Uav Communications for 5g and Beyond

ISBN-13: 9781119575696 / Angielski / Twarda / 2020 / 464 str.

Yong Zeng; Ismail Guvenc; Rui Zhang

Spis treści

List of Contributors xviiAcronyms xxiPart I Fundamentals of UAV Communications 11 Overview 3Qingqing Wu, Yong Zeng, and Rui Zhang1.1 UAV Definitions, Classes, and Global Trend 31.2 UAV Communication and Spectrum Requirement 41.3 Potential Existing Technologies for UAV Communications 61.3.1 Direct Link 61.3.2 Satellite 71.3.3 Ad-Hoc Network 81.3.4 Cellular Network 81.4 Two Paradigms in Cellular UAV Communications 91.4.1 Cellular-Connected UAVs 91.4.2 UAV-Assisted Wireless Communications 101.5 New Opportunities and Challenges 111.5.1 High Altitude 111.5.2 High LoS Probability 121.5.3 High 3D Mobility 121.5.4 SWAP Constraints 131.6 Chapter Summary and Main Organization of the Book 13References 152 A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles 17Wahab Khawaja, Ismail Guvenc, David W. Matolak, Uwe-Carsten Fiebig, and Nicolas Schneckenberger2.1 Introduction 172.2 Literature Review 202.2.1 Literature Review on Aerial Propagation 202.2.2 Existing Surveys on UAV AG Propagation 212.3 UAV AG Propagation Characteristics 222.3.1 Comparison of UAV AG and Terrestrial Propagation 222.3.2 Frequency Bands for UAV AG Propagation 232.3.3 Scattering Characteristics for AG Propagation 242.3.4 Antenna Configurations for AG Propagation 242.3.5 Doppler Effects 252.4 AG Channel Measurements: Configurations, Challenges, Scenarios, and Waveforms 252.4.1 Channel Measurement Configurations 262.4.2 Challenges in AG Channel Measurements 292.4.3 AG Propagation Scenarios 292.4.3.1 Open Space 312.4.3.2 Hilly/Mountainous 312.4.3.3 Forest 322.4.3.4 Water/Sea 322.4.4 Elevation Angle Effects 322.5 UAV AG Propagation Measurement and Simulation Results in the Literature 332.5.1 Path Loss/Shadowing 332.5.2 Delay Dispersion 362.5.3 Narrowband Fading and Ricean K-factor 362.5.4 Doppler Spread 372.5.5 Effects of UAV AG Measurement Environment 372.5.5.1 Urban/Suburban 382.5.5.2 Rural/Open Field 382.5.5.3 Mountains/Hilly, Over Sea, Forest 392.5.6 Simulations for Channel Characterization 402.6 UAV AG Propagation Models 412.6.1 AG Propagation Channel Model Types 412.6.2 Path-Loss and Large-Scale Fading Models 422.6.2.1 Free-Space Path-Loss Model 432.6.2.2 Floating-Intercept Path-Loss Model 432.6.2.3 Dual-Slope Path-Loss Model 432.6.2.4 Log-Distance Path-Loss Model 452.6.2.5 Modified FSPL Model 452.6.2.6 Two-Ray PL Model 452.6.2.7 Log-Distance FI Model 452.6.2.8 LOS/NLOS Mixture Path-Loss Model 462.6.3 Airframe Shadowing 472.6.4 Small-Scale Fading Models 472.6.5 Intermittent MPCs 482.6.6 Effect of Frequency Bands on Channel Models 512.6.7 MIMO AG Propagation Channel Models 522.6.8 Comparison of Different AG Channel Models 542.6.8.1 Large-Scale Fading Models 542.6.8.2 Small-Scale Fading Models 542.6.9 Comparison of Traditional Channel Models with UAV AG Propagation Channel Models 552.6.10 Ray Tracing Simulations 562.6.11 3GPP Channel Models for UAVs 582.7 Conclusions 60References 603 UAV Detection and Identification 71Martins Ezuma, Fatih Erden, Chethan Kumar Anjinappa, Ozgur Ozdemir, Ismail Guvenc, and David Matolak3.1 Introduction 713.2 