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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
AUTONOMOUS AIRBORNE WIRELESS NETWORKS Discover what lies beyond the bleeding-edge of autonomous airborne networks with this authoritative new resource Autonomous Airborne Wireless Networks delivers an insightful exploration on recent advances in the theory and practice of using airborne wireless networks to provide emergency communications, coverage and capacity expansion, information dissemination, and more. The distinguished engineers and editors have selected resources that cover the fundamentals of airborne networks, including channel models, recent regulation developments, self-organized networking, AI-enabled flying networks, and notable applications in a variety of industries. The book evaluates advances in the cutting-edge of unmanned aerial vehicle wireless network technology while offering readers new ideas on how airborne wireless networks can support various applications expected of future networks. The rapidly developing field is examined from a fresh perspective, one not just concerned with ideas of control, trajectory optimization, and navigation. Autonomous Airborne Wireless Networks considers several potential use cases for the technology and demonstrates how it can be integrated with concepts from self-organized network technology and artificial intelligence to deliver results in those cases. Readers will also enjoy: * A thorough discussion of distributed drone base station positioning for emergency cellular networks using reinforcement learning (AI-enabled trajectory optimization) * An exploration of unmanned aerial vehicle-to-wearables (UAV2W) indoor radio propagation channel measurements and modelling * An up-to-date treatment of energy minimization in UAV trajectory design for delay tolerant emergency communication * Examinations of cache-enabled UAVs, 3D MIMO for airborne networks, and airborne networks for Internet of Things communications Perfect for telecom engineers and industry professionals working on identifying practical and efficient concepts tailored to overcome challenges facing unmanned aerial vehicles providing wireless communications, Autonomous Airborne Wireless Networks also has a place on the bookshelves of stakeholders, regulators, and research agencies working on the latest developments in UAV communications.
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Table of Contents
Cover
Title Page
Copyright
Editor Biographies
List of Contributors
1 Introduction
2 Channel Model for Airborne Networks
2.1 Introduction
2.2 UAV Classification
2.3 UAV‐Enabled Wireless Communication
2.4 Channel Modeling in UAV Communications
2.5 Key Research Challenges of UAV‐Enabled Wireless Network
2.6 Conclusion
Bibliography
3 Ultra‐wideband Channel Measurements and Modeling for Unmanned Aerial Vehicle‐to‐Wearables (UAV2W) Systems
3.1 Introduction
3.2 Measurement Settings
3.3 UWB‐UAV2W Radio Channel Characterization
3.4 Statistical Analysis
3.5 Conclusion
Bibliography
Notes
4 A Cooperative Multiagent Approach for Optimal Drone Deployment Using Reinforcement Learning
4.1 Introduction
4.2 System Model
4.3 Reinforcement Learning Solution
4.4 Representative Simulation Results
4.5 Conclusions and Future Work
Acknowledgments
Bibliography
5 SWIPT‐PS Enabled Cache‐Aided Self‐Energized UAV for Cooperative Communication
5.1 Introduction
5.2 System Model
5.3 Optimization Problem Formulation
5.4 Numerical Simulation Results
5.5 Conclusion
Acknowledgments
Appendix 5.A Proof of Optimal Solutions Obtained in (P1)
Bibliography
Notes
6 Performance of mmWave UAV‐Assisted 5G Hybrid Heterogeneous Networks
6.1 The Significance of UAV Deployment
6.2 Contribution
6.3 The Potential of mmWave and THz Communication
6.4 Challenges and Applications
6.5 Fronthaul Connectivity using UAVs
6.6 Communication Model
6.7 Association of SCBs with UAVs
6.8 Results and Discussions
6.9 Conclusion
Bibliography
Notes
7 UAV‐Enabled Cooperative Jamming for Physical Layer Security in Cognitive Radio Network
7.1 Introduction
7.2 System Model
7.3 Proposed Algorithm
7.4 Numerical Results
7.5 Conclusion
Bibliography
8 IRS‐Assisted Localization for Airborne Mobile Networks
8.1 Introduction
8.2 Intelligent Reflecting Surfaces in Airborne Networks
8.3 Localization Using IRS
8.4 Research Challenges
8.5 Summary and Conclusion
Bibliography
9 Performance Analysis of UAV‐Enabled Disaster Recovery Networks
9.1 Introduction
9.2 UAV Networks
9.3 Benefits of UAV Networks
9.4 Design Consideration of UAV Networks
9.5 New Technology and Infrastructure Trends
9.6 Research Trends
9.7 Future Insights
9.8 Conclusion
Bibliography
10 Network‐Assisted Unmanned Aerial Vehicle Communication for Smart Monitoring of Lockdown
10.1 Introduction
10.2 UAVs as Aerial Base Stations
10.3 UAV as Relays for Terrestrial Communication
10.4 Conclusion
Bibliography
Note
11 Unmanned Aerial Vehicles for Agriculture: an Overview of IoT‐Based Scenarios
11.1 Introduction
11.2 The Perspective of Research Projects
11.3 IoT Scenarios in Agriculture
11.4 Wireless Communication Protocols
11.5 Multi‐access Edge Computing and 5G Networks
11.6 Conclusion
Bibliography
Notes
12 Airborne Systems and Underwater Monitoring
12.1 Introduction
12.2 Automated Image Labeling
12.3 Water/Land Visual Differentiation
12.4 Offline Bathymetric Mapping
12.5 Online Bathymetric Mapping
12.6 Conclusion and Future Work
Bibliography
13 Demystifying Futuristic Satellite Networks: Requirements, Security Threats, and Issues
13.1 Introduction
13.2 Inter‐Satellite and Deep Space Network
13.3 Security Requirements and Challenges in ISDSN
13.4 Conclusion
Bibliography
Notes
14 Conclusion
14.1 Future Hot Topics
14.2 Concluding Remarks
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Regulation for LAP deployment of UAVs in different countries.
Table 2.2 Measurement campaigns to characterize the path loss and large‐scale...
Table 2.3 Measured small‐scale fading of AG propagation in different environm...
Chapter 3
Table 3.1 The measurement apparatus with their specifications.
Table 3.2 Path loss measurement and path loss exponent for nine different bod...
Table 3.3 Combined path loss measurement and path loss exponent for four diff...
