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E-raamat: UAV Communications for 5G and Beyond

Edited by (North Carolina State University, NC, USA), Edited by , Edited by (National University of Singapore), Edited by (National Mobile Communications Research Laboratory, Southeast University, China; Purple Mountain Laboratories, Nanjing, China), Edited by (Universitat Pompeu Fabra,)
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  • Ilmumisaeg: 07-Dec-2020
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119575672
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  • Formaat: PDF+DRM
  • Sari: IEEE Press
  • Ilmumisaeg: 07-Dec-2020
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119575672
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Explore foundational and advanced issues in UAV cellular communications with this cutting-edge and timely new resource

UAV Communications for 5G and Beyond delivers a comprehensive overview of the potential applications, networking architectures, research findings, enabling technologies, experimental measurement results, and industry standardizations for UAV communications in cellular systems. The book covers both existing LTE infrastructure, as well as future 5G-and-beyond systems.

UAV Communications covers a range of topics that will be of interest to students and professionals alike. Issues of UAV detection and identification are discussed, as is the positioning of autonomous aerial vehicles. More fundamental subjects, like the necessary tradeoffs involved in UAV communication are examined in detail.

The distinguished editors offer readers an opportunity to improve their ability to plan and design for the near-future, explosive growth in the number of UAVs, as well as the correspondingly demanding systems that come with them. Readers will learn about a wide variety of timely and practical UAV topics, like:

  • Performance measurement for aerial vehicles over cellular networks, particularly with respect to existing LTE performance
  • Inter-cell interference coordination with drones
  • Massive multiple-input and multiple-output (MIMO) for Cellular UAV communications, including beamforming, null-steering, and the performance of forward-link C&C channels
  • 3GPP standardization for cellular-supported UAVs, including UAV traffic requirements, channel modeling, and interference challenges
  • Trajectory optimization for UAV communications

Perfect for professional engineers and researchers working in the field of unmanned aerial vehicles, UAV Communications for 5G and Beyond also belongs on the bookshelves of students in masters and PhD programs studying the integration of UAVs into cellular communication systems.

List of Contributors xvii
Acronyms xxi
Part I Fundamentals of UAV Communications 1(102)
1 Overview
3(14)
Qingqing Wu
Yong Zeng
Rui Zhang
1.1 UAV Definitions, Classes, and Global Trend
3(1)
1.2 UAV Communication and Spectrum Requirement
4(2)
1.3 Potential Existing Technologies for UAV Communications
6(3)
1.3.1 Direct Link
6(1)
1.3.2 Satellite
7(1)
1.3.3 Ad-Hoc Network