RF-Based UAV Detection Techniques 753.2.1 RF Fingerprinting Technique 763.2.2 WiFi Fingerprinting Technique 763.3 Multistage UAV RF Signal Detection 773.3.1 Preprocessing Step: Multiresolution Analysis 783.3.2 The Naive Bayesian Decision Mechanism for RF Signal Detection 823.3.3 Detection of WiFi and Bluetooth Interference 843.4 UAV Classification Using RF Fingerprints 893.4.1 Feature Selection Using Neighborhood Components Analysis (NCA) 913.5 Experimental Results 923.5.1 Experimental Setup 923.5.2 Detection Results 943.5.3 UAV Classification Results 953.6 Conclusion 100Acknowledgments 100References 100Part II Cellular-Connected UAV Communications 1034 Performance Analysis for Cellular-Connected UAVs 105M. Mahdi Azari, Fernando Rosas, and Sofie Pollin4.1 Introduction 1054.1.1 Motivation 1054.1.2 Related Works 1074.1.3 Contributions and Chapter Structure 1084.2 Modelling Preliminaries 1094.2.1 Stochastic Geometry 1094.2.2 Network Architecture 1104.2.3 Channel Model 1114.2.4 Blockage Modeling and LoS Probability 1124.2.5 User Association Strategy and Link SINR 1124.3 Performance Analysis 1124.3.1 Exact Coverage Probability 1134.3.2 Approximations for UAV Coverage Probability 1154.3.2.1 Discarding NLoS and Noise Effects 1164.3.2.2 Moment Matching 1164.3.3 Achievable Throughput and Area Spectral Efficiency Analysis 1184.4 System Design: Study Cases and Discussion 1194.4.1 Analysis of Accuracy 1194.4.2 Design Parameters 1204.4.2.1 Impact of UAV Altitude 1204.4.2.2 Impact of UAV Antenna Beamwidth 1214.4.2.3 Impact of UAV Antenna Tilt 1234.4.2.4 Impact of Different Types of Environment 1234.4.3 Heterogeneous Networks - Tier Selection 1254.4.4 Network Densification 1274.5 Conclusion 129References 1365 Performance Enhancements for LTE-Connected UAVs: Experiments and Simulations 139Rafhael Medeiros de Amorim, Jeroen Wigard, István Z. Kovács, and Troels B. Sørensen5.1 Introduction 1395.2 LTE Live Network Measurements 1405.2.1 Downlink Experiments 1415.2.2 Path-Loss Model Characterization 1455.2.3 Uplink Experiments 1455.3 Performance in LTE Networks 1495.4 Reliability Enhancements 1505.4.1 Interference Cancellation 1515.4.2 Inter-Cell Interference Control 1525.4.3 CoMP 1525.4.4 Antenna Beam Selection 1535.4.5 Dual LTE Access 1555.4.6 Dedicated Spectrum 1585.4.7 Discussion 1585.5 Summary and Outlook 159References 1606 3GPP Standardization for Cellular-Supported UAVs 163Helka-Liina Määttänen6.1 Short Introduction to LTE and NR 1636.1.1 LTE Physical Layer and MIMO 1656.1.2 NR Physical Layer and MIMO 1666.2 Drones Served by Mobile Networks 1676.2.1 Interference Detection and Mitigation 1686.2.2 Mobility for Drones 1706.2.3 Need for Drone Identification and Authorization 1716.3 3GPP Standardization Support for UAVs 1726.3.1 Measurement Reporting Based on RSRP Level of Multiple Cells 1726.3.2 Height, Speed, and Location Reporting 1746.3.3 Uplink Power Control Enhancement 1756.3.4 Flight Path Signalling 1756.3.5 Drone Authorization and Identification 1766.4 Flying Mode Detection in Cellular Networks 177References 1797 Enhanced Cellular Support for UAVs with Massive MIMO 181Giovanni Geraci, Adrian Garcia-Rodriguez, Lorenzo Galati Giordano, and David López-Pérez7.1 