Unmanned Aerial Vehicle Applications over Cellular Networks for 5G and Beyond (eBook)

Hongliang Zhang, Lingyang Song, Zhu Han (Autoren)

eBook Download: PDF

2019 | 1st ed. 2020
X, 221 Seiten
Springer International Publishing (Verlag)
978-3-030-33039-2 (ISBN)

Infos

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.

Inhalt

Preface 6
Contents 8
Acronyms 10
1 Overview of 5G and Beyond Communications 12
1.1 Background and Requirements 12
1.2 UAV Applications 13
1.2.1 Flying Infrastructure 14
1.2.2 Aerial Internet-of-Things 15
1.3 Current State-of-the-art 17
1.3.1 Channel Model 17
1.3.1.1 Elevation Angle-Based Model 17
1.3.1.2 3GPP Model 18
1.3.2 Aerial Access Networks 20
1.3.3 Aerial IoT Networks 24
1.3.4 Propulsion and Mobility Model 32
References 36
2 Basic Theoretical Background 37
2.1 Brief Introductions to Optimization Theory 37
2.1.1 Continuous Optimization 38
2.1.1.1 Convex Optimization Problem 38
2.1.1.2 Non-convex Optimization Problem 40
2.1.2 Integer Optimization 41
2.1.2.1 Branch-and-Bound Method 42
2.1.2.2 Bound Calculation 43
2.2 Basics of Game Theory 44
2.2.1 Basic Concepts 44
2.2.1.1 Definition of a Game 44
2.2.1.2 Nash Equilibrium 45
2.2.1.3 Examples of Game Theory 46
2.2.2 Contract Theory 47
2.2.2.1 Classification 47
2.2.2.2 Models and Reward Design 50
2.3 Related Machine Learning Technologies 52
2.3.1 Classical Machine Learning 52
2.3.1.1 Supervised Learning 53
2.3.1.2 Unsupervised Learning 54
2.3.1.3 Machine Learning Algorithm Design 55
2.3.2 Deep Learning 55
2.3.2.1 Basics of Neural Networks 56
2.3.2.2 Back-Propagation Algorithm 59
2.3.3 Reinforcement Learning 62
2.3.3.1 Markov Decision Processes 62
2.3.3.2 Reinforcement Learning Methods 65
References 70
3 UAV Assisted Cellular Communications 71
3.1 UAVs Serving as Base Stations 71
3.1.1 System Model 73
3.1.1.1 Mobility and Energy Consumption 74
3.1.1.2 Wireless Downlink Model 74
3.1.1.3 The Utility of the UAV Operators 75
3.1.1.4 Cost of the MBS Manager 76
3.1.1.5 Contract Formulation 77
3.1.2 Optimal Contract Design 78
3.1.2.1 Basic Properties 79
3.1.2.2 Optimal Pricing Strategy 81
3.1.2.3 Optimal Quality Assignment Problem 84
3.1.2.4 Algorithm for the MBS Optimal Contract 86
3.1.2.5 Socially Optimal Contract 89
3.1.3 Theoretical Analysis and Discussions 90
3.1.3.1 The Impact of the Height on the UAV Types 90
3.1.3.2 The Impact of the UAV Types on the Optimal Revenue 92
3.1.4 Simulation Results 92
3.1.4.1 Simulation Setups 93
3.1.4.2 Simulation Results and Discussions 93
3.1.5 Summary 98
3.2 UAVs Serving as Relays 99
3.2.1 System Model and Problem Formulation 99
3.2.2 Power and Trajectory Optimization 103
3.2.2.1 Trajectory Design 104
3.2.2.2 Power Control 105
3.2.3 Simulation Results 106
3.2.4 Summary 108
References 108
4 Cellular Assisted UAV Sensing 111
4.1 Cellular Internet of UAVs 111
4.1.1 System Model 112
4.1.1.1 UAV Sensing 112
4.1.1.2 UAV Transmission 113
4.1.2 Problem Formulation 114
4.1.2.1 Energy Consumption 114
4.1.2.2 Problem Description 115
4.1.3 Energy Efficiency Maximization Algorithm 115
4.1.3.1 UAV Sensing Optimization 116
4.1.3.2 UAV Transmission Optimization 117
4.1.3.3 Overall Algorithm 119
4.1.4 Simulation Results 119
4.1.5 Summary 121
4.2 Cooperative Cellular Internet of UAVs 121
4.2.1 System Model 122
4.2.1.1 UAV Sensing 123
4.2.1.2 UAV Transmission 124