8(1)
1.3.4 Cellular Network
8(1)
1.4 Two Paradigms in Cellular UAV Communications
9(2)
1.4.1 Cellular-Connected UAVs
9(1)
1.4.2 UAV-Assisted Wireless Communications
10(1)
1.5 New Opportunities and Challenges
11(2)
1.5.1 High Altitude
11(1)
1.5.2 High LoS Probability
12(1)
1.5.3 High 3D Mobility
12(1)
1.5.4 SWAP Constraints
13(1)
1.6
Chapter Summary and Main Organization of the Book
13(2)
References
15(2)
2 A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles
17(54)
Wahab Khawaja
Ismail Guvenc
David W. Matolak
Uwe-Carsten Fiebig
Nicolas Schneckenberger
2.1 Introduction
17(3)
2.2 Literature Review
20(2)
2.2.1 Literature Review on Aerial Propagation
20(1)
2.2.2 Existing Surveys on UAV AG Propagation
21(1)
2.3 UAV AG Propagation Characteristics
22(3)
2.3.1 Comparison of UAV AG and Terrestrial Propagation
22(1)
2.3.2 Frequency Bands for UAV AG Propagation
23(1)
2.3.3 Scattering Characteristics for AG Propagation
24(1)
2.3.4 Antenna Configurations for AG Propagation
24(1)
2.3.5 Doppler Effects
25(1)
2.4 AG Channel Measurements: Configurations, Challenges, Scenarios, and Waveforms
25(8)
2.4.1 Channel Measurement Configurations
26(3)
2.4.2 Challenges in AG Channel Measurements
29(1)
2.4.3 AG Propagation Scenarios
29(1)
2.4.3.1 Open Space
31(1)
2.4.3.2 Hilly/Mountainous
31(1)
2.4.3.3 Forest
32(1)
2.4.3.4 Water/Sea
32(1)
2.4.4 Elevation Angle Effects
32(1)
2.5 UAV AG Propagation Measurement and Simulation Results in the Literature
33(8)
2.5.1 Path Loss/Shadowing
33(3)
2.5.2 Delay Dispersion
36(1)
2.5.3 Narrowband Fading and Ricean K-factor
36(1)
2.5.4 Doppler Spread
37(1)
2.5.5 Effects of UAV AG Measurement Environment
37(1)
2.5.5.1 Urban/Suburban
38(1)
2.5.5.2 Rural/Open Field
38(1)
2.5.5.3 Mountains/Hilly, Over Sea, Forest
39(1)
2.5.6 Simulations for Channel Characterization
40(1)
2.6 UAV AG Propagation Models
41(19)
2.6.1 AG Propagation Channel Model Types
41(1)
2.6.2 Path-Loss and Large-Scale Fading Models
42(1)
2.6.2.1 Free-Space Path-Loss Model
43(1)
2.6.2.2 Floating-Intercept Path-Loss Model
43(1)
2.6.2.3 Dual-Slope Path-Loss Model
43(1)
2.6.2.4 Log-Distance Path-Loss Model
45(1)
2.6.2.5 Modified FSPL Model
45(1)
2.6.2.6 Two-Ray PL Model
45(1)
2.6.2.7 Log-Distance FI Model
45(1)
2.6.2.8 LOS/NLOS Mixture Path-Loss Model
46(1)
2.6.3 Airframe Shadowing
47(1)
2.6.4 Small-Scale Fading Models
47(1)
2.6.5 Intermittent MPCs
48(3)
2.6.6 Effect of Frequency Bands on Channel Models
51(1)
2.6.7 MIMO AG Propagation Channel Models
52(2)
2.6.8 Comparison of Different AG Channel Models
54(1)
2.6.8.1 Large-Scale Fading Models
54(1)
2.6.8.2 Small-Scale Fading Models
54(1)
2.6.9 Comparison of Traditional Channel Models with UAV AG Propagation Channel Models
55(1)
2.6.10 Ray Tracing Simulations
56(2)
2.6.11 3GPP Channel Models for UAVs
58(2)
2.7 Conclusions
60(1)
References
60(11)
3 UAV Detection and Identification
71(32)
Martins Ezuma
Fatih Erden
Chethan Kumar Anjinappa
Ozgur Ozdemir
Ismail Guvenc
David Matolak
3.1 Introduction
71(4)
3.2 RF-Based UAV Detection Techniques
75(2)
3.2.1 RF Fingerprinting Technique
76(1)
3.2.2 WiFi Fingerprinting Technique
