Cellular-connected UAV Trajectory Design

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Table of contents

Abstract
Abrege [Francais]
Acknowledgements
Contents
List of Figures
List of Tables
Acronyms
Notations
1 Introduction
1.1 Background and Motivations
1.1.1 Role Played by Node Localization
1.2 Aims and Objectives
1.3 Research Methodology and Assumptions
1.4 Outline of the Thesis
2 System Model
2.1 Introduction
2.2 Channel Models
2.2.1 Segmented Channel Model
2.2.2 Probabilistic Link Attenuation Models
2.3 Communication Performance Metric
2.3.1 SINR
2.3.2 Outage
2.3.3 UAV Energy Consumption
2.3.4 Communication Throughput
2.4 Trajectory Design in UAV-aided Wireless Networks
2.5 Cellular-connected UAV Trajectory Design
3 Map-based Placement and Trajectory Design in UAV-aided Wireless Networks
3.1 Introduction
3.2 Optimal Trajectory Design for an Intelligent Data Harvesting
3.2.1 Communication System Model
3.2.2 Joint Scheduling and Trajectory Optimization
3.2.3 LoS Probability Model Using Map Compression
3.2.4 Proposed Solution for Communication Trajectory Optimization
3.2.5 Iterative Algorithm
3.2.6 Proof of Convergence
3.2.7 Trajectory Initializing
3.3 Optimal UAV Relay Placement in LTE Networks
3.3.1 Communication Model
3.3.2 UAV Placement Optimization
3.4 Numerical Results
3.5 Conclusion
4 Active Learning for Channel Estimation: Map-based approaches
4.1 Introduction
4.2 UAV Kinematic Model
4.3 Learning Trajectory Design
4.3.1 Measurement Collection and Channel Learning
4.3.2 Optimization Problem
4.3.3 Dynamic Programming
4.4 Numerical Results
4.5 Conclusion
5 UAV-aided Radio Node Localization
5.1 System Model
5.2 User Localization and Channel Model Learning
5.2.1 PSO Techniques
5.2.2 Single User Case
5.2.3 Multi User Case
5.3 Trajectory Design for Accelerated Learning
5.3.1 Fisher Information Matrix
5.3.2 Cramer-Rao Bound Analysis
5.3.3 Trajectory Optimization
5.3.4 Greedy Trajectory Design
5.4 Numerical Results
5.5 Conclusion
6 3D City Map Reconstruction from Radio Measurements
6.1 Introduction
6.2 System Model
6.3 LoS vs. NLoS Classication
6.3.1 Target User Clustering
6.3.2 Optimization of Target User Group
6.3.3 Radio Propagation Parameter Learning
6.3.4 User Classication
6.4 3D City Map Reconstruction
6.4.1 Optimum UAV Altitude
6.5 Numerical Results
6.6 Conclusion
7 UAV Trajectory Design Under Cellular Connectivity Constraints
7.1 Introduction
7.2 System Model
7.2.1 Communication Model
7.2.2 Problem Formulation
7.3 Feasibility Check
7.4 Trajectory Optimization
7.5 Numerical Results
7.6 Conclusion
8 Experimental Studies
8.1 Introduction
8.2 System Design
8.2.1 UAV Design
8.2.2 OAI eNBs
8.2.3 Autonomous Placement
8.3 UAV Placement
8.3.1 Channel Parameter Estimation
8.3.2 Placement Algorithm
8.4 Experimental Results
8.5 Conclusion and Discussion
8.5.1 Design Improvement
8.5.2 Channel Models
9 Conclusion
Appendices
A Chapter 3 Appendices
A.1 The derivation of the average channel gain
A.2 Proof of Lemma 3.2.1
A.3 Proof of Proposition 3.2.1
B Chapter 5 Appendices
B.1 Proof of convergence for multi-user localization
B.2 Derivation of FIM
C The estimate of the map reconstruction error
D Chapter 7 Appendices
D.1 Proof of Proposition 7.2.1
D.2 Proof of Proposition 7.3.1
D.3 Proof of Lemma 7.4.1
Resume [Francais]