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UAV Communications for 5G and Beyond


UAV Communications for 5G and Beyond


IEEE Press 1. Aufl.

von: Yong Zeng, Ismail Guvenc, Rui Zhang, Giovanni Geraci, David W. Matolak

121,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 07.12.2020
ISBN/EAN: 9781119575672
Sprache: englisch
Anzahl Seiten: 464

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Beschreibungen

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

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