Zhang / Song / Han Unmanned Aerial Vehicle Applications over Cellular Networks for 5G and Beyond

1. Auflage 2019
ISBN: 978-3-030-33039-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 231 Seiten

Reihe: Wireless Networks

This book discusses how to plan the time-variant placements of the UAVs served as base station (BS)/relay, which is very challenging due to the complicated 3D propagation environments, as well as many other practical constraints such as power and flying speed. Spectrum sharing with existing cellular networks is also investigated in this book. The emerging unmanned aerial vehicles (UAVs) have been playing an increasing role in the military, public, and civil applications. To seamlessly integrate UAVs into future cellular networks, this book will cover two main scenarios of UAV applications as follows. The first type of applications can be referred to as UAV Assisted Cellular Communications.
Second type of application is to exploit UAVs for sensing purposes, such as smart agriculture, security monitoring, and traffic surveillance. Due to the limited computation capability of UAVs, the real-time sensory data needs to be transmitted to the BS for real-time data processing.  The cellular networks are necessarily committed to support the data transmission for UAVs, which the authors refer to as Cellular assisted UAV Sensing. To support real-time sensing streaming, the authors design joint sensing and communication protocols, develop novel beamforming and estimation algorithms, and study efficient distributed resource optimization methods.
This book targets signal processing engineers, computer and information scientists, applied mathematicians and statisticians, as well as systems engineers to carve out the role that analytical and experimental engineering has to play in UAV research and development. Undergraduate students, industry managers, government research agency workers and general readers interested in the fields of communications and networks will also want to read this book.

Hongliang Zhang received the B.S. and PhD degrees at School of Electrical Engineering and Computer Science in Peking University, in 2014 and 2019, respectively. Currently, he is a Postdoctoral fellow in Electrical and Computer Engineering Department as well as Computer Science Department at the University of Houston, Texas. His current research interest includes cooperative communications, Internet-of-Things networks, hypergraph theory, and optimization theory. He has also served as a TPC Member for Globecom 2016, ICC 2016, ICCC 2017, ICC 2018, Globecom 2018, ICCC 2019, and Globecom 2019.
Lingyang Song received his PhD from the University of York, UK, in 2007, where he received the K. M. Stott Prize for excellent research. He worked as a postdoctoral research fellow at the University of Oslo, Norway, and Harvard University, until rejoining Philips Research UK in March 2008. In May 2009, he joined the School of Electronics Engineering and Computer Science, Peking University, China, as a full professor. His main research interests include cooperative and cognitive communications, physical layer security, and wireless ad hoc/sensor networks. He published extensively, wrote 6 text books, and is co-inventor of a number of patents (standard contributions). He received 9 paper awards in IEEE journal and conferences including IEEE JSAC 2016, IEEE WCNC 2012, ICC 2014, Globecom 2014, ICC 2015, etc. He is currently on the Editorial Board of IEEE Transactions on Wireless Communications and Journal of Network and Computer Applications. He served as the TPC co-chairs for the International Conference on Ubiquitous and Future Networks (ICUFN2011/2012), symposium co-chairs in the International Wireless Communications and Mobile Computing Conference (IWCMC 2009/2010), IEEE International Conference on Communication Technology (ICCT2011), and IEEE International Conference on Communications (ICC 2014, 2015). He is the recipient of 2012 IEEE Asia Pacific (AP) Young Researcher Award. Dr. Song is a senior member of IEEE, and IEEE ComSoc distinguished lecturer since 2015.
Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor in Boise State University, Idaho. Currently, he is a Professor in Electrical and Computer Engineering Department as well as Computer Science Department at the University of Houston, Texas. His research interests include wireless resource allocation and management, wireless communications and networking, game theory, wireless multimedia, security, and smart grid communication. Dr. Han received an NSF Career Award in 2010, the Fred W. Ellersick Prize of the IEEE Communication Society in 2011, the EURASIP Best Paper Award for the Journal on Advances in Signal Processing in 2015, several best paper awards in IEEE conferences, and is currently an IEEE Communications Society Distinguished Lecturer. Dr. Han is top 1% highly cited researcher according to Web of Science 2017. Dr. Han published the following related book:   Zhu Han, Mingyi Hong, and Dan Wang, Signal Processing and Networking for Big Data Applications, Cambridge University Press, UK, 2017.

Autoren/Hrsg.