Introduction 1817.2 System Model 1817.2.1 Cellular Network Topology 1837.2.2 System Model 1847.2.3 Massive MIMO Channel Estimation 1867.2.4 Massive MIMO Spatial Multiplexing 1867.3 Single-User Downlink Performance 1877.3.1 UAV Downlink C&C Channel 1877.4 Massive MIMO Downlink Performance 1907.4.1 UAV Downlink C&C Channel 1907.4.2 UAV-GUE Downlink Interplay 1927.5 Enhanced Downlink Performance 1947.5.1 UAV Downlink C&C Channel 1957.5.2 UAV-GUE Downlink Interplay 1967.6 Uplink Performance 1977.6.1 UAV Uplink C&C Channel and Data Streaming 1977.6.2 UAV-GUE Uplink Interplay 1987.7 Conclusions 199References 2008 High-Capacity Millimeter Wave UAV Communications 203Nuria González-Prelcic, Robert W. Heath, Cristian Rusu, and Aldebaro Klautau8.1 Motivation 2038.2 UAV Roles and Use Cases Enabled by Millimeter Wave Communication 2068.2.1 UAV Roles in Cellular Networks 2068.2.2 UAV Use Cases Enabled by High-Capacity Cellular Networks 2078.3 Aerial Channel Models at Millimeter Wave Frequencies 2088.3.1 Propagation Considerations for Aerial Channels 2088.3.1.1 Atmospheric Considerations 2088.3.1.2 Blockages 2108.3.2 Air-to-Air Millimeter Wave Channel Model 2118.3.3 Air-to-Ground Millimeter Wave Channel Model 2128.3.4 Ray Tracing as a Tool to Obtain Channel Measurements 2148.4 Key Aspects of UAV MIMO Communication at mmWave Frequencies 2158.5 Establishing Aerial mmWave MIMO Links 2198.5.1 Beam Training and Tracking for UAV Millimeter Wave Communication 2198.5.2 Channel Estimation and Tracking in Aerial Environments 2198.5.3 Design of Hybrid Precoders and Combiners 2218.6 Research Opportunities 2228.6.1 Sensing at the Tower 2228.6.2 Joint Communication and Radar 2228.6.3 Positioning and Mapping 2238.7 Conclusions 223References 223Part III UAV-Assisted Wireless Communications 2319 Stochastic Geometry-Based Performance Analysis of Drone Cellular Networks 233Morteza Banagar, Vishnu V. Chetlur, and Harpreet S. Dhillon9.1 Introduction 2339.2 Overview of the System Model 2359.2.1 Spatial Model 2359.2.2 3GPP-Inspired Mobility Model 2369.2.3 Channel Model 2379.2.4 Metrics of Interest 2379.3 Average Rate 2389.4 Handover Probability 2429.5 Results and Discussion 2469.5.1 Density of Interfering DBSs 2479.5.2 Average Rate 2479.5.3 Handover Probability 2499.6 Conclusion 250Acknowledgment 251References 25110 UAV Placement and Aerial-Ground Interference Coordination 255Abhaykumar Kumbhar and Ismail Guvenc10.1 Introduction 25510.2 Literature Review 25610.3 UABS Use Case for AG-HetNets 25910.4 UABS Placement in AG-HetNet 26010.5 AG-HetNet Design Guidelines 26410.5.1 Path-Loss Model 26510.5.1.1 Log-Distance Path-Loss Model 26510.5.1.2 Okumura-Hata Path-Loss Model 26610.6 Inter-Cell Interference Coordination 26610.6.1 UE Association and Scheduling 26910.7 Simulation Results 27010.7.1 5pSE with UABSs Deployed on Hexagonal Grid 27010.7.1.1 5pSE with Log-Normal Path-Loss Model 27010.7.1.2 5pSE with Okumura-Hata Path-Loss Model 27110.7.2 5pSE with GA-Based UABS Deployment Optimization 27310.7.2.1 5pSE with Log-Normal Path-Loss Model 27310.7.2.2 5pSE with Okumura-Hata Path-Loss model 27510.7.3 Performance Comparison Between Fixed (Hexagonal) and Optimized UABS Deployment with eICIC