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

:	Hongliang Zhang, Lingyang Song, Zhu Han
:	Unmanned Aerial Vehicle Applications over Cellular Networks for 5G and Beyond
:	Springer-Verlag
:	9783030330392
:	1
:	CHF 114.50
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	231
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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.

div>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.

	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