About the Editors xii
List of Contributors xiii
Preface xiv
Acknowledgments xx
List of Abbreviations xxi
1 Introduction and Fundamentals 1
Stefania 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, Gurkan Solmaz, Kostas Tsagkaris, and Henk Wymeersch
1.1 Introduction and Motivation 1
1.2 Use Cases, Verticals, and Applications 2
1.2.1 Emergency Calls 2
1.2.2 Public Safety and Natural Disasters 3
1.2.3 ITS and Autonomous Vehicles 4
1.2.4 IIoT, Construction Sites, and Mines 4
1.2.5 Commercial and Transport Hubs 5
1.2.6 Internet-of-Things 5
1.2.7 Education and Gaming 6
1.3 Fundamentals of Positioning and Navigation 7
1.3.1 Position-Dependent Measurements 7
1.3.2 Positioning Methods 8
1.3.3 AI/ML for Positioning 9
1.4 Fundamentals of Location-Based Analytics 10
1.5 Introduction to Architectural Principles 12
1.5.1 5G Architecture and Positioning 13
1.5.2 Location-Based Analytics Platform 13
1.6 Book Outline 16
Part I Positioning Enablers 19
2 Positioning Methods 21
Stefania Bartoletti, Carlos S. Alvarez-Merino, Raquel Barco, Hui Chen, Andrea Conti, Yannis Filippas, Domenico Giustiniano, Carlos A. Gomez 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 Wymeersch
2.1 Positioning as Parameter Estimation 22
2.1.1 The Snapshot Positioning Problem 22
2.1.2 Fisher Information and Bounds 24
2.1.3 Tracking and Location-Data Fusion 25
2.1.3.1 Practical Aspects 27
2.2 Device-Based Radio Positioning 28
2.2.1 Theoretical Foundations 28
2.2.1.1 Signal Model 28
2.2.1.2 Equivalent Fisher Information Matrix 29
2.2.1.3 Interpretation 30
2.2.2 Signal Processing Techniques 30
2.2.3 Example Results of 5G-Based Positioning in IIoT Scenarios 31
2.3 Device-Free Radio Localization 33
2.3.1 Theoretical Foundations 34
2.3.1.1 Signal Model 34
2.3.1.2 EFIM for DFL 35
2.3.1.3 Interpretation 36
2.3.2 Signal Processing Techniques 37
2.3.3 Experimental Results on 5G-Based DFL 38
2.4 AI/ML for Positioning 40
2.4.1 Fingerprinting Approach 41
2.4.2 Soft Information-Based Approach 44
2.4.3 AI/ML to Mitigate Practical Impairments 46
3 Standardization in 5G and 5G Advanced Positioning 51
Sara Modarres Razavi, Mythri Hunukumbure, and Domenico Giustiniano
3.1 Positioning Standardization Support Prior to 5G 51
3.1.1 GNSS and Real-Time Kinematics (RTK) GNSS Positioning 52
3.1.2 WiFi/Bluetooth-Based Positioning 55
3.1.3 Terrestrial Beacon System 56
3.1.4 Sensor Positioning 56
3.1.5 RAT-Dependent Positioning Prior to 5G 56
3.1.5.1 Enhanced CID (eCID) 57
3.1.5.2 Observed Time-Difference-of-Arrival (OTDoA) 58
3.1.5.3 Uplink Time-Difference-of-Arrival (UTDoA) 58
3.1.6 Internet of Things (IoT) Positioning 58
3.1.7 Other Non-3GPP Technologies 58
3.1.7.1 UWB 58
3.1.7.2 Fingerprinting 59
3.2 5G Positioning 59
3.2.1 5G Localization Architecture 60
3.2.2 Positioning Protocols 62
3.2.3 RAT-Dependent NR Positioning Technologies 64
3.2.3.1 Downlink-Based Solutions 64
3.2.3.2 Uplink-Based Solutions 64
3.2.3.3 Downlink- and Uplink-Based Solutions 64
3.2.4 Specific Positioning Signals 65
3.2.4.1 Downlink Positioning Reference Signal 66
3.2.4.2 Uplink Signal for Positioning 67
3.2.5 Positioning Measurements 68
3.3 Hybrid Positioning Technologies 69
3.3.1 Outdoor Fusion 69
3.3.2 Indoor Fusion 70
3.4 5G Advanced Positioning 71
4 Enablers Toward 6G Positioning and Sensing 75
Joerg Widmer, Henk Wymeersch, Stefania Bartoletti, Hui Chen, Andrea Conti, Nicolò Decarli, Fan Jiang, Barbara M. Masini, Flavio Morselli, Gianluca Torsoli, and Moe Z. Win
4.1 Integrated Sensing and Communication 76
4.1.1 ISAC Application: Joint Radar and Communication with Sidelink V2X 77