and FeICIC 27610.7.3.1 Influence of LDPLM on 5pSE 27710.7.3.2 Influence of OHPLM on 5pSE 27710.7.4 Comparison of Computation Time for Different UABS Deployment Algorithms 27710.8 Concluding remarks 279References 27911 Joint Trajectory and Resource Optimization 283Yong Zeng, Qingqing Wu, and Rui Zhang11.1 General Problem Formulation 28311.2 Initial Path Planning via the Traveling Salesman and Pickup-and-Delivery Problems 28511.2.1 TSP without Return 28611.2.2 TSP with Given Initial and Final Locations 28711.2.3 TSP with Neighborhood 28711.2.4 Pickup-and-Delivery Problem 28811.3 Trajectory Discretization 29011.3.1 Time Discretization 29011.3.2 Path Discretization 29111.4 Block Coordinate Descent 29111.5 Successive Convex Approximation 29211.6 Unified Algorithm 29511.7 Summary 296References 29612 Energy-Efficient UAV Communications 299Yong Zeng and Rui Zhang12.1 UAV Energy Consumption Model 29912.1.1 Fixed-Wing Energy Model 30012.1.1.1 Forces on a UAV 30012.1.1.2 Straight and Level Flight 30112.1.1.3 Circular Flight 30212.1.1.4 Arbitrary Level Flight 30312.1.1.5 Arbitrary 3D Flight 30412.1.2 Rotary-Wing Energy Model 30412.2 Energy Efficiency Maximization 30612.3 Energy Minimization with Communication Requirement 31012.4 UAV-Ground Energy Trade-off 31212.5 Chapter Summary 312References 31313 Fundamental Trade-Offs for UAV Communications 315Qingqing Wu, Liang Liu, Yong Zeng, and Rui Zhang13.1 Introduction 31513.2 Fundamental Trade-offs 31713.2.1 Throughput-Delay Trade-Off 31713.2.2 Throughput-Energy Trade-Off 31813.2.3 Delay-Energy Trade-Off 31913.3 Throughput-Delay Trade-Off 31913.3.1 Single-UAV-Enabled Wireless Network 31913.3.2 Multi-UAV-Enabled Wireless Network 32113.4 Throughput-Energy Trade-Off 32313.4.1 UAV Propulsion Energy Consumption Model 32313.4.2 Energy-Constrained Trajectory Optimization 32413.5 Further Discussions and Future Work 32513.6 Chapter Summary 327References 32714 UAV-Cellular Spectrum Sharing 329Chiya Zhang and Wei Zhang14.1 Introduction 32914.1.1 Cognitive Radio 32914.1.1.1 Overlay Spectrum Sharing 32914.1.1.2 Underlay Spectrum Sharing 33014.1.2 Drone Communication 33014.1.2.1 UAV Spectrum Sharing 33114.1.2.2 UAV Spectrum Sharing with Exclusive Regions 33214.1.3 Chapter Overview 33314.2 SNR Meta-Distribution of Drone Networks 33314.2.1 Stochastic Geometry Analysis 33314.2.2 Characteristic Function of the SNR Meta-Distribution 33414.2.3 LOS Probability 33814.3 Spectrum Sharing of Drone Networks 33814.3.1 Spectrum Sharing in Single-Tier DSCs 33914.3.2 Spectrum Sharing with Cellular Network 34214.4 Summary 345References 346Part IV Other Advanced Technologies for UAV Communications 34915 Non-Orthogonal Multiple Access for UAV Communications 351Tianwei Hou, Yuanwei Liu, and Xin Sun15.1 Introduction 35115.1.1 Motivation 35215.2 User-Centric Strategy for Emergency Communications 35215.2.1 System Model 35415.2.1.1 Far user case 35415.2.1.2 Near user case 35515.2.2 Coverage Probability of the User-Centric Strategy 35615.3 UAV-Centric Strategy for Offloading Actions 35915.3.1 SINR Analysis 36015.3.2 Coverage Probability of the UAV-Centric Strategy 36115.4 Numerical Results 36415.4.1 User-Centric Strategy 36515.4.2 UAV-Centric Strategy 