Table 3.4 Path loss measurement and path loss exponent for four different bod...
Table 3.5 Time dispersion analysis in the case of LoS for nine body locations...
Table 3.6 Time dispersion analysis in the case of NLoS for four body location...
Table 3.7 Path loss values in the indoor and outdoor environments for four po...
Table 3.8 Delay analysis values in nanoseconds for two body locations conside...
Table 3.9 AIC score for all the distributions considered for modeling the fad...
Chapter 4
Table 4.1 State‐of‐the‐art UAV positioning solutions using RL.
Table 4.2 Simulation parameters.
Chapter 5
Table 5.1 Definitions of mathematical symbols and variables.
Table 5.2 Rate at the users for different UAV's trajectories.
Chapter 6
Table 6.1 Impact on the characteristics of signals at THz and mmWave frequenc...
Table 6.2 Simulation parameters [2,39].
Chapter 7
Table 7.1 Simulation parameters
Chapter 9
Table 9.1 Critical review on state of the art.
Chapter 10
Table 10.1 Fitting parameters for receiver threshold
dBm.
Table 10.2 Fitting parameters for receiver threshold
dBm.
Table 10.3 Fitting parameters for receiver threshold
dBm.
Table 10.4 Fitting parameters for receiver threshold
dBm.
Table 10.5 Fitting parameters for receiver threshold
dBm.
Table 10.6 Fitting parameters for receiver threshold
dBm.
Table 10.7 5G air interface simulation parameters.
Table 10.8 Download maximum throughput.
Chapter 11
Table 11.1 The most relevant EU‐funded R&I projects exploiting UAV technology...
Table 11.2 Surveyed literature in the field of SF, especially considering the...
Table 11.3 Agricultural scenarios covered by the described works and the use ...
Chapter 12
Table 12.1 Automated point selection simulation results
Table 12.2 Best case interpolation decision table
List of Illustrations
Chapter 2
Figure 2.1 Aerial user equipment and aerial base station.
Figure 2.2 Air‐to‐ground propagation in UAV‐assisted cellular network.
Figure 2.3 Multipath air‐to‐ground propagation in urban setting.
Chapter 3
Figure 3.1 The UWB measurement communication setup.
Figure 3.2 The UWB antenna and the IRIS+ quadcopter used in the measurement ...
Figure 3.3 The UWB antenna patch locations on the human body for the UWB mea...
Figure 3.4 The sketch plan of the measurement campaign with the 10 distinct ...
Figure 3.5 Different environments considered for the measurement campaign. (...
Figure 3.6 Path loss factor determination from linear regression for a wirel...
Figure 3.7 Averaged PDP at different distances.
Figure 3.8 Normalized averaged path loss delay comparison.
Figure 3.9 Statistical test (AIC) to determine the best distribution for fad...
Figure 3.10 Empirical and predicted CDF for radio channel between forehead a...
Chapter 4
Figure 4.1 Manhattan grid urban layout.
Figure 4.2 UAV path loss in urban environment.
Figure 4.3 MARL framework for multi‐drone networks.
Figure 4.4 Available action sets. (a) Basic strategy action space. (b) All s...
Figure 4.5 Basic strategy..
Figure 4.6 ALL strategy.
Figure 4.7 New strategy.
Figure 4.8 User density areas. (a) Low density. (b) Medium density. (c) High...
Figure 4.9 Single frequency: Number of users in outage. (a) Low density. (b)...
Figure 4.10 Single frequency: Global system backhaul. (a) Low density. (b) M...
Figure 4.11 Single frequency: Number of active drones. (a) Low density. (b) ...
Figure 4.12 Three frequencies: Number of users in outage. (a) Low density. (...
Figure 4.13 Three frequencies: Global system backhaul. (a) Low density. (b) ...
Figure 4.14 Three frequencies: Number of active drones. (a) Low density. (b)...
Figure 4.15 Six frequencies: Number of users in outage. (a) Low density. (b)...
Figure 4.16 Six frequencies: Global system backhaul. (a) Low density. (b) Me...
Figure 4.17 Six frequencies: Number of active drones. (a) Low density. (b) M...
Chapter 5
Figure 5.1 Reference system model of self‐energized UAV‐assisted communicati...
Figure 5.2 Block diagram of the decode‐and‐forward (DF) relaying for the sel...
Figure 5.3 Time block diagram of the proposed system model.
Figure 5.4 System layout for the proposed communication network.
Figure 5.5 Achievable rate at the user versus
for various values of cachin...
Figure 5.6 Comparison of achievable rate at the user for different caching c...
Figure 5.7 Transmission SNR versus achievable rate at the user for different...
Figure 5.8 User requested rate
versus optimal values of
and
: (a) when ...
Figure 5.9 Optimized trajectory of the UAV for the given communication setup...
Chapter 6
Figure 6.1 An illustration of mmWave/THz and UAVs integrated hybrid communic...
Figure 6.2 Pictorial representation of UAV‐enabled wireless network.
Figure 6.3 Stochastic geometry for the communication between an SCB and a UA...
Figure 6.4 A snapshot of 3D placement of child‐UAVs and the association of S...
Figure 6.5 Comparison of sum rate by varying the constraint (6.15) and (6.16...
Figure 6.6 A comparison of unassociated SCBs and the sum rate with constrain...
Figure 6.7 Performance of sum rate by varying the constraint (6.16) and havi...
Figure 6.8 Sum rate's performance when the number of child‐UAVs and
are va...
Chapter 7
Figure 7.1 The UAV‐enabled cooperative jamming in cognitive radio system.
Figure 7.2 Average secrecy rate versus flight time period.
Figure 7.3 Trajectories of UAV with different methods.
Figure 7.4 Distance between UAV and Eve.
Figure 7.5 Convergence behavior. (a) Convergence behavior at
dBm. (b) Aver...
Chapter 8
Figure 8.1 Localization using IRS model with two IRSs and one SC.
Figure 8.2 Localization of a UAV using multiple IRSs.
Chapter 9
Figure 9.1 Flow chart explaining different architectures of UAV systems.
Figure 9.2 UAV system's different topologies.
Figure 9.3 UAV system's benefits in different applications.