4.1.1.1 V2X and Its Sensing Potential 79
4.1.1.2 V2X Target Parameter Estimation and Signal Numerology 80
4.1.1.3 V2X Resource Allocation 81
4.1.2 ISAC Application: Human Activity Recognition and Person Identification 82
4.1.2.1 Beyond Positioning 82
4.1.2.2 System Aspects 83
4.1.2.3 Processing Chain 83
4.2 Reconfigurable Intelligent Surfaces for Positioning and Sensing 85
4.2.1 RIS Enabling and Enhancing Positioning 86
4.2.1.1 RIS Enabling Positioning 87
4.2.1.2 RIS Enhancing Positioning 87
4.2.1.3 Use Cases 88
4.2.2 RIS for Sensing 88
4.3 Advanced Methods 90
4.3.1 Model-Based Methods 90
4.3.2 AI-Based Methods 92
4.3.2.1 Use Case 93
5 Security, Integrity, and Privacy Aspects 99
Stefania Bartoletti, Giuseppe Bianchi, Nicola Blefari Melazzi, Domenico Garlisi, Danilo Orlando, Ivan Palama, and Sara Modarres Razavi
5.1 Location Privacy 100
5.1.1 Overview on the Privacy Implication 100
5.1.2 Identification and Authentication in Cellular Networks 102
5.1.3 IMSI Catching Attack 103
5.1.4 Enhanced Privacy Protection in 5G Networks 104
5.1.5 Location Privacy Algorithms 106
5.1.6 Location Privacy Considered Model 108
5.1.7 Location Privacy Tested Approach 108
5.2 Location Security 110
5.2.1 Location Security in 4G/5G Networks 112
5.2.2 Threat Models and Bounds 113
5.2.2.1 Formal Model 114
5.2.2.2 Error Model for the Spoofing Attack 115
5.2.2.3 Threat Model Example Case Study: Range-Based Localization Using RSSI 116
5.2.2.4 Error Bound Under Spoofing Attack 116
5.2.2.5 Case Study 117
5.3 3GPP Integrity Support 119
References 121
Part II Location-based Analytics and New Services 125
6 Location and Analytics for Verticals 127
Gurkan 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 Ropodi
6.1 People-Centric Analytics 127
6.1.1 Crowd Mobility Analytics 127
6.1.1.1 Introduction and RelatedWork 127
6.1.1.2 Example Experimental Results from Crowd Mobility Analytics: Group Inference 133
6.1.2 Flow Monitoring 135
6.1.2.1 Introduction and RelatedWork 135
6.1.2.2 Selected DL Approaches and Results for Trajectory Prediction 136
6.1.3 COVID--19 Contact Tracing 139
6.1.3.1 Introduction and RelatedWork 139
6.1.3.2 Selected Approach and Example Results from Contact Tracing 141
6.2 Localization in Road Safety Applications 142
6.2.1 Safety-Critical Use Cases and 5G Position-Related Requirements 143
6.2.1.1 Introduction and RelatedWork 143
6.2.1.2 Example Results for Safety-Critical Use Cases 144
6.2.2 Upper Layers Architecture in ETSI ITS Standard 145
6.2.2.1 Introduction and RelatedWork 145
6.2.2.2 Example Results for ITS 148
6.2.3 5G Automotive Association (5GAA) Activities 151
7 Location-Aware Network Management 157
Sergio 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 Tsagkaris
7.1 Introduction 157
7.2 Location-Aware Cellular Network Planning 161
7.2.1 What Is the Cellular Network Planning? 161
7.2.2 Why Is Localization Important in the Planning Phase? 162
7.2.3 Location-Aware Cellular Network Planning 164
7.2.4 Future Directions 164
7.3 Location-Aware Network Optimization 165
7.3.1 What Is the Cellular Network Optimization? 165
7.3.2 Why Is Location Information Important in Optimization? 166
7.3.3 Hybrid Clustering-Based Optimization of 5G Mobile Networks 167
7.3.3.1 Clustering Methods and Algorithmic Approach 167
7.3.3.2 Results and Conclusions 168
7.3.4 Location-Aware Capacity and Coverage Optimization 170
7.3.4.1 Dual-Connectivity Optimization 170
7.3.4.2 Results and Conclusions 171
7.3.5 SINR Prediction in Presence of Correlated Shadowing in Cellular Networks 172
7.3.5.1 SINR Prediction with Kriging 173
7.3.5.2 Results and Conclusions 175
