Positioning and Location-Based Analytics in 5G and Beyond

Cover
Title Page
Copyright
Contents
About the Editors
List of Contributors
Preface
Acknowledgments
List of Abbreviations
Chapter 1 Introduction and Fundamentals
	1.1 Introduction and Motivation
	1.2 Use Cases, Verticals, and Applications
		1.2.1 Emergency Calls
		1.2.2 Public Safety and Natural Disasters
		1.2.3 ITS and Autonomous Vehicles
		1.2.4 IIoT, Construction Sites, and Mines
		1.2.5 Commercial and Transport Hubs
		1.2.6 Internet‐of‐Things
		1.2.7 Education and Gaming
	1.3 Fundamentals of Positioning and Navigation
		1.3.1 Position‐Dependent Measurements
		1.3.2 Positioning Methods
		1.3.3 AI/ML for Positioning
	1.4 Fundamentals of Location‐Based Analytics
	1.5 Introduction to Architectural Principles
		1.5.1 5G Architecture and Positioning
		1.5.2 Location‐Based Analytics Platform
	1.6 Book Outline
	References
Part I Positioning Enablers
	Chapter 2 Positioning Methods
		2.1 Positioning as Parameter Estimation
			2.1.1 The Snapshot Positioning Problem
			2.1.2 Fisher Information and Bounds
			2.1.3 Tracking and Location‐Data Fusion
				2.1.3.1 Practical Aspects
		2.2 Device‐Based Radio Positioning
			2.2.1 Theoretical Foundations
				2.2.1.1 Signal Model
				2.2.1.2 Equivalent Fisher Information Matrix
				2.2.1.3 Interpretation
			2.2.2 Signal Processing Techniques
			2.2.3 Example Results of 5G‐Based Positioning in IIoT Scenarios
		2.3 Device‐Free Radio Localization
			2.3.1 Theoretical Foundations
				2.3.1.1 Signal Model
				2.3.1.2 EFIM for DFL
				2.3.1.3 Interpretation
			2.3.2 Signal Processing Techniques
			2.3.3 Experimental Results on 5G‐Based DFL
		2.4 AI/ML for Positioning
			2.4.1 Fingerprinting Approach
			2.4.2 Soft Information‐Based Approach
			2.4.3 AI/ML to Mitigate Practical Impairments
		References
	Chapter 3 Standardization in 5G and 5G Advanced Positioning
		3.1 Positioning Standardization Support Prior to 5G
			3.1.1 GNSS and Real‐Time Kinematics (RTK) GNSS Positioning
			3.1.2 WiFi/Bluetooth‐Based Positioning
			3.1.3 Terrestrial Beacon System
			3.1.4 Sensor Positioning
			3.1.5 RAT‐Dependent Positioning Prior to 5G
				3.1.5.1 Enhanced CID (eCID)
				3.1.5.2 Observed Time‐Difference‐of‐Arrival (OTDoA)
				3.1.5.3 Uplink Time‐Difference‐of‐Arrival (UTDoA)
			3.1.6 Internet of Things (IoT) Positioning
			3.1.7 Other Non‐3GPP Technologies
				3.1.7.1 UWB
				3.1.7.2 Fingerprinting
		3.2 5G Positioning
			3.2.1 5G Localization Architecture
			3.2.2 Positioning Protocols
			3.2.3 RAT‐Dependent NR Positioning Technologies
				3.2.3.1 Downlink‐Based Solutions
				3.2.3.2 Uplink‐Based Solutions
				3.2.3.3 Downlink‐ and Uplink‐Based Solutions
			3.2.4 Specific Positioning Signals
				3.2.4.1 Downlink Positioning Reference Signal
				3.2.4.2 Uplink Signal for Positioning
			3.2.5 Positioning Measurements
		3.3 Hybrid Positioning Technologies
			3.3.1 Outdoor Fusion
			3.3.2 Indoor Fusion
		3.4 5G Advanced Positioning
		References
	Chapter 4 Enablers Toward 6G Positioning and Sensing
		4.1 Integrated Sensing and Communication
			4.1.1 ISAC Application: Joint Radar and Communication with Sidelink V2X
				4.1.1.1 V2X and Its Sensing Potential
				4.1.1.2 V2X Target Parameter Estimation and Signal Numerology
				4.1.1.3 V2X Resource Allocation