76(1)
3.3 Multistage UAV RF Signal Detection
77(12)
3.3.1 Preprocessing Step: Multiresolution Analysis
78(4)
3.3.2 The Naive Bayesian Decision Mechanism for RF Signal Detection
82(2)
3.3.3 Detection of WiFi and Bluetooth Interference
84(5)
3.4 UAV Classification Using RF Fingerprints
89(3)
3.4.1 Feature Selection Using Neighborhood Components Analysis (NCA)
91(1)
3.5 Experimental Results
92(8)
3.5.1 Experimental Setup
92(2)
3.5.2 Detection Results
94(1)
3.5.3 UAV Classification Results
95(5)
3.6 Conclusion
100(1)
Acknowledgments
100(1)
References
100(3)
Part II Cellular-Connected UAV Communications 103(128)
4 Performance Analysis for Cellular-Connected UAVs
105(34)
M. Mandi Azari
Fernando Rosas
Sofie Pollin
4.1 Introduction
105(4)
4.1.1 Motivation
105(2)
4.1.2 Related Works
107(1)
4.1.3 Contributions and
Chapter Structure
108(1)
4.2 Modelling Preliminaries
109(3)
4.2.1 Stochastic Geometry
109(1)
4.2.2 Network Architecture
110(1)
4.2.3 Channel Model
111(1)
4.2.4 Blockage Modeling and LoS Probability
112(1)
4.2.5 User Association Strategy and Link SINR
112(1)
4.3 Performance Analysis
112(7)
4.3.1 Exact Coverage Probability
113(2)
4.3.2 Approximations for UAV Coverage Probability
115(1)
4.3.2.1 Discarding NLoS and Noise Effects
116(1)
4.3.2.2 Moment Matching
116(2)
4.3.3 Achievable Throughput and Area Spectral Efficiency Analysis
118(1)
4.4 System Design: Study Cases and Discussion
119(10)
4.4.1 Analysis of Accuracy
119(1)
4.4.2 Design Parameters
120(1)
4.4.2.1 Impact of UAV Altitude
120(1)
4.4.2.2 Impact of UAV Antenna Beamwidth
121(1)
4.4.2.3 Impact of UAV Antenna Tilt
123(1)
4.4.2.4 Impact of Different Types of Environment
123(2)
4.4.3 Heterogeneous Networks - Tier Selection
125(2)
4.4.4 Network Densification
127(2)
4.5 Conclusion
129(7)
References
136(3)
5 Performance Enhancements for LTE-Connected UAVs: Experiments and Simulations
139(24)
Rafhael Medeiros de Amorim
Jeroen Wigard
Istvan Z. Kovacs
Troels B. Sorensen
5.1 Introduction
139(1)
5.2 LTE Live Network Measurements
140(9)
5.2.1 Downlink Experiments
141(4)
5.2.2 Path-Loss Model Characterization
145(1)
5.2.3 Uplink Experiments
145(4)
5.3 Performance in LTE Networks
149(1)
5.4 Reliability Enhancements
150(9)
5.4.1 Interference Cancellation
151(1)
5.4.2 Inter-Cell Interference Control
152(1)
5.4.3 CoMP
152(1)
5.4.4 Antenna Beam Selection
153(2)
5.4.5 Dual LTE Access
155(3)
5.4.6 Dedicated Spectrum
158(1)
5.4.7 Discussion
158(1)
5.5 Summary and Outlook
159(1)
References
160(3)
6 3GPP Standardization for Cellular-Supported UAVs
163(18)
Helka-Liina Maattanen
6.1 Short Introduction to LTE and NR
163(4)
6.1.1 LTE Physical Layer and MIMO
165(1)
6.1.2 NR Physical Layer and MIMO
166(1)
6.2 Drones Served by Mobile Networks
167(5)
6.2.1 Interference Detection and Mitigation
168(2)
6.2.2 Mobility for Drones
170(1)
6.2.3 Need for Drone Identification and Authorization
171(1)
6.3 3GPP Standardization Support for UAVs
172(5)
6.3.1 Measurement Reporting Based on RSRP Level of Multiple Cells
172(2)
6.3.2 Height, Speed, and Location Reporting
174(1)
6.3.3 Uplink Power Control Enhancement
175(1)