7.3.5.3 Multi-user (MU) Scheduling Enhancement with Geolocation Information and Radio Environment Maps (REMs) 177
7.3.5.4 Results and Conclusions 178
7.3.6 Social-Aware Load Balancing System for Crowds in Cellular Networks 179
7.3.6.1 Social-Aware Fuzzy Logic Controller (FLC) Power Traffic Sharing (PTS) Control 179
7.3.6.2 Results and Conclusions 180
7.3.7 Future Directions 182
7.4 Location-Aware Network Failure Management 182
7.4.1 What Is the Cellular Network Failure Management? 182
7.4.2 Why Is Localization Important in Failure Management? 183
7.4.3 Contextualized Indicators 184
7.4.3.1 Contextualized Indicators 184
7.4.3.2 Results and Conclusions 186
7.4.4 Location-Based Deep Learning Techniques for Network Analysis 189
7.4.4.1 Synthetic mages and Deep-Learning Classification 189
7.4.4.2 Results and Conclusions 190
Part III Architectural Aspects for Localization and Analytics 197
8 Location-Based Analytics as a Service 199
Athina Ropodi, Giacomo Bernini, Aristotelis Margaris, and Kostas Tsagkaris
8.1 Motivation for a Dedicated Platform 199
8.2 Principles 201
8.2.1 Microservice Architectural Approach 201
8.2.2 Software Containerization 203
8.2.3 Mixed Kappa and Lambda Data Lake Approach 203
8.2.4 Designing an ML- and AI-Aware Solution 204
8.2.5 Abstracting Computation Optimization Processes 204
8.2.6 Automating Dependency Resolution and Linking 205
8.2.7 Achieving Low Latency End-to-End 205
8.2.8 Decoupling Processing and API Access 205
8.2.9 Offering Dynamic Resource Allocation 206
8.2.10 Decoupling Services and Security 206
8.3 Platform System Overview 206
8.4 Platform System Blocks Description 209
8.4.1 API Blocks 209
8.4.2 Control Blocks 210
8.4.3 Core Blocks 212
8.4.4 Virtualization Management and Infrastructure Blocks 214
8.5 Functional Decomposition 214
8.5.1 Data Collection Functions 215
8.5.2 Persistence and Message Queue Functions 216
8.5.3 Positioning and Analytics Functions 218
8.5.3.1 Positioning Functions 218
8.5.3.2 Analytics Functions 218
8.5.4 Security and Privacy Functions 219
8.5.4.1 Security Functions 219
8.5.4.2 Privacy Functions 221
8.5.5 Analytics API Functions 221
8.5.6 Control Functions 221
8.5.7 Management, Orchestration, and Virtualization Functions 222
8.6 SystemWorkflows and Data Schema Analysis 224
8.6.1 SystemWorkflows 224
8.6.1.1 Service Activation 224
8.6.1.2 Service Consumption 226
8.6.1.3 Southbound Data Collection 226
8.6.1.4 Positioning and Analytics Service Operation 228
8.6.2 Applicable Data Schema 231
8.6.2.1 GeoJSON Data Format 231
8.6.2.2 JSON SQL Table Schema Format 231
8.6.2.3 3GPP Location Input Data 232
8.7 Platform Implementation: Available Technologies 232
8.7.1 Access Control Module 234
8.7.2 Service Discovery Module 234
8.7.3 API Gateway and Service Subscription Module 234
8.7.4 Data Operations Controller 234
8.7.5 ML Pipeline Controller 235
8.7.6 ML Model Repository 235
8.7.7 Data Collection Module 235
8.7.8 Data Persistence Module 235
8.7.9 Message Queue 236
8.7.10 Virtualization layer 236
8.7.11 Management and Orchestration 237
9 Reference Standard Architectures 239
Giacomo Bernini, Aristotelis Margaris, Athina Ropodi, and Kostas Tsagkaris
9.1 Data Analytics in the 3GPP Architecture 239
9.1.1 Evolved Network Data Analytics in 3GPP R17 240
9.1.2 Mapping with Location Data Analytics 244
9.2 3GPP CAPIF 245
9.3 3GPP SEAL 246
9.4 ETSI NFV 248
9.4.1 Mapping with Location Analytics Functions Management 250
9.5 ETSI Zero Touch Network and Service Management (ZSM) 250
9.5.1 Mapping with Location Analytics Services Management 251
References 253
Index 255