			4.1.2 ISAC Application: Human Activity Recognition and Person Identification
				4.1.2.1 Beyond Positioning
				4.1.2.2 System Aspects
				4.1.2.3 Processing Chain (see Figure )
		4.2 Reconfigurable Intelligent Surfaces for Positioning and Sensing
			4.2.1 RIS Enabling and Enhancing Positioning
				4.2.1.1 RIS Enabling Positioning
				4.2.1.2 RIS Enhancing Positioning
				4.2.1.3 Use Cases
			4.2.2 RIS for Sensing
		4.3 Advanced Methods
			4.3.1 Model‐Based Methods
			4.3.2 AI‐Based Methods
				4.3.2.1 Use Case
		References
	Chapter 5 Security, Integrity, and Privacy Aspects
		5.1 Location Privacy
			5.1.1 Overview on the Privacy Implication
			5.1.2 Identification and Authentication in Cellular Networks
			5.1.3 IMSI Catching Attack
			5.1.4 Enhanced Privacy Protection in 5G Networks
			5.1.5 Location Privacy Algorithms
			5.1.6 Location Privacy Considered Model
			5.1.7 Location Privacy Tested Approach
		5.2 Location Security
			5.2.1 Location Security in 4G/5G Networks
			5.2.2 Threat Models and Bounds
				5.2.2.1 Formal Model
				5.2.2.2 Error Model for the Spoofing Attack
				5.2.2.3 Threat Model Example Case Study: Range‐Based Localization Using RSSI
				5.2.2.4 Error Bound Under Spoofing Attack
				5.2.2.5 Case Study
		5.3 3GPP Integrity Support
		References
Part II Location‐based Analytics and New Services
	Chapter 6 Location and Analytics for Verticals
		6.1 People‐Centric Analytics
			6.1.1 Crowd Mobility Analytics
				6.1.1.1 Introduction and Related Work
				6.1.1.2 Example Experimental Results from Crowd Mobility Analytics: Group Inference
			6.1.2 Flow Monitoring
				6.1.2.1 Introduction and Related Work
				6.1.2.2 Selected DL Approaches and Results for Trajectory Prediction
			6.1.3 COVID—19 Contact Tracing
				6.1.3.1 Introduction and Related Work
				6.1.3.2 Selected Approach and Example Results from Contact Tracing
		6.2 Localization in Road Safety Applications
			6.2.1 Safety‐Critical Use Cases and 5G Position‐Related Requirements
				6.2.1.1 Introduction and Related Work
				6.2.1.2 Example Results for Safety‐Critical Use Cases
			6.2.2 Upper Layers Architecture in ETSI ITS Standard
				6.2.2.1 Introduction and Related Work
				6.2.2.2 Example Results for ITS
			6.2.3 5G Automotive Association (5GAA) Activities
		References
	Chapter 7 Location‐Aware Network Management
		7.1 Introduction
		7.2 Location‐Aware Cellular Network Planning
			7.2.1 What Is the Cellular Network Planning?
			7.2.2 Why Is Localization Important in the Planning Phase?
			7.2.3 Location‐Aware Cellular Network Planning
			7.2.4 Future Directions
		7.3 Location‐Aware Network Optimization
			7.3.1 What Is the Cellular Network Optimization?
			7.3.2 Why Is Location Information Important in Optimization?
			7.3.3 Hybrid Clustering‐Based Optimization of 5G Mobile Networks
				7.3.3.1 Clustering Methods and Algorithmic Approach
				7.3.3.2 Results and Conclusions
			7.3.4 Location‐Aware Capacity and Coverage Optimization
				7.3.4.1 Dual‐Connectivity Optimization
				7.3.4.2 Results and Conclusions
			7.3.5 SINR Prediction in Presence of Correlated Shadowing in Cellular Networks
				7.3.5.1 SINR Prediction with Kriging
				7.3.5.2 Results and Conclusions
				7.3.5.3 Multi‐user (MU) Scheduling Enhancement with Geolocation Information and Radio Environment Maps (REMs)