Chapter 10
Figure 10.1 Cellular Network‐assisted low‐altitude aerial base station (ABS)...
Figure 10.2 Ray tracing simulation in urban environment.
Figure 10.3 Variation of number of ABSs required with its altitude.
Figure 10.4 Variation of number of ABSs required with its transmitting power...
Figure 10.5 Variation of number of ABSs required with geographical area to b...
Figure 10.6 TBS path loss and transmission power.
Figure 10.7 Master UAV transmission power and line of sight.
Figure 10.8 System for channel measurement.
Figure 10.9 Received power by ground users from SUAVs cluster.
Figure 10.10 64‐QAM throughput coverage area.
Chapter 11
Figure 11.1 Qualitative comparison of the most diffused wireless communicati...
Figure 11.2 Plausible network architectures, highlighting the use of MEC in
Chapter 12
Figure 12.1 Complete system on UAV.
Figure 12.2 Worst overall classification accuracy simulation result. (a) Ori...
Figure 12.3 Worst false negative simulation result. (a) Original image. (b) ...
Figure 12.4 Field experiment results for automated point labeling. (a) Origi...
Figure 12.5 Winch and Raspberry Pi.
Figure 12.6 Sensor payload.
Figure 12.7 Spline results. (a) RMSE. (b) Maximum difference.
Figure 12.8 IDW results. (a) RMSE. (b) Maximum difference.
Chapter 13
Figure 13.1 Tier‐1 of satellite networks: It includes the connection of a sa...
Figure 13.2 Tier‐2 of satellite networks: It includes the inter‐satellite li...
Figure 13.3 Tier‐3 of satellite networks: It includes the communication of t...
Guide
Cover Page
Title Page
Copyright
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Autonomous Airborne Wireless Networks
Edited by
Muhammad Ali Imran, Oluwakayode Onireti, Shuja Ansari, and Qammer H. Abbasi
University of Glasgow, UK
This edition first published 2021
© 2021 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
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Editor Biographies
Muhammad Ali Imran is Dean at the University of Glasgow, UESTC, Professor of Communication Systems, and Head of Communications Sensing and Imaging group in the James Watt School of Engineering at the University of Glasgow, UK.
Oluwakayode Onireti is a lecturer at the James Watt School of Engineering, University of Glasgow, UK.
Shuja Ansari is currently a research associate at Communications Sensing and Imaging group in the James Watt School of Engineering at the University of Glasgow, UK, and Wave‐1 Urban 5G use case implementation lead at Glasgow 5G Testbed funded by the Scotland 5G Center.
Qammer H. Abbasi is a senior lecturer (Associate Professor), Program Director for Dual PhD degree, and Deputy Head of Communications Sensing and Imaging group in the James Watt School of Engineering at the University of Glasgow, UK.
List of Contributors
Qammer H. Abbasi
James Watt School of Engineering University of Glasgow
Glasgow
UK
Hisham Abuella
School of Electrical and Computer Engineering, Oklahoma State University
Stillwater, OK
USA
Rigoberto Acosta‐González
Department of Electronics and Telecommunications, Universidad Central “Marta Abreu” de Las Villas
Santa Clara
Cuba
Muhammad W. Akhtar
School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST)
Islamabad
Pakistan
Gotta Alberto
Institute of Information Science and Technologies (ISTI) and Institute of Science and Technologies for Energy and Sustainable Mobility, National Research Council (CNR)
Pisa
Italy
Mudassar Ali
Department of Telecommunication Engineering, UET
Taxila
Pakistan
Imran S. Ansari
James Watt School of Engineering University of Glasgow
Glasgow
UK
Rafay I. Ansari
Department of Computer and Information Science
Northumbria University
Newcastle upon Tyne
UK
Shuja S. Ansari
James Watt School of Engineering University of Glasgow
Glasgow
UK
Muhammad R. Asghar
School of Computer Science The University of Auckland
Auckland
New Zealand
Muhammad Awais
School of Computing and Communications
Lancaster University
Lancaster
UK
Elizabeth Basha
Electrical and Computer Engineering Department University of the Pacific
Stockton, CA
USA
Rabeea Basir
School of Electrical Engineering & Computer Science (SEECS) National University of Sciences and Technology
Islamabad
Pakistan
and
James Watt School of Engineering University of Glasgow
Glasgow
UK
Charles F. Bunting
School of Electrical and Computer Engineering, Oklahoma State University
Stillwater, OK
USA
Yunfei Chen
School of Engineering
University of Warwick
Coventry
UK
Naveed A. Chughtai
Military College of Signals National University of Sciences and Technology
Rawalpindi
Pakistan
Jacob N. Dixon
IBM
Rochester, MN
USA
Sabit Ekin
School of Electrical and Computer Engineering, Oklahoma State University
Stillwater, OK
USA
Syed A. Hassan
School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST)
Islamabad
Pakistan
Muhammad A. Imran
James Watt School of Engineering University of Glasgow
Glasgow
UK
Jamey D. Jacob
School of Mechanical and Aerospace Engineering, Oklahoma State University
Stillwater, OK
USA
Dushantha Nalin K. Jayakody
Department of Information Technology, School of Computer Science and Robotics, National Research Tomsk Polytechnic University
Tomsk
Russian Federation
and
Centre for Telecommunication Research, School of Engineering Sri Lanka Technological Campus
Padukka
Sri Lanka
Amit Kachroo
School of Electrical and Computer Engineering, Oklahoma State University
Stillwater, OK
USA
Aziz Khuwaja
School of Engineering, Electrical and Electronic Engineering Stream University of Warwick
Coventry
UK
Paulo V. Klaine
Electronics and Nanoscale Engineering Department University of Glasgow
Glasgow
UK
Hassan Malik
Department of Computer Science Edge Hill University
Ormskirk
UK
Bacco Manlio
Institute of Information Science and Technologies (ISTI) and Institute of Science and Technologies for Energy and Sustainable Mobility, National Research Council (CNR)
Pisa
Italy
Ruggeri Massimiliano
National Research Council (CNR) Institute of Science and Technologies for Energy and Sustainable Mobility
Ferrara
Italy
Lina Mohjazi
James Watt School of Engineering University of Glasgow
Glasgow
UK
Samuel Montejo‐Sánchez
Programa Institucional de Fomento a la I+D+i, Universidad Tecnológica Metropolitana
Santiago
Chile
Hieu V. Nguyen
The University of Danang – Advanced Institute of Science and Technology
Da Nang
Vietnam
Qiang Ni
School of Computing and Communications
Lancaster University
Lancaster
UK
Phu X. Nguyen
Department of Computer Fundamentals, FPT University
Ho Chi Minh City
Vietnam
Van‐Dinh Nguyen
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg
Luxembourg
Oluwakayode Onireti
James Watt School of Engineering University of Glasgow
Glasgow
UK
and