Zhang, Hongliang

Song, Lingyang

Han, Zhu

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Acronyms;10
4;1 Overview of 5G and Beyond Communications;12
4.1;1.1 Background and Requirements;12
4.2;1.2 UAV Applications;13
4.2.1;1.2.1 Flying Infrastructure;14
4.2.2;1.2.2 Aerial Internet-of-Things;15
4.3;1.3 Current State-of-the-art;17
4.3.1;1.3.1 Channel Model;17
4.3.1.1;1.3.1.1 Elevation Angle-Based Model;17
4.3.1.2;1.3.1.2 3GPP Model;18
4.3.2;1.3.2 Aerial Access Networks;20
4.3.3;1.3.3 Aerial IoT Networks;24
4.3.4;1.3.4 Propulsion and Mobility Model;32
4.4;References;36
5;2 Basic Theoretical Background;37
5.1;2.1 Brief Introductions to Optimization Theory;37
5.1.1;2.1.1 Continuous Optimization;38
5.1.1.1;2.1.1.1 Convex Optimization Problem;38
5.1.1.2;2.1.1.2 Non-convex Optimization Problem;40
5.1.2;2.1.2 Integer Optimization;41
5.1.2.1;2.1.2.1 Branch-and-Bound Method;42
5.1.2.2;2.1.2.2 Bound Calculation;43
5.2;2.2 Basics of Game Theory;44
5.2.1;2.2.1 Basic Concepts;44
5.2.1.1;2.2.1.1 Definition of a Game;44
5.2.1.2;2.2.1.2 Nash Equilibrium;45
5.2.1.3;2.2.1.3 Examples of Game Theory;46
5.2.2;2.2.2 Contract Theory;47
5.2.2.1;2.2.2.1 Classification;47
5.2.2.2;2.2.2.2 Models and Reward Design;50
5.3;2.3 Related Machine Learning Technologies;52
5.3.1;2.3.1 Classical Machine Learning;52
5.3.1.1;2.3.1.1 Supervised Learning;53
5.3.1.2;2.3.1.2 Unsupervised Learning;54
5.3.1.3;2.3.1.3 Machine Learning Algorithm Design;55
5.3.2;2.3.2 Deep Learning;55
5.3.2.1;2.3.2.1 Basics of Neural Networks;56
5.3.2.2;2.3.2.2 Back-Propagation Algorithm;59
5.3.3;2.3.3 Reinforcement Learning;62
5.3.3.1;2.3.3.1 Markov Decision Processes;62
5.3.3.2;2.3.3.2 Reinforcement Learning Methods;65
5.4;References;70
6;3 UAV Assisted Cellular Communications;71
6.1;3.1 UAVs Serving as Base Stations;71
6.1.1;3.1.1 System Model;73
6.1.1.1;3.1.1.1 Mobility and Energy Consumption;74
6.1.1.2;3.1.1.2 Wireless Downlink Model;74
6.1.1.3;3.1.1.3 The Utility of the UAV Operators;75
6.1.1.4;3.1.1.4 Cost of the MBS Manager;76
6.1.1.5;3.1.1.5 Contract Formulation;77
6.1.2;3.1.2 Optimal Contract Design;78
6.1.2.1;3.1.2.1 Basic Properties;79
6.1.2.2;3.1.2.2 Optimal Pricing Strategy;81
6.1.2.3;3.1.2.3 Optimal Quality Assignment Problem;84
6.1.2.4;3.1.2.4 Algorithm for the MBS Optimal Contract;86
6.1.2.5;3.1.2.5 Socially Optimal Contract;89
6.1.3;3.1.3 Theoretical Analysis and Discussions;90
6.1.3.1;3.1.3.1 The Impact of the Height on the UAV Types;90
6.1.3.2;3.1.3.2 The Impact of the UAV Types on the Optimal Revenue;92
6.1.4;3.1.4 Simulation Results;92
6.1.4.1;3.1.4.1 Simulation Setups;93
6.1.4.2;3.1.4.2 Simulation Results and Discussions;93
6.1.5;3.1.5 Summary;98
6.2;3.2 UAVs Serving as Relays;99
6.2.1;3.2.1 System Model and Problem Formulation;99
6.2.2;3.2.2 Power and Trajectory Optimization;103
6.2.2.1;3.2.2.1 Trajectory Design;104
6.2.2.2;3.2.2.2 Power Control;105
6.2.3;3.2.3 Simulation Results;106
6.2.4;3.2.4 Summary;108
6.3;References;108
7;4 Cellular Assisted UAV Sensing;111
7.1;4.1 Cellular Internet of UAVs;111
7.1.1;4.1.1 System Model;112
7.1.1.1;4.1.1.1 UAV Sensing;112
7.1.1.2;4.1.1.2 UAV Transmission;113
7.1.2;4.1.2 Problem Formulation;114
7.1.2.1;4.1.2.1 Energy Consumption;114
7.1.2.2;4.1.2.2 Problem Description;115
7.1.3;4.1.3 Energy Efficiency Maximization Algorithm;115
7.1.3.1;4.1.3.1 UAV Sensing Optimization;116
7.1.3.2;4.1.3.2 UAV Transmission Optimization;117
7.1.3.3;4.1.3.3 Overall Algorithm;119
7.1.4;4.1.4 Simulation Results;119