6.3.4 Flight Path Signalling
175(1)
6.3.5 Drone Authorization and Identification
176(1)
6.4 Flying Mode Detection in Cellular Networks
177(2)
References
179(2)
7 Enhanced Cellular Support for UAVs with Massive MIMO
181(22)
Giovanni Geraci
Adrian Garcia-Rodriguez
Lorenzo Galati Giordano
David Lopez-Perez
7.1 Introduction
181(1)
7.2 System Model
181(6)
7.2.1 Cellular Network Topology
183(1)
7.2.2 System Model
184(2)
7.2.3 Massive MIMO Channel Estimation
186(1)
7.2.4 Massive MIMO Spatial Multiplexing
186(1)
7.3 Single-User Downlink Performance
187(3)
7.3.1 UAV Downlink C&C Channel
187(3)
7.4 Massive MIMO Downlink Performance
190(4)
7.4.1 UAV Downlink C&C Channel
190(2)
7.4.2 UAV-GUE Downlink Interplay
192(2)
7.5 Enhanced Downlink Performance
194(3)
7.5.1 UAV Downlink C&C Channel
195(1)
7.5.2 UAV-GUE Downlink Interplay
196(1)
7.6 Uplink Performance
197(2)
7.6.1 UAV Uplink C&C Channel and Data Streaming
197(1)
7.6.2 UAV-GUE Uplink Interplay
198(1)
7.7 Conclusions
199(1)
References
200(3)
8 High-Capacity Millimeter Wave UAV Communications
203(28)
Nuria Gonzalez-Prelcic
Robert W. Heath
Cristian Rusu
Aldebaro Klautau
8.1 Motivation
203(3)
8.2 UAV Roles and Use Cases Enabled by Millimeter Wave Communication
206(2)
8.2.1 UAV Roles in Cellular Networks
206(1)
8.2.2 UAV Use Cases Enabled by High-Capacity Cellular Networks
207(1)
8.3 Aerial Channel Models at Millimeter Wave Frequencies
208(7)
8.3.1 Propagation Considerations for Aerial Channels
208(1)
8.3.1.1 Atmospheric Considerations
208(1)
8.3.1.2 Blockages
210(1)
8.3.2 Air-to-Air Millimeter Wave Channel Model
211(1)
8.3.3 Air-to-Ground Millimeter Wave Channel Model
212(2)
8.3.4 Ray Tracing as a Tool to Obtain Channel Measurements
214(1)
8.4 Key Aspects of UAV MIMO Communication at mmWave Frequencies
215(4)
8.5 Establishing Aerial mmWave MIMO Links
219(3)
8.5.1 Beam Training and Tracking for UAV Millimeter Wave Communication
219(1)
8.5.2 Channel Estimation and Tracking in Aerial Environments
219(2)
8.5.3 Design of Hybrid Precoders and Combiners
221(1)
8.6 Research Opportunities
222(1)
8.6.1 Sensing at the Tower
222(1)
8.6.2 Joint Communication and Radar
222(1)
8.6.3 Positioning and Mapping
223(1)
8.7 Conclusions
223(1)
References
223(8)
Part III UAV-Assisted Wireless Communications 231(118)
9 Stochastic Geometry-Based Performance Analysis of Drone Cellular Networks
233(22)
Morteza Banagar
Vishnu V. Chetlur
Harpreet S. Dhillon
9.1 Introduction
233(2)
9.2 Overview of the System Model
235(3)
9.2.1 Spatial Model
235(1)
9.2.2 3GPP-Inspired Mobility Model
236(1)
9.2.3 Channel Model
237(1)
9.2.4 Metrics of Interest
237(1)
9.3 Average Rate
238(4)
9.4 Handover Probability
242(4)
9.5 Results and Discussion
246(4)
9.5.1 Density of Interfering DBSs
247(1)
9.5.2 Average Rate
247(2)
9.5.3 Handover Probability
249(1)
9.6 Conclusion
250(1)
Acknowledgment
251(1)
References
251(4)
10 UAV Placement and Aerial-Ground Interference Coordination
255(28)
Abhaykumar Kumbhar
Ismail Guvenc
10.1 Introduction
255(1)
10.2 Literature Review
256(3)
10.3 UABS Use Case for AG-HetNets
259(1)
10.4 UABS Placement in AG-HetNet
260(4)