Department of Electrical Engineering, Sukkur IBA University
Sukkur
Pakistan
Barsocchi Paolo
Institute of Information Science and Technologies (ISTI) and Institute of Science and Technologies for Energy and Sustainable Mobility, National Research Council (CNR)
Pisa
Italy
Haris Pervaiz
School of Computing and Communications
Lancaster University
Lancaster
UK
Olaoluwa Popoola
James Watt School of Engineering University of Glasgow
Glasgow
UK
Tharindu D. Ponnimbaduge Perera
Department of Information Technology, School of Computer Science and Robotics, National Research Tomsk Polytechnic University
Tomsk
Russian Federation
Adithya Popuri
School of Electrical and Computer Engineering, Oklahoma State University
Stillwater, OK
USA
Saad Qaisar
School of Electrical Engineering & Computer Science (SEECS) National University of Sciences and Technology
Islamabad
Pakistan
and
Department of Electrical and Electronic Engineering
University of Jeddah
Jeddah
Saudi Arabia
Marwa Qaraqe
Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University (HBKU)
Doha
Qatar
Navuday Sharma
Test Software Development Ericsson Eesti AS
Tallinn
Estonia
Richard D. Souza
Department of Electrical and Electronics Engineering, Federal University of Santa Catarina
Florianóplis
Brazil
Muhammad K. Shehzad
School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST)
Islamabad
Pakistan
Oh‐Soon Shin
School of Electronic Engineering Soongsil University
Seoul
South Korea
Sean Thalken
Electrical and Computer Engineering Department University of the Pacific
Stockton, CA
USA
Jason To‐Tran
Electrical and Computer Engineering Department University of the Pacific
Stockton, CA
USA
Christopher Uramoto
Electrical and Computer Engineering Department University of the Pacific
Stockton, CA
USA
Muhammad Usman
Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University (HBKU)
Doha
Qatar
Surbhi Vishwakarma
School of Electrical and Computer Engineering, Oklahoma State University
Stillwater, OK
USA
Davis Young
Electrical and Computer Engineering Department University of the Pacific
Stockton, CA
USA
Lei Zhang
Electronics and Nanoscale Engineering Department University of Glasgow
Glasgow
UK
1Introduction
Muhammad A. Imran, Oluwakayode Onireti, Shuja S. Ansari and Qammer H. Abbasi
James Watt School of Engineering, University of Glasgow, Glasgow, UK
Airborne networks (ANs) are now playing an increasingly crucial role in military, civilian, and public applications such as surveillance and monitoring, military, and rescue operations. More recently, airborne networks have also become a topic of interest in the industrial and research community of wireless communication. The 3rd Generation Partnership Project (3GPP) standardization has a study item devoted to facilitating the seamless integration of airborne wireless networks into future cellular networks. Airborne wireless networks enabled by unmanned aerial vehicles (UAVs) can provide cost‐effective and reliable wireless communications to support various use cases in future networks. Compared with high‐altitude platforms or conventional terrestrial communications, the provision of on‐demand communication systems with UAVs has faster deployment time and more flexibility in terms of reconfiguration. Further, UAV‐enabled propagation can also offer better communication channels due to the existence of the line‐of‐sight (LoS) links, which are of short range.
Despite the several benefits of airborne wireless networks, they suffer from some realistic constraints such as being energy constrained because of the limited battery power, safety concerns, and the strict flight zone. Hence, developing new signal processing, communication, and optimization framework for autonomous airborne wireless networks is essential. Such networks can offer high data rates and assist the traditional terrestrial networks to provide real‐time and ultrareliable sensing applications for the beyond‐5G networks. Achieving this gain requires the correct characterization of the propagation channel while considering the high mobility dynamics. Accurate channel modeling is imperative to fulfill the ever‐increasing requirements of the end user to transfer data at higher rates. The air‐to‐ground (AG) and the air‐to‐air (AA) channel propagation models for the airborne wireless network channel can be characterized by using measurement and empirical studies. Further, the key performance indicators (KPIs) of airborne wireless networks such as flight time, trajectory, data rate, energy efficiency, and latency need to be optimized for the different use cases.
This book explores recent advances in the theory and practice of airborne wireless networks for the next generation of wireless networks to support various applications, including emergency communications, coverage and capacity expansion, Internet of things (IoT), information dissemination, future healthcare, pop‐up networks, etc. The book focuses on channel characteristics and modeling, networking architectures, self‐organized airborne networks, self‐organized backhaul, artificial‐intelligence‐enabled trajectory optimization, and application in sectors such as agriculture, underwater communications, and emergency networks. The book further highlights the main considerations during the design of the autonomous airborne networks and exploits new opportunities due to the recent advancement in wireless communication systems.
This book for the first time evaluates the advances in the current state of the art and it provides readers with insights on how airborne wireless networks can seamlessly support various applications expected in future networks. More specifically, the book shows the readers how the integration of self‐organized networks and artificial intelligence can support the various use cases of airborne wireless networks.
UAVs provide a suitable aerial platform for various wireless network applications that require reliable and ubiquitous communication. The channel model plays a crucial role in the wireless communications system and thus Chapter 2 focuses on the channel model for UAV networks. The authors first provide an overview of UAV networks in terms of their classification and how they can be used to enable future wireless communication systems. Accurate channel modeling is imperative to fulfill the ever‐increasing requirements of the end user to transfer data at higher rates. Hence, the authors discuss channel modeling in UAV communications while focusing on the salient feature of the AG and AA propagation channels. Finally, the chapter concludes by discussing some of the key research challenges for the practical deployment of UAVs as airborne wireless nodes.