7.1.5;4.1.5 Summary;121
7.2;4.2 Cooperative Cellular Internet of UAVs;121
7.2.1;4.2.1 System Model;122
7.2.1.1;4.2.1.1 UAV Sensing;123
7.2.1.2;4.2.1.2 UAV Transmission;124
7.2.1.3;4.2.1.3 Task Completion Time;125
7.2.2;4.2.2 Sense-and-Send Protocol;125
7.2.3;4.2.3 Problem Formulation;128
7.2.3.1;4.2.3.1 Problem Description;128
7.2.3.2;4.2.3.2 Problem Decomposition;129
7.2.3.3;4.2.3.3 Iterative Algorithm Description;130
7.2.4;4.2.4 Iterative Trajectory, Sensing, and Scheduling Optimization Algorithm;131
7.2.4.1;4.2.4.1 Trajectory Optimization;131
7.2.4.2;4.2.4.2 Sensing Location Optimization;134
7.2.4.3;4.2.4.3 UAV Scheduling;137
7.2.4.4;4.2.4.4 Performance Analysis;138
7.2.5;4.2.5 Simulation Results;141
7.2.6;4.2.6 Summary;147
7.3;4.3 UAV-to-X Communications;148
7.3.1;4.3.1 System Model;149
7.3.1.1;4.3.1.1 Scenario Description;149
7.3.1.2;4.3.1.2 Data Transmission;151
7.3.1.3;4.3.1.3 Channel Model;152
7.3.2;4.3.2 Cooperative UAV Sense-and-Send Protocol;154
7.3.3;4.3.3 Problem Formulation;156
7.3.3.1;4.3.3.1 Joint Subchannel Allocation and UAV Speed Optimization Problem Formulation;156
7.3.3.2;4.3.3.2 Problem Decomposition;158
7.3.4;4.3.4 Joint Subchannel Allocation and UAV SpeedOptimization;159
7.3.4.1;4.3.4.1 U2N and CU Subchannel Allocation Algorithm;159
7.3.4.2;4.3.4.2 U2U Subchannel Allocation Algorithm;161
7.3.4.3;4.3.4.3 UAV Speed Optimization Algorithm;165
7.3.4.4;4.3.4.4 Iterative Subchannel Allocation and UAV Speed Optimization Algorithm;168
7.3.5;4.3.5 Simulation Results;170
7.3.6;4.3.6 Summary;175
7.4;4.4 Reinforcement Learning for the Cellular Internet of UAVs;175
7.4.1;4.4.1 System Model;176
7.4.1.1;4.4.1.1 UAV Sensing;177
7.4.1.2;4.4.1.2 UAV Transmission;177
7.4.2;4.4.2 Decentralized Sense-and-Send Protocol;178
7.4.2.1;4.4.2.1 Sense-and-Send Cycle;178
7.4.2.2;4.4.2.2 Uplink Subchannel Allocation Mechanism;180
7.4.3;4.4.3 Sense-and-Send Protocol Analysis;181
7.4.3.1;4.4.3.1 Outer Markov Chain of UAV Sensing;181
7.4.3.2;4.4.3.2 Inner Markov Chain of UAV Transmission;182
7.4.3.3;4.4.3.3 Analysis on the Data Rate;185
7.4.4;4.4.4 Decentralized Trajectory Design;186
7.4.4.1;4.4.4.1 UAV Trajectory Design Problem;186
7.4.4.2;4.4.4.2 Reinforcement Learning Framework;188
7.4.4.3;4.4.4.3 Enhanced Multi-UAV Q-Learning Algorithm for UAV Trajectory Design;191
7.4.4.4;4.4.4.4 Analysis of Reinforcement Learning Algorithms;194
7.4.5;4.4.5 Simulation Results;196
7.4.6;4.4.6 Summary;201
7.5;4.5 Applications of the Cellular Internet of UAVs;201
7.5.1;4.5.1 Preliminaries of UAV Sensing System;203
7.5.1.1;4.5.1.1 System Overview;203
7.5.1.2;4.5.1.2 Dataset Description;204
7.5.1.3;4.5.1.3 Data Reliability;205
7.5.1.4;4.5.1.4 Selection of Model Parameters;206
7.5.2;4.5.2 Fine-Grained AQI Distribution Model;206
7.5.2.1;4.5.2.1 Physical Particle Dispersion Model;206
7.5.2.2;4.5.2.2 Neural Network Model;207
7.5.2.3;4.5.2.3 GPM-NN Model;208
7.5.3;4.5.3 Adaptive AQI Monitoring Algorithm;212
7.5.3.1;4.5.3.1 Complete Monitoring;213
7.5.3.2;4.5.3.2 Selective Monitoring;213
7.5.3.3;4.5.3.3 Trajectory Optimization;215
7.5.4;4.5.4 Application Scenario I: Performance Analysis in Horizontal Open Space;216
7.5.4.1;4.5.4.1 Scenario Description;216
7.5.4.2;4.5.4.2 Performance Analysis;217
7.5.5;4.5.5 Application Scenario II: Performance Analysis in Vertical Enclosed Space;222
7.5.5.1;4.5.5.1 Scenario Description;222
7.5.5.2;4.5.5.2 Performance Analysis;223
7.5.6;4.5.6 Summary;226
7.6;References;227