWork 1276.1.1.2 Example Experimental Results from Crowd Mobility Analytics: Group Inference 1336.1.2 Flow Monitoring 1356.1.2.1 Introduction and RelatedWork 1356.1.2.2 Selected DL Approaches and Results for Trajectory Prediction 1366.1.3 COVID--19 Contact Tracing 1396.1.3.1 Introduction and RelatedWork 1396.1.3.2 Selected Approach and Example Results from Contact Tracing 1416.2 Localization in Road Safety Applications 1426.2.1 Safety-Critical Use Cases and 5G Position-Related Requirements 1436.2.1.1 Introduction and RelatedWork 1436.2.1.2 Example Results for Safety-Critical Use Cases 1446.2.2 Upper Layers Architecture in ETSI ITS Standard 1456.2.2.1 Introduction and RelatedWork 1456.2.2.2 Example Results for ITS 1486.2.3 5G Automotive Association (5GAA) Activities 1517 Location-Aware Network Management 157Sergio Fortes, Eduardo Baena, Raquel Barco, Isabel de la Bandera, Zwi Altman, Luca Chiaraviglio, Wassim B. Chikha, Sana B. Jemaa, Yannis Filippas, Imed Hadj-Kacem, Aristotelis Margaris, Marie Masson, and Kostas Tsagkaris7.1 Introduction 1577.2 Location-Aware Cellular Network Planning 1617.2.1 What Is the Cellular Network Planning? 1617.2.2 Why Is Localization Important in the Planning Phase? 1627.2.3 Location-Aware Cellular Network Planning 1647.2.4 Future Directions 1647.3 Location-Aware Network Optimization 1657.3.1 What Is the Cellular Network Optimization? 1657.3.2 Why Is Location Information Important in Optimization? 1667.3.3 Hybrid Clustering-Based Optimization of 5G Mobile Networks 1677.3.3.1 Clustering Methods and Algorithmic Approach 1677.3.3.2 Results and Conclusions 1687.3.4 Location-Aware Capacity and Coverage Optimization 1707.3.4.1 Dual-Connectivity Optimization 1707.3.4.2 Results and Conclusions 1717.3.5 SINR Prediction in Presence of Correlated Shadowing in Cellular Networks 1727.3.5.1 SINR Prediction with Kriging 1737.3.5.2 Results and Conclusions 1757.3.5.3 Multi-user (MU) Scheduling Enhancement with Geolocation Information and Radio Environment Maps (REMs) 1777.3.5.4 Results and Conclusions 1787.3.6 Social-Aware Load Balancing System for Crowds in Cellular Networks 1797.3.6.1 Social-Aware Fuzzy Logic Controller (FLC) Power Traffic Sharing (PTS) Control 1797.3.6.2 Results and Conclusions 1807.3.7 Future Directions 1827.4 Location-Aware Network Failure Management 1827.4.1 What Is the Cellular Network Failure Management? 1827.4.2 Why Is Localization Important in Failure Management? 1837.4.3 Contextualized Indicators 1847.4.3.1 Contextualized Indicators 1847.4.3.2 Results and Conclusions 1867.4.4 Location-Based Deep Learning Techniques for Network Analysis 1897.4.4.1 Synthetic mages and Deep-Learning Classification 1897.4.4.2 Results and Conclusions 190Part III Architectural Aspects for Localization and Analytics 1978 Location-Based Analytics as a Service 199Athina Ropodi, Giacomo Bernini, Aristotelis Margaris, and Kostas Tsagkaris8.1 Motivation for a Dedicated Platform 1998.2 Principles 2018.2.1 Microservice Architectural Approach 2018.2.2 Software Containerization 2038.2.3 Mixed Kappa and Lambda Data Lake Approach 2038.2.4 Designing an ML- and AI-Aware Solution 2048.2.5 Abstracting Computation Optimization Processes 2048.2.6 Automating Dependency Resolution and Linking 2058.2.7 Achieving Low Latency End-to-End 2058.2.8 Decoupling Processing and API Access 2058.2.9 Offering Dynamic Resource Allocation 2068.2.10 Decoupling Services and Security 2068.3 Platform System Overview 2068.4 Platform System Blocks Description 2098.4.1 API Blocks 2098.4.2 Control Blocks 2108.4.3 Core Blocks 2128.4.4 Virtualization Management and Infrastructure Blocks 2148.5 Functional Decomposition 2148.5.1 Data Collection Functions 2158.5.2 Persistence and Message Queue Functions 2168.5.3 Positioning and Analytics Functions 2188.5.3.1 Positioning Functions 2188.5.3.2 Analytics Functions 2188.5.4 Security and Privacy Functions 2198.5.4.1 Security Functions 2198.5.4.2 Privacy Functions 2218.5.5 Analytics API Functions 2218.5.6 Control Functions 2218.5.7 Management, Orchestration, and Virtualization Functions 2228.6 SystemWorkflows and Data Schema Analysis 2248.6.1 SystemWorkflows 2248.6.1.1 Service Activation 2248.6.1.2 Service Consumption 2268.6.1.3 Southbound Data Collection 2268.6.1.4 Positioning and Analytics Service Operation 2288.6.2 Applicable Data Schema 2318.6.2.1 GeoJSON Data Format 2318.6.2.2 JSON SQL Table Schema Format 2318.6.2.3 3GPP Location Input Data 2328.7 Platform Implementation: Available Technologies 2328.7.1 Access Control Module 2348.7.2 Service Discovery Module 2348.7.3 API Gateway and Service Subscription Module 2348.7.4 Data Operations Controller 2348.7.5 ML Pipeline Controller 2358.7.6 ML Model Repository 2358.7.7 Data Collection Module 2358.7.8 Data Persistence Module 2358.7.9 Message Queue 2368.7.10 Virtualization layer 2368.7.11 Management and Orchestration 2379 Reference Standard Architectures 239Giacomo Bernini, Aristotelis Margaris, Athina Ropodi, and Kostas Tsagkaris9.1 Data Analytics in the 3GPP Architecture 2399.1.1 Evolved Network Data Analytics in 3GPP R17 2409.1.2 Mapping with Location Data Analytics 2449.2 3GPP CAPIF 2459.3 3GPP SEAL 2469.4 ETSI NFV 2489.4.1 Mapping with Location Analytics Functions Management 2509.5 ETSI Zero Touch Network and Service Management (ZSM) 2509.5.1 Mapping with Location Analytics Services Management 251References 253Index 255