4.2.1.3 Task Completion Time 125
4.2.2 Sense-and-Send Protocol 125
4.2.3 Problem Formulation 128
4.2.3.1 Problem Description 128
4.2.3.2 Problem Decomposition 129
4.2.3.3 Iterative Algorithm Description 130
4.2.4 Iterative Trajectory, Sensing, and Scheduling Optimization Algorithm 131
4.2.4.1 Trajectory Optimization 131
4.2.4.2 Sensing Location Optimization 134
4.2.4.3 UAV Scheduling 137
4.2.4.4 Performance Analysis 138
4.2.5 Simulation Results 141
4.2.6 Summary 147
4.3 UAV-to-X Communications 148
4.3.1 System Model 149
4.3.1.1 Scenario Description 149
4.3.1.2 Data Transmission 151
4.3.1.3 Channel Model 152
4.3.2 Cooperative UAV Sense-and-Send Protocol 154
4.3.3 Problem Formulation 156
4.3.3.1 Joint Subchannel Allocation and UAV Speed Optimization Problem Formulation 156
4.3.3.2 Problem Decomposition 158
4.3.4 Joint Subchannel Allocation and UAV SpeedOptimization 159
4.3.4.1 U2N and CU Subchannel Allocation Algorithm 159
4.3.4.2 U2U Subchannel Allocation Algorithm 161
4.3.4.3 UAV Speed Optimization Algorithm 165
4.3.4.4 Iterative Subchannel Allocation and UAV Speed Optimization Algorithm 168
4.3.5 Simulation Results 170
4.3.6 Summary 175
4.4 Reinforcement Learning for the Cellular Internet of UAVs 175
4.4.1 System Model 176
4.4.1.1 UAV Sensing 177
4.4.1.2 UAV Transmission 177
4.4.2 Decentralized Sense-and-Send Protocol 178
4.4.2.1 Sense-and-Send Cycle 178
4.4.2.2 Uplink Subchannel Allocation Mechanism 180
4.4.3 Sense-and-Send Protocol Analysis 181
4.4.3.1 Outer Markov Chain of UAV Sensing 181
4.4.3.2 Inner Markov Chain of UAV Transmission 182
4.4.3.3 Analysis on the Data Rate 185
4.4.4 Decentralized Trajectory Design 186
4.4.4.1 UAV Trajectory Design Problem 186
4.4.4.2 Reinforcement Learning Framework 188
4.4.4.3 Enhanced Multi-UAV Q-Learning Algorithm for UAV Trajectory Design 191
4.4.4.4 Analysis of Reinforcement Learning Algorithms 194
4.4.5 Simulation Results 196
4.4.6 Summary 201
4.5 Applications of the Cellular Internet of UAVs 201
4.5.1 Preliminaries of UAV Sensing System 203
4.5.1.1 System Overview 203
4.5.1.2 Dataset Description 204
4.5.1.3 Data Reliability 205
4.5.1.4 Selection of Model Parameters 206
4.5.2 Fine-Grained AQI Distribution Model 206
4.5.2.1 Physical Particle Dispersion Model 206
4.5.2.2 Neural Network Model 207
4.5.2.3 GPM-NN Model 208
4.5.3 Adaptive AQI Monitoring Algorithm 212
4.5.3.1 Complete Monitoring 213
4.5.3.2 Selective Monitoring 213
4.5.3.3 Trajectory Optimization 215
4.5.4 Application Scenario I: Performance Analysis in Horizontal Open Space 216
4.5.4.1 Scenario Description 216
4.5.4.2 Performance Analysis 217
4.5.5 Application Scenario II: Performance Analysis in Vertical Enclosed Space 222
4.5.5.1 Scenario Description 222
4.5.5.2 Performance Analysis 223
4.5.6 Summary 226
References 227

Produkt-Details

• Erscheint lt. Verlag: 13.12.2019
• Reihe/Serie: Wireless Networks
• Reihe/Serie: Wireless Networks
• Zusatzinfo: X, 221 p. 95 illus., 91 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Nachrichtentechnik
• Schlagworte: cooperative UAV • data offloading • Deep learning • IoTS • load balance • machine learning • Power Control • Reinforcement Learning • Resource Allocation • trajectory design • UAV • UAV-to-X communications
• ISBN-10: 3-030-33039-7 / 3030330397
• ISBN-13: 978-3-030-33039-2 / 9783030330392