In Chapter 3, the authors describe the fundamental properties of the ultrawide band (UWB) channel and present one of the first experimental off‐body studies between a human subject and an UAV at 7.5 GHz of bandwidth. In the study presented in this chapter, the transmitter antenna was placed on a UAV while the receiver antenna was patched on a human subject at different body locations during the campaign. The chapter presents the measurement setting, detailing the measurement campaign that was conducted in an indoor and an outdoor environment with LoS and non‐line‐of‐sight (NLoS) cases. Furthermore, the chapter presents the UWB‐unmanned aerial vehicle‐to‐wearables (UAV2W) channel characterization. Finally, the chapter presents the statistical analysis to determine the distribution that best characterizes the fading channels between different body locations and the UAV.
Chapter 4 describes the use of a Q‐learning algorithm, which is based on a cooperative multiagent approach, to intelligently find the optimal position of a set of drones. The algorithm presented in the chapter is designed with the objective to minimize the number of users in an outage in the network. Hence, the algorithm determines the optimal distribution of frequencies and whether it should shut down a set of drones. The chapter also proposes and compares four different strategies for the Q‐learning algorithm with different action selection policies, whose algorithms differ in terms of design complexity, ability to vary the number of drones in operation, and convergence time. The chapter presents numerical results that show the relationship between the density of users in the region of interest and the number of frequencies in operation.
In Chapter 5, the authors describe a self‐energized UAV‐assisted caching relaying scheme. In this scheme, the UAV's communication capabilities are powered solely by the power‐splitting simultaneous wireless information and power transfer (PS‐SWIPT) energy‐harvesting (EH) technique, and it employs decode and forward (DF) relaying protocol to assist the information transmission to users from the source node. The authors present the transmission block diagram to accommodate communication processes within the system. Afterward, the authors address the problem of identifying optimal time and energy resources for the communication system and the optimal UAV's trajectory while adhering to the quality of service (QoS) requirements of the communication network. Finally, numerical simulation results to identify the impacts of the system parameters on the information rate at the user equipment are presented.
Chapter 6 focuses on the case study of millimeter‐wave (mmWave) and terahertz (THz) communication and technical challenges for applying mmWave and THz frequency band for communication with UAVs. The chapter starts by presenting the potential of mmWave and THz bands for communications. This is followed by an overview of the technical challenges for implementing mmWave and THz band for UAV communications. The chapter then presents a theoretical analysis that focuses on the placement of UAVs. Besides, the chapter investigates the performance of UAV‐enabled hybrid heterogeneous network (HetNet) by considering stringent communication‐related constraints such as the system bandwidth, data rate, signal‐to‐noise ratio (SNR), etc. The association of terrestrial small‐cell base stations (SCBs) with UAVs is addressed such that the sum rate of the overall system is maximized. Finally, numerical results are included to show the favorable performance of the UAV‐assisted wireless network.
In Chapter 7, the authors discuss a method that uses a cooperative UAV as a friendly jammer to enhance the security performance of cognitive radio networks. The chapter starts by presenting the system model for the UAV‐enabled cooperative jamming in a cognitive radio system. Then the optimization problem is formulated. The resource allocation in the network must jointly optimize the transmission power and UAV's trajectory to maximize the secrecy rate while satisfying a given interference threshold at the primary receiver (PR). With the original problem non‐convex, the authors first transform the original problem into a more tractable form and then present a successive convex approximation‐based algorithm for its solutions. Finally, numerical results are included to show a significant improvement in the security performance of the UAV‐enabled cognitive radio networks.
Chapter 8 explores the possibility of using intelligent reflecting surfaces (IRS) in airborne networks for the localization of users and base stations. Positioning is an important aspect in the present and future wireless networks, where it augments the network operations and assists in multiple localization‐based applications. The chapter starts by presenting the related works and the underlying opportunities around IRS‐ and UAV‐based base stations. The authors then discuss the integration of IRS in ANs and the potential use cases. Afterward, the chapter presents an IRS‐based localization model for ANs along with some mathematical modeling. Finally, some future research challenges that present research opportunities are included.
Chapter 9 describes the application of UAVs for disaster recovery networks. The chapter starts by providing an overview of the UAV networks including the description of the UAV architectures, namely, single‐UAV systems, multi‐UAV systems, cooperative multi‐UAV systems, and multilayer UAV networks. The authors then discuss the most prominent applications of UAVs and the different system requirements of the UAV system. Afterward, the chapter discusses the design consideration of UAV networks in the context of disaster recovery networks. New technologies and infrastructure trends for UAV disaster networks namely, network function virtualization (NFV), software‐defined networks (SDN), cloud computing, and millimeter‐wave networks are also discussed in the chapter. Further, the authors discuss the enhancement in technologies such as artificial intelligence, machine learning, optimization theory, and game theory as they impact the overall performance of the UAV‐enabled disaster recovery networks. Finally, the chapter presents the research trends and some insight into the future.
In Chapter 10, the authors discuss the importance of UAVs in monitoring COVID‐19 restrictions of social distancing, public gatherings, and physical contacts in a smart city environment. The chapter starts with a review of recent literature addressing the impact of COVID‐19 in the current scenario and strategies to find potential solutions with existing communication and computing technologies. Afterward, the authors present two use case scenarios of UAVs namely, UAVs as aerial base stations (ABS) and UAVs as Relays, while including the simulation setups with ray tracing for both scenarios. The chapter then presents the derivation of the optimal number of ABSs to cover a geographical region, given the constraint on ABS transmission power, the altitude of hovering, and including the path loss and channel fading effects from ray‐tracing simulations. The authors then describe the 5G air interface when using the UAVs as relays. Finally, simulation results on the received power by the ground users and the throughput coverage area are presented.
In Chapter 11, the authors present and discuss both the research initiatives and the scientific literature on IoT‐based smart farming (SF), especially the use of UAVs in SF. The authors start by presenting an analysis of how UAVs are used in SF and the application scenarios. This is then followed by a detailed review of the scientific work in the literature highlighting the role of unmanned vehicles. The chapter then presents both the requirements and solutions for networking and a brief comparison of the existing protocol supporting IoT scenarios in agricultural settings. Finally, the chapter discusses the potential future role of the joint use of mobile edge computing (MEC) and the 5G network, presenting network architecture to connect smart farms through UAVs and satellites.