10.5 AG-HetNet Design Guidelines
264(2)
10.5.1 Path-Loss Model
265(1)
10.5.1.1 Log-Distance Path-Loss Model
265(1)
10.5.1.2 Okumura-Hata Path-Loss Model
266(1)
10.6 Inter-Cell Interference Coordination
266(4)
10.6.1 UE Association and Scheduling
269(1)
10.7 Simulation Results
270(9)
10.7.1 5pSE with UABSs Deployed on Hexagonal Grid
270(1)
10.7.1.1 5pSE with Log-Normal Path-Loss Model
270(1)
10.7.1.2 5pSE with Okumura-Hata Path-Loss Model
271(2)
10.7.2 5pSE with GA-Based UABS Deployment Optimization
273(1)
10.7.2.1 5pSE with Log-Normal Path-Loss Model
273(1)
10.7.2.2 5pSE with Okumura-Hata Path-Loss model
275(1)
10.7.3 Performance Comparison Between Fixed (Hexagonal) and Optimized UABS Deployment with eICIC and FeICIC
276(1)
10.7.3.1 Influence of LDPLM on 5pSE
277(1)
10.7.3.2 Influence of OHPLM on 5pSE
277(1)
10.7.4 Comparison of Computation Time for Different UABS Deployment Algorithms
277(2)
10.8 Concluding remarks
279(1)
References
279(4)
11 Joint Trajectory and Resource Optimization
283(16)
Yong Zeng
Qingqing Wu
Rui Zhang
11.1 General Problem Formulation
283(2)
11.2 Initial Path Planning via the Traveling Salesman and Pickup-and-Delivery Problems
285(5)
11.2.1 TSP without Return
286(1)
11.2.2 TSP with Given Initial and Final Locations
287(1)
11.2.3 TSP with Neighborhood
287(1)
11.2.4 Pickup-and-Delivery Problem
288(2)
11.3 Trajectory Discretization
290(1)
11.3.1 Time Discretization
290(1)
11.3.2 Path Discretization
291(1)
11.4 Block Coordinate Descent
291(1)
11.5 Successive Convex Approximation
292(3)
11.6 Unified Algorithm
295(1)
11.7 Summary
296(1)
References
296(3)
12 Energy-Efficient UAV Communications
299(16)
Yong Zeng
Rui Zhang
12.1 UAV Energy Consumption Model
299(7)
12.1.1 Fixed-Wing Energy Model
300(1)
12.1.1.1 Forces on a UAV
300(1)
12.1.1.2 Straight and Level Flight
301(1)
12.1.1.3 Circular Flight
302(1)
12.1.1.4 Arbitrary Level Flight
303(1)
12.1.1.5 Arbitrary 3D Flight
304(1)
12.1.2 Rotary-Wing Energy Model
304(2)
12.2 Energy Efficiency Maximization
306(4)
12.3 Energy Minimization with Communication Requirement
310(2)
12.4 UAV-Ground Energy Trade-off
312(1)
12.5
Chapter Summary
312(1)
References
313(2)
13 Fundamental Trade-Offs for UAV Communications
315(14)
Qingqing Wu
Liang Liu
Yong Zeng
Rui Zhang
13.1 Introduction
315(2)
13.2 Fundamental Trade-offs
317(2)
13.2.1 Throughput-Delay Trade-Off
317(1)
13.2.2 Throughput-Energy Trade-Off
318(1)
13.2.3 Delay-Energy Trade-Off
319(1)
13.3 Throughput-Delay Trade-Off
319(4)
13.3.1 Single-UAV-Enabled Wireless Network
319(2)
13.3.2 Multi-UAV-Enabled Wireless Network
321(2)
13.4 Throughput-Energy Trade-Off
323(2)
13.4.1 UAV Propulsion Energy Consumption Model
323(1)
13.4.2 Energy-Constrained Trajectory Optimization
324(1)
13.5 Further Discussions and Future Work
325(2)
13.6
Chapter Summary
327(1)
References
327(2)
14 UAV-Cellular Spectrum Sharing
329(20)
Chiya Zhang
Wei Zhang
14.1 Introduction
329(4)
14.1.1 Cognitive Radio
329(1)
14.1.1.1 Overlay Spectrum Sharing
329(1)
14.1.1.2 Underlay Spectrum Sharing
330(1)
14.1.2 Drone Communication
330(1)
14.1.2.1 UAV Spectrum Sharing
331(1)