36715.5 Conclusions 369References 36916 Physical Layer Security for UAV Communications 373Nadisanka Rupasinghe, Yavuz Yapici, Ismail Guvenc, Huaiyu Dai, and Arupjyoti Bhuyan16.1 Introduction 37316.2 Breaching Security in Wireless Networks 37416.2.1 Denial-of-Service Attacks 37416.2.2 Masquerade Attacks 37416.2.3 Message Modification Attacks 37416.2.4 Eavesdropping Intruders 37516.2.5 Traffic Analysis 37516.3 Wireless Network Security Requirements 37516.3.1 Authenticity 37516.3.2 Confidentiality 37616.3.3 Integrity 37616.3.4 Availability 37616.4 Physical Layer Security 37616.4.1 Physical Layer versus Upper Layers 37716.4.2 Physical Layer Security Techniques 37716.4.2.1 Artificial Noise 37816.4.2.2 Cooperative Jamming 37816.4.2.3 Protected Zone 37816.5 Physical Layer Security for UAVs 37916.5.1 UAV Trajectory Design to Enhance PLS 37916.5.2 Cooperative Jamming to Enhance PLS 38116.5.3 Spectral- and Energy-Efficient PLS Techniques 38216.6 A Case Study: Secure UAV Transmission 38316.6.1 System Model 38316.6.1.1 Location Distribution and mmWave Channel Model 38516.6.2 Protected Zone Approach for Enhancing PLS 38516.6.3 Secure NOMA for UAV BS Downlink 38616.6.3.1 Secrecy Outage and Sum Secrecy Rates 38616.6.3.2 Shape Optimization for Protected Zone 38816.6.3.3 Numerical Results 38916.6.3.4 Location of the Most Detrimental Eavesdropper 38916.6.3.5 Impact of the Protected Zone Shape on Secrecy Rates 39016.6.3.6 Variation of Secrecy Rates with Altitude 391Summary 392References 39317 UAV-Enabled Wireless Power Transfer 399Jie Xu, Yong Zeng, and Rui Zhang17.1 Introduction 39917.2 System Model 40117.3 Sum-Energy Maximization 40217.4 Min-Energy Maximization under Infinite Charging Duration 40317.4.1 Multi-Location-Hovering Solution 40417.5 Min-Energy Maximization Under Finite Charging Duration 40717.5.1 Successive Hover-and-Fly Trajectory Design 40717.5.1.1 Flying Distance Minimization to Visit Gamma Hovering Locations 40717.5.1.2 Hovering Time Allocation When T >= Tfly 40817.5.1.3 Trajectory Refinement When T fly 40917.5.2 SCA-Based Trajectory Design 40917.6 Numerical Results 41117.7 Conclusion and Future Research Directions 413References 41518 Ad-Hoc Networks in the Sky 417Kamesh Namuduri18.1 Communication Support for UAVs 41718.1.1 Satellite Connectivity 41818.1.2 Cellular Connectivity 42018.1.3 Aerial Connectivity 42018.2 The Mobility Challenge 42118.2.1 UAS-to-UAS Communication 42118.2.2 Mobility Models 42218.3 Establishing an Ad-Hoc Network 42318.3.1 Network Addressing 42418.3.2 Routing 42518.4 Standards 42618.4.1 ASTM: Remote ID for UAS 42618.4.2 EUROCAE: Safe, Secure, and Efficient UAS Operations 42618.4.3 3GPP: 4G LTE and 5G Support for Connected UAS Operations 42618.4.4 IEEE P1920.1: Aerial Communications and Networking Standards 42718.4.5 IEEE P1920.2: Vehicle-to-Vehicle Communications Standard for UAS 42718.5 Technologies and Products 42718.5.1 Silvus Streamcaster 42718.5.2 goTenna 42718.5.3 MPU5 and Wave Relay from Persistent Systems 42818.5.4 Kinetic Mesh Networks from Rajant 42818.6 Software-Defined Network as a Solution for UAV Networks 42818.7 Summary 429References 429Index 433

O autorze

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.