Wetlands monitoring requires accurate topographic and bathymetric maps, and this can be achieved using UAVs that can create maps regularly, with minimum cost and reduced environmental impact. Chapter 12 introduces a set of systems needed to create this automation. The chapter starts by discussing the automated image labeling system. Next, the authors present an online classification system for differentiating land and water. The authors then present offline bathymetric map creation using aerial robots. Since the offline approach does not take full advantage of the adaptability that the UAV provides, the authors present the online bathymetric mapping. Finally, the chapter presents results and analysis to show the best combination of the online bathymetric mapping.
Integration of terrestrial and satellite networks has been proposed for leveraging the combined benefits of both complementary technologies. Moreover, with the quest of exploring deep space and connecting solar system planets with the Earth, the traditional satellite network has gone beyond the geosynchronous equatorial orbit (GEO) wherein Interplanetary Internet will play a key role. Chapter 13 presents a short review of the inter‐satellite and deep space network (ISDSN). This chapter discusses the classification of the ISDSN into different tiers while highlighting the communication and networking paradigms. Further, the chapter also discusses the security requirements, challenges, and threats in each tier. The potential solutions to the identified challenges at the different tiers of the ISDSN are also described. Finally, the chapter concludes by highlighting the crucial role of the ISDSN in future cellular networks.
2Channel Model for Airborne Networks
Aziz A. Khuwaja1,2 and Yunfei Chen1
1School of Engineering, Electrical and Electronic Engineering Stream, University of Warwick, Coventry, UK
2Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan
2.1 Introduction
The use of unmanned aerial vehicles (UAVs) is desirable due to their high maneuverability, ease of operability, and affordable prices in various civilian applications, such as disaster relief, aerial photography, remote surveillance, and continuous telemetry. One of the promising application of UAVs is enabling the wireless communication network in cases of natural calamity and in hot spot areas during peak demand where the resources of the existing communication network have been depleted [1]. Qualcomm has already initiated field trials for the execution of fifth generation (5G) cellular applications [2]. Google and Facebook are also exploiting the use of UAVs to provide Internet access to far‐flung destinations [3].
The selection of an appropriate type of UAV is essential to meet the desired quality of service (QoS) depending on applications and goals in different environments. In fact, for any specific wireless networking application, the UAV altitude and its capabilities must be taken into account. UAVs can be categorized, based on their altitude, into low‐altitude platforms (LAPs) and high‐altitude platforms (HAPs). Furthermore, based on their structure, UAVs can be categorized as fixed‐wing and rotary‐wing UAVs. In comparison with rotary wings, fixed‐wing UAVs move in the forward direction to remain aloft, whereas rotary‐wing UAVs are desired for applications that require UAVs to be quasi‐stationary over a given area. However, in both types, flight duration depends on their energy sources, weight, speed, and trajectory.
The salient features of UAV‐based communication network are the air‐to‐ground (AG) and air‐to‐air (AA) propagation channels. Accurate channel modeling is imperative to fulfill the ever‐increasing requirements of end users to transfer data at higher rates. The available channel models for AG propagation are designed either for terrestrial communication or for aeronautical communications at higher altitudes. These models are not preferable for low‐altitude UAV communication, which uses small size UAVs in different urban environments. On one hand, the AG channel exhibits higher probability of line‐of‐sight (LoS) propagation, which reduces the transmit power requirement and provides higher link reliability. In cases with non‐line‐of‐sight (NLoS), shadowing and diffraction losses can be compensated with a large elevation angle between the UAV and the ground device. On the other hand, UAV mobility can incur significant temporal variations in both the AG and AA propagation due to the Doppler shift.
Small UAVs may experience airframe shadowing due to their flight path with sharper changes in pitch, yaw, and roll angle. In addition, distinct structural design and material of UAV body may contribute additional shadowing attenuation. This phenomenon has not yet been extensively studied in the literature.
Despite the number of promising UAV applications, one must address several technical challenges before the widespread applicability of UAVs. For example, while using UAV in aerial base station (BS) scenario, the important design considerations include radio resource management, flight time, optimal three‐dimensional deployment of UAV, trajectory optimization, and performance analysis. Meanwhile, considering UAV in the aerial user equipment (UE) scenario, the main challenges include interference management, handover management, latency control, and three‐dimensional localization. However, in both scenarios, channel modeling is an important design step in the implementation of UAV‐based communication network. This chapter provides an overview of the use of UAV as aerial UEs and aerial BSs and discusses the technical challenges related to AG channel modeling, airframe shadowing, optimal deployment of UAVs, trajectory optimization, resource management, and energy efficiency.
2.2 UAV Classification
The need for an appropriate type of UAV depends on the specific mission, environmental conditions, and civil aviation regulations to attain certain altitude. In addition, for any particular UAV‐enabled wireless networking application, several factors, such as the number of UAVs, their optimal deployment, and QoS requirement, must be taken in to account. The operational altitude of the UAV from the ground level can be categorized as LAP and HAP. UAVs in LAP can fly between the altitude ranges from tens of meters to a few kilometers [4]. However, civil aviation authorities of some countries have set the operational altitude of UAVs up to a few hundred meters to avoid airborne collision with commercial flights. For example, Table 2.1 lists the regulations of maximum allowable LAP deployment of UAVs in various countries without any specific permit [5]. HAPs, on the other hand, have altitudes above 17 km where UAVs are typically quasi‐stationary [1,4].
Table 2.1 Regulation for LAP deployment of UAVs in different countries.
Country
Maximum altitude (m)
Minimum distance to humans (m)
Minimum distance to airport (km)
US
122
—
8
UK
122
50
—
Chile
130
36
—
Australia
120
30
5.5
South Africa
46
50
10
For time‐sensitive applications such as emergency services, LAPs are more appropriate then HAPs due to their rapid deployment, quick mobility, and cost‐effectiveness. Furthermore, LAPs can be used for collecting sensor data from the ground. In this case, LAPs can be readily replaced or recharged as needed. In contrast, HAPs are preferred due to their long endurance (days or months) operations and wider ground coverage [1]. However, operational cost of HAPs is high and their deployment time is significantly longer.