14.1.2.2 UAV Spectrum Sharing with Exclusive Regions
332(1)
14.1.3
Chapter Overview
333(1)
14.2 SNR Meta-Distribution of Drone Networks
333(5)
14.2.1 Stochastic Geometry Analysis
333(1)
14.2.2 Characteristic Function of the SNR Meta-Distribution
334(4)
14.2.3 LOS Probability
338(1)
14.3 Spectrum Sharing of Drone Networks
338(7)
14.3.1 Spectrum Sharing in Single-Tier DSCs
339(3)
14.3.2 Spectrum Sharing with Cellular Network
342(3)
14.4 Summary
345(1)
References
346(3)
Part IV Other Advanced Technologies for UAV Communications 349(84)
15 Non-Orthogonal Multiple Access for UAV Communications
351(22)
Tianwei Hou
Yuanwei Liu
Xin Sun
15.1 Introduction
351(1)
15.1.1 Motivation
352(1)
15.2 User-Centric Strategy for Emergency Communications
352(7)
15.2.1 System Model
354(1)
15.2.1.1 Far user case
354(1)
15.2.1.2 Near user case
355(1)
15.2.2 Coverage Probability of the User-Centric Strategy
356(3)
15.3 UAV-Centric Strategy for Offloading Actions
359(5)
15.3.1 SINR Analysis
360(1)
15.3.2 Coverage Probability of the UAV-Centric Strategy
361(3)
15.4 Numerical Results
364(5)
15.4.1 User-Centric Strategy
365(2)
15.4.2 UAV-Centric Strategy
367(2)
15.5 Conclusions
369(1)
References
369(4)
16 Physical Layer Security for UAV Communications
373(26)
Nadisanka Rupasinghe
Yavuz Yapici
Ismail Guvenc
Huaiyu Dai
Arupjyoti Bhuyan
16.1 Introduction
373(1)
16.2 Breaching Security in Wireless Networks
374(1)
16.2.1 Denial-of-Service Attacks
374(1)
16.2.2 Masquerade Attacks
374(1)
16.2.3 Message Modification Attacks
374(1)
16.2.4 Eavesdropping Intruders
375(1)
16.2.5 Traffic Analysis
375(1)
16.3 Wireless Network Security Requirements
375(1)
16.3.1 Authenticity
375(1)
16.3.2 Confidentiality
376(1)
16.3.3 Integrity
376(1)
16.3.4 Availability
376(1)
16.4 Physical Layer Security
376(3)
16.4.1 Physical Layer versus Upper Layers
377(1)
16.4.2 Physical Layer Security Techniques
377(1)
16.4.2.1 Artificial Noise
378(1)
16.4.2.2 Cooperative Jamming
378(1)
16.4.2.3 Protected Zone
378(1)
16.5 Physical Layer Security for UAVs
379(4)
16.5.1 UAV Trajectory Design to Enhance PLS
379(2)
16.5.2 Cooperative Jamming to Enhance PLS
381(1)
16.5.3 Spectral- and Energy-Efficient PLS Techniques
382(1)
16.6 A Case Study: Secure UAV Transmission
383(9)
16.6.1 System Model
383(1)
16.6.1.1 Location Distribution and mmWave Channel Model
385(1)
16.6.2 Protected Zone Approach for Enhancing PLS-
385(1)
16.6.3 Secure NOMA for UAV BS Downlink
386(1)
16.6.3.1 Secrecy Outage and Sum Secrecy Rates
386(1)
16.6.3.2 Shape Optimization for Protected Zone
388(1)
16.6.3.3 Numerical Results
389(1)
16.6.3.4 Location of the Most Detrimental Eavesdropper
389(1)
16.6.3.5 Impact of the Protected Zone Shape on Secrecy Rates
390(1)
16.6.3.6 Variation of Secrecy Rates with Altitude
391(1)
Summary
392(1)
References
393(6)
17 UAV-Enabled Wireless Power Transfer
399(18)
Jie Xu
Yong Zeng
Rui Zhang
17.1 Introduction
399(2)
17.2 System Model
401(1)
17.3 Sum-Energy Maximization
402(1)
17.4 Min-Energy Maximization under Infinite Charging Duration
403(4)
17.4.1 Multi-Location-Hovering Solution
404(3)