				7.3.5.4 Results and Conclusions
			7.3.6 Social‐Aware Load Balancing System for Crowds in Cellular Networks
				7.3.6.1 Social‐Aware Fuzzy Logic Controller (FLC) Power Traffic Sharing (PTS) Control
				7.3.6.2 Results and Conclusions
			7.3.7 Future Directions
		7.4 Location‐Aware Network Failure Management
			7.4.1 What Is the Cellular Network Failure Management?
			7.4.2 Why Is Localization Important in Failure Management?
			7.4.3 Contextualized Indicators
				7.4.3.1 Contextualized Indicators
				7.4.3.2 Results and Conclusions
			7.4.4 Location‐Based Deep Learning Techniques for Network Analysis
				7.4.4.1 Synthetic mages and Deep‐Learning Classification
				7.4.4.2 Results and Conclusions
		References
Part III Architectural Aspects for Localization and Analytics
	Chapter 8 Location‐Based Analytics as a Service
		8.1 Motivation for a Dedicated Platform
		8.2 Principles
			8.2.1 Microservice Architectural Approach
			8.2.2 Software Containerization
			8.2.3 Mixed Kappa and Lambda Data Lake Approach
			8.2.4 Designing an ML‐ and AI‐Aware Solution
			8.2.5 Abstracting Computation Optimization Processes
			8.2.6 Automating Dependency Resolution and Linking
			8.2.7 Achieving Low Latency End‐to‐End
			8.2.8 Decoupling Processing and API Access
			8.2.9 Offering Dynamic Resource Allocation
			8.2.10 Decoupling Services and Security
		8.3 Platform System Overview
		8.4 Platform System Blocks Description
			8.4.1 API Blocks
			8.4.2 Control Blocks
			8.4.3 Core Blocks
			8.4.4 Virtualization Management and Infrastructure Blocks
		8.5 Functional Decomposition
			8.5.1 Data Collection Functions
			8.5.2 Persistence and Message Queue Functions
			8.5.3 Positioning and Analytics Functions
				8.5.3.1 Positioning Functions
				8.5.3.2 Analytics Functions
			8.5.4 Security and Privacy Functions
				8.5.4.1 Security Functions
				8.5.4.2 Privacy Functions
			8.5.5 Analytics API Functions
			8.5.6 Control Functions
			8.5.7 Management, Orchestration, and Virtualization Functions
		8.6 System Workflows and Data Schema Analysis
			8.6.1 System Workflows
				8.6.1.1 Service Activation
				8.6.1.2 Service Consumption
				8.6.1.3 Southbound Data Collection
				8.6.1.4 Positioning and Analytics Service Operation
			8.6.2 Applicable Data Schema
				8.6.2.1 GeoJSON Data Format
				8.6.2.2 JSON SQL Table Schema Format
				8.6.2.3 3GPP Location Input Data
		8.7 Platform Implementation: Available Technologies
			8.7.1 Access Control Module
			8.7.2 Service Discovery Module
			8.7.3 API Gateway and Service Subscription Module
			8.7.4 Data Operations Controller
			8.7.5 ML Pipeline Controller
			8.7.6 ML Model Repository
			8.7.7 Data Collection Module
			8.7.8 Data Persistence Module
			8.7.9 Message Queue
			8.7.10 Virtualization layer
			8.7.11 Management and Orchestration
		References
	Chapter 9 Reference Standard Architectures
		9.1 Data Analytics in the 3GPP Architecture
			9.1.1 Evolved Network Data Analytics in 3GPP R17
			9.1.2 Mapping with Location Data Analytics
		9.2 3GPP CAPIF
		9.3 3GPP SEAL
		9.4 ETSI NFV
			9.4.1 Mapping with Location Analytics Functions Management
		9.5 ETSI Zero Touch Network and Service Management (ZSM)
			9.5.1 Mapping with Location Analytics Services Management
		References
Index
EULA