UAV can also be categorized based on their structure into rotary‐wing and fixed‐wing UAVs. Rotary‐wing UAVs are powered by rotating blades, and based on the number of blades they are termed as either quadcopter with four blades, hexacopter with six blades, or octocopter with eight blades. On the other hand, fixed‐wing UAVs include those that are driven by propellers with small size engine and have wings that are fixed. However, the flight time of UAVs relies on several key factors, such as type, weight, speed, energy sources (battery or engine), and trajectory of the UAV.
Figure 2.1 Aerial user equipment and aerial base station.
2.3 UAV‐Enabled Wireless Communication
UAVs can operate as aerial UE as shown in Figure 2.1. For example, aerial surveillance can be a cost‐effective solution to provide access to those terrains that may be difficult to reach by humans in land vehicles. In this case, UAVs equipped with camera and sensors are used to gather video recordings and live images of a specific target on the ground and data from the sensor. Thereafter, the UAV has to coordinate with the ground user via existing cellular infrastructure and transfer the collected information with certain reliability, throughput, and delay while achieving the QoS requirements. The first scenario in Figure 2.1 (left side) requires a better connectivity between the aerial UE and at least one of the BSs installed typically at the ground. However, a performance drop is expected in the presence of aerial BSs acting as interferers. Moreover, the coexistence between the aerial UE, terrestrial UE, and the cellular infrastructure has to be studied.
On the other hand, UAVs provide power efficiency and mobility to deploy as aerial BS in the future wireless networks. In this case, the mobility of UAV can dynamically provide additional on‐demand capacity. This advantage of UAV‐enabled network can be exploited by service providers for densification of network, temporary coverage of an area, or quick network deployment in an emergency scenario. Moreover, localization service precision can be improved due to the favorable propagation conditions between the UAV and the ground user. The second scenario in Figure 2.1 (right side) requires a better link between one of the multiple aerial BSs and all the terrestrial UEs. In comparison with fixed BSs, the aerial BSs are capable of adjusting their altitude to provide good LoS propagation. However, the key challenge in this scenario is the optimum placement of aerial BSs to maximize the ground coverage for higher achievable throughput.
2.4 Channel Modeling in UAV Communications
In wireless communications, the propagation channel is the free space between the transmitter and the receiver. It is obvious that the performance of wireless networks is influenced by the characteristics of the propagation channel. Therefore, knowledge of wireless channels is pertinent in designing UAV‐enabled networks for future wireless communication. Furthermore, the characterization of radio channel and its modeling for UAV network architecture are crucial for the analysis of network performance.
Majority of the channel modeling efforts is devoted to the terrestrial radio channel with fixed infrastructure. However, these channel models may not be completely suitable for wireless communication using UAVs because of their mobility and small size. The AG channel between the UAV and the ground user implies higher link reliability and requires lower transmission power due to the higher probability of LoS propagation. In the case of NLoS, power variations are more severe because the ground‐based side of the AG link is surrounded by obstacles that adversely affect the propagation. Figure 2.2 depicts the AG propagation channel and shows the distinction between LoS and NLoS components of the channel, with being the propagation distance. Furthermore, temporal variations and the Doppler shift are caused by the UAV mobility. As a result, the arbitrary UAV mobility pattern and operational environment are challenges in modeling the AG channel. Apart from the AG propagation channel, other factors such as airframe shadowing and on‐board antenna placement and characteristics can influence the received power strength.
Figure 2.2 Air‐to‐ground propagation in UAV‐assisted cellular network.
In addition, AA channels between airborne UAVs mostly experience strong LoS similar to the high‐altitude AG channels. However, Doppler shift is higher because UAV mobility is significantly higher and it is difficult to maintain alignment between multiple UAVs.
Accurate AG and AA propagation channel models are imperative for the optimal deployment and the design of the UAV communication networks. This section will discuss recent efforts in the modeling of AG and AA propagation channels.
2.4.1 Background
In wireless communications, several propagation phenomena occur when electromagnetic waves radiate from the transmitter in several directions and interact with the surrounding environment before reaching the receiver. As shown in Figure 2.3, propagation phenomena such as reflection, scattering, diffraction, and penetration occur due to the natural obstacles and buildings, which provoke the multiple realization of the signal transmitted from the UAV, often known as multipath components (MPC). Thus, each component received at the receiver with different amplitude, phase, and delay, and the resultant signal is a superposition of multiple copies of the transmitted signal, which can interfere either constructively or destructively depending on their respective random phases [6]. Typically, several fading mechanisms are added linearly in dB to represent the radio channel as
Figure 2.3 Multipath air‐to‐ground propagation in urban setting.
where is the distance‐dependent path loss, is the large‐scale fading consisting of power variation on a large scale due to the environment, and is the small‐scale fading. Parameters of channel model, such as path loss exponent and LoS probability, are dependent on the altitude level because propagation conditions change at different altitudes. The airspace is often segregated into three propagation echelons or slices as follows:
Terrestrial channel
: For suburban and urban environments, altitude is between 10 and 22.5 m, respectively
[7]
. In this case, the terrestrial channel models can be used to model AG propagation because the airborne UAV is below the rooftop level. As a result, NLoS is the dominant component in the propagation.
Obstructed AG channel
: For suburban and urban environments, altitude is 10–40 m and 22.5–100 m, respectively. In this case, LoS probability is higher than that of the terrestrial channels.
High‐altitude AG channel
: All channels are in LoS for the altitude ranges between 100 and 300 m or above. Consequently, the propagation is similar to that in the free space case. Moreover, no shadowing is experienced for these channels.
2.4.1.1 Path Loss and Large‐Scale Fading
Air‐to‐Air Channel Free space path loss model is the simplest channel model to represent the AA propagation at a relatively high altitude. Thus, the received power is given by [6]
where denotes the transmitted signal power, and represent the gain of the transmitter and receiver antennas, respectively, is the ground distance between the transmitter and receiver, and is the carrier wavelength. Path loss exponent is the rate of distance‐dependent power loss, where varies with environments. In Eq. (2.2), for free space propagation. Therefore, the distance‐dependent path loss expression can be generalized as
Air‐to‐Ground Channel In urban environment, the AG channel may not experience complete free space propagation. In the existing literature on UAV communications, the log‐distance model is the prominently used path loss model due to its simplicity and applicability when environmental parameters are difficult to define. Therefore, path loss in dB is given by
where is the path loss for the reference distance . For the same propagation distance between the ground device and the UAV, large‐scale variations are different at different locations within the same environment because the materials of obstacles vary from each other, which affects the radio signal propagation. As a result, at any distance , in Eq. (2.1) is the shadow fading measured in dB and modeled as the normal random variable with variance in dB. This model is extensively applied for modeling of the terrestrial channels. Table 2.2 lists some measurement campaigns for the estimations of path loss and large‐scale effects.