17.5 Min-Energy Maximization Under Finite Charging Duration
407(4)
17.5.1 Successive Hover-and-Fly Trajectory Design
407(1)
17.5.1.1 Flying Distance Minimization to Visit r Hovering Locations
407(1)
17.5.1.2 Hovering Time Allocation When T > or = to Tfly
408(1)
17.5.1.3 Trajectory Refinement When T < Tfly
409(1)
17.5.2 SCA-Based Trajectory Design
409(2)
17.6 Numerical Results
411(2)
17.7 Conclusion and Future Research Directions
413(2)
References
415(2)
18 Ad-Hoc Networks in the Sky
417(16)
Kamesh Namuduri
18.1 Communication Support for UAVs
417(4)
18.1.1 Satellite Connectivity
418(2)
18.1.2 Cellular Connectivity
420(1)
18.1.3 Aerial Connectivity
420(1)
18.2 The Mobility Challenge
421(2)
18.2.1 UAS-to-UAS Communication
421(1)
18.2.2 Mobility Models
422(1)
18.3 Establishing an Ad-Hoc Network
423(3)
18.3.1 Network Addressing
424(1)
18.3.2 Routing
425(1)
18.4 Standards
426(1)
18.4.1 ASTM: Remote ID for UAS
426(1)
18.4.2 EUROCAE: Safe, Secure, and Efficient UAS Operations
426(1)
18.4.3 3GPP: 4G LTE and 5G Support for Connected UAS Operations
426(1)
18.4.4 IEEE P1920.1: Aerial Communications and Networking Standards
427(1)
18.4.5 IEEE P1920.2: Vehicle-to-Vehicle Communications Standard for UAS
427(1)
18.5 Technologies and Products
427(1)
18.5.1 Silvus Streamcaster
427(1)
18.5.2 goTenna
427(1)
18.5.3 MPU5 and Wave Relay from Persistent Systems
428(1)
18.5.4 Kinetic Mesh Networks from Rajant
428(1)
18.6 Software-Defined Network as a Solution for UAV Networks
428(1)
18.7 Summary
429(1)
References
429(4)
Index 433
Yong Zeng is a Professor at the National Mobile Communications Research Laboratory, Southeast University, China, and also with the Purple Mountain Laboratories, Nanjing, China. He is recognized as a Highly Cited Researcher by Web of Science Group. He is the recipient of IEEE Communications Society Asia-Pacific Outstanding Young Researcher Award and IEEE Marconi Prize Paper Award in Wireless Communications.

Ismail Guvenc is a Professor at North Carolina State University in the United States. He formerly worked with DOCOMO Innovations, Florida International University, and Mitsubishi Electric Research Labs. His recent research interests include 5G/6G wireless systems, aerial communications for UTM/AAM, and mmWave communications.

Rui Zhang is a Professor with the National University of Singapore. His current research interests include wireless information and power transfer, drone communication, and reconfigurable MIMO.

Giovanni Geraci is an Assistant Professor at Universitat Pompeu Fabra, Barcelona. He was previously with Nokia Bell Labs and holds a Ph.D. from UNSW Sydney. He is a "la Caixa" Junior Leader and a "Ramón y Cajal" Fellow, and the recipient of the IEEE ComSoc Europe, Middle East, and Africa Outstanding Young Researcher Award.

David W. Matolak is Professor at the University of South Carolina in the United States. He has over 20 years of experience in communication systems research, development, design, and deployment. He has worked with private firms, government institutions, and academic labs.
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