Another popular channel model to characterize the AG propagation in UAV communications is the probabilistic path loss model in [4] and [17]. In [17], the path loss between the ground device and the UAV is dependent on the position of the UAV and the propagation environments (e.g. suburban, urban, dense‐urban, high‐rise). Consequently, during the AG radio propagation, the communication link can be either LoS or NLoS depending on the environment. Many of the existing works [18–35] on UAV communications adopted the probabilistic path loss model of [4] and [17]. In these works, the probability of occurrence of LoS and NLoS links are functions of the environmental parameters, height of the buildings, and the elevation angle between the ground device and the UAV. This model is based on environmental parameters defined in the recommendations of the International Telecommunication Union (ITU). In particular, ITU‐R provides statistical parameters related to the environment that determine the height, number, and density of the buildings or obstacles. For instance, in [36], the height of the buildings can be modeled by using the Rayleigh distribution. The average path loss for the AG propagation in [17] is given as
where and are the LoS and NLoS path loss, respectively, for the free space propagation. is the LoS probability given as
Table 2.2 Measurement campaigns to characterize the path loss and large‐scale AG propagation fading.
References
Scenario
(dB)
(dB)
Yanmaz et al.
[8]
Urban/Open field
2.2–2.6
—
—
Yanmaz et al.
[9]
Open field
2.01
—
—
Ahmed et al.
[10]
—
2.32
—
—
Khawaja et al.
[11]
Suburban/Open field
2.54–3.037
21.9–34.9
2.79–5.3
Newhall et al.
[12]
Urban/Rural
4.1
—
5.24
Tu and Shimamoto
[13]
Near airports
2–2.25
—
—
Matolak and Sun
[14]
Suburban
1.7 (L‐band)
98.2–99.4 (L‐band)
2.6–3.1 (L‐band)
1.5–2 (C‐band)
110.4–116.7 (C‐band)
2.9–3.2 (C‐band)
Sun and Matolak
[15]
Mountains
1–1.8
96.1–123.9
2.2–3.9
Meng and Lee
[16]
Over sea
1.4–2.46
19–129
—
where and are the constant values related to the environment, is the elevation angle between the ground user and the UAV, is the altitude of the UAV, and is the distance between the ground projection of the UAV and the ground device. According to Eq. (2.6), as the elevation angle increases with the UAV altitude, the blockage effect decreases and the AG propagation becomes more LoS. An advantage of this model is that it is applicable for different environments and for different UAV altitudes. However, it is unable to capture the impact of path loss for AG propagation in mountainous regions and over water bodies due to the lack of information related to their statistical parameters.
Conventional well‐known channel models for cellular communications can be used for UAV communications for UAV altitude between 1.5 and 10 m. One such model for the macro‐cell network was designed for the rural environment by the 3rd Generation Partnership Project (3GPP) in [7,37].
Since LoS and NLoS links are treated separately, the probability of LoS propagation is expressed as
Path loss and large‐scale fading can be calculated once the LoS probability is known from Eq. (2.7). As the communication nodes change their position, path loss also changes and can be found as
where
with , , , and being the carrier frequency, height of ground BS, the average width of street, and the speed of light, respectively.
For the obstructed AG propagation with the UAV altitude between 10 and 40 m, the LoS probability in the rural environment for the macro‐cell network can be computed as [7]
where
The path loss for LoS and NLoS links can be computed as
For a high‐altitude AG channel with , the LoS probability is 1 and the path loss can be formulated as Eq. (2.17).
2.4.1.2 Small‐Scale Fading
Small‐scale fading refers to the random fluctuations of amplitude and phase of the received signal over a short distance or a short period of time due to constructive or destructive interference of the MPC. For different propagation environments and wireless systems, different distribution models are suggested to analyze the random variations in the received signal envelop. The Rician and Rayleigh distributions are widely used models in the literature of wireless communications, where both are based on the central limit theorem. The Rician distribution provides better fit for the AA and AG channels, where the impact of LoS propagation is stronger. On the other hand, when the MPC impinges at the receiver with random amplitude and phase, the small‐scale fading effect can be captured by the Rayleigh distribution [6].
Geometrical analysis, numerical simulations, and empirical data are used to obtain the stochastic fading models [38–40]. Geometry‐based stochastic channel model (GBSCM) is the most popular type of small‐scale fading model. GBSCM is subdivided into regular‐shaped geometry‐based stochastic channel model (RS‐GBSCM) and irregular‐shaped geometry‐based stochastic channel model (IS‐GBSCM). Time‐variant IS‐GBSCM was presented in [41] and RS‐GBSCM was presented in [42] and [43].These works illustrated Rician distribution for small‐scale fading. In [44], non‐geometric stochastic channel model (NGSCM) was provided, where small‐scale effects of AG propagation were modeled by using Rician and Loo models. Table 2.3 provides the measured characteristics of small‐scale fading of AG propagation in different environments.
Table 2.3 Measured small‐scale fading of AG propagation in different environments.
References
Scenario
Frequency band
Fading distribution
Khawaja et al.
[11]
Suburban/Open field
Ultra‐wideband
Nakagami
Newhall et al.
[12]
Urban/Suburban
Wideband
Rayleigh, Rician
Tu and Shimamoto
[13]
Urban/Suburban
Wideband
Rician
Matolak and Sun
[14]
Urban/Suburban
Wideband
Rician
Simunek et al.
[45]
Urban/Suburban
Narrowband
Rician
Cid et al.
[46]
Forest/Foliage
Ultra‐wideband
Rician, Nakagami
Matolak and Sun
[47]
Sea/Fresh water
Wideband
Rician
