Skip to main content

UAV Communications and Networks

  • Chapter
  • First Online:
Fundamentals of 6G Communications and Networking

Part of the book series: Signals and Communication Technology ((SCT))

  • 764 Accesses

Abstract

Witnessing the recent developments of UAV-assisted networks, such as Loon by Loon LLC, Aquila by Facebook, and HAWK30 by HAPSMobile, we are at the cusp of a communication revolution where non-terrestrial networks (NTN) are envisaged to meet the terrestrial networks through the sky. Wireless connectivity has already been extending towards the sky by integrating unmanned aerial vehicles (UAVs). UAV-assisted communication can efficiently secure and utilize the favorable channel condition, thanks to its flexible maneuverability. The application of such mobile platform is not limited to supporting existing network infrastructure (i.e., data offloading), disaster management, data collection for IoT, and so on. However, to fully utilize such a flexible but energy-limited UAV platform, there are many considerations and challenging issues, which will be elucidated throughout this chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Loon LLC. (2020) Project Loon. [Online]. Available: https://loon.co/

  2. J. Marriott, B. Tezel, Z. Liu, and N. Stier. (2018) Trajectory optimization of solar-powered high-altitude long endurance aircraft. [Online]. Available: https://research.fb.com/publications/trajectory-optimization-of-solar-powered-high-altitude-long-endurance-aircraft/

  3. HAPSMobile. (2020) HAWK30. [Online]. Available: https://www.hapsmobile.com/

  4. S. Jung, W. J. Yun, M. Shin, J. Kim, and J.-H. Kim, “Orchestrated scheduling and multi-agent deep reinforcement learning for cloud-assisted multi-UAV charging systems,” IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 5362–5377, 2021.

    Article  Google Scholar 

  5. Z. Zhang, Y. Xiao, Z. Ma, M. Xiao, Z. Ding, X. Lei, G. K. Karagiannidis, and P. Fan, “6G wireless networks: Vision, requirements, architecture, and key technologies,” IEEE Vehicular Technology Magazine, vol. 14, no. 3, pp. 28–41, 2019.

    Article  Google Scholar 

  6. S. Zhang, H. Zhang, and L. Song, “Beyond D2D: Full dimension UAV-to-everything communications in 6G,” IEEE Transactions on Vehicular Technology, vol. 69, no. 6, pp. 6592–6602, 2020.

    Article  MathSciNet  Google Scholar 

  7. Z. M. Fadlullah, D. Takaishi, H. Nishiyama, N. Kato, and R. Miura, “A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV-aided networks,” IEEE Network, vol. 30, no. 1, pp. 100–105, 2016.

    Article  Google Scholar 

  8. X. Sun, D. W. K. Ng, Z. Ding, Y. Xu, and Z. Zhong, “Physical layer security in UAV systems: Challenges and opportunities,” IEEE Wireless Communications, vol. 26, no. 5, pp. 40–47, 2019.

    Article  Google Scholar 

  9. E. K. Markakis, K. Karras, A. Sideris, G. Alexiou, and E. Pallis, “Computing, caching, and communication at the edge: The cornerstone for building a versatile 5G ecosystem,” IEEE Communications Magazine, vol. 55, no. 11, pp. 152–157, 2017.

    Article  Google Scholar 

  10. Z. Zhou, C. Zhang, C. Xu, F. Xiong, Y. Zhang, and T. Umer, “Energy-efficient industrial internet of UAVs for power line inspection in smart grid,” IEEE Transactions on Industrial Informatics, vol. 14, no. 6, pp. 2705–2714, 2018.

    Article  Google Scholar 

  11. B. Li, Z. Fei, and Y. Zhang, “UAV communications for 5G and beyond: Recent advances and future trends,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2241–2263, 2018.

    Article  Google Scholar 

  12. I. Bor-Yaliniz, A. El-Keyi, and H. Yanikomeroglu, “Spatial configuration of agile wireless networks with drone-BSs and user-in-the-loop,” IEEE Transactions on Wireless Communications, vol. 18, no. 2, pp. 753–768, 2018.

    Article  Google Scholar 

  13. Y. Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Communications Magazine, vol. 54, no. 5, pp. 36–42, 2016.

    Article  Google Scholar 

  14. M. Shin, J. Kim, and M. Levorato, “Auction-based charging scheduling with deep learning framework for multi-drone networks,” IEEE Trans. Vehi. Technol., vol. 68, no. 5, pp. 4235–4248, 2019.

    Article  Google Scholar 

  15. S. Park, L. Zhang, and S. Chakraborty, “Battery assignment and scheduling for drone delivery businesses,” in 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED). IEEE, 2017, pp. 1–6.

    Google Scholar 

  16. A. Couture-Beil and R. T. Vaughan, “Adaptive mobile charging stations for multi-robot systems,” in 2009 IEEE/RSJ international conference on intelligent robots and systems. IEEE, 2009, pp. 1363–1368.

    Google Scholar 

  17. M. P. Wellman, W. E. Walsh, P. R. Wurman, and J. K. MacKie-Mason, “Auction protocols for decentralized scheduling,” Games and economic behavior, vol. 35, no. 1-2, pp. 271–303, 2001.

    Article  MathSciNet  MATH  Google Scholar 

  18. D. C. Parkes and L. H. Ungar, “An auction-based method for decentralized train scheduling,” in Proceedings of the fifth international conference on Autonomous agents, 2001, pp. 43–50.

    Google Scholar 

  19. T. Shu and M. Krunz, “Coverage-time optimization for clustered wireless sensor networks: a power-balancing approach,” IEEE/ACM Transactions On Networking, vol. 18, no. 1, pp. 202–215, 2009.

    Google Scholar 

  20. J. Kim, J. Choi, and W. Lee, “Energy-aware distributed topology control for coverage-time optimization in clustering-based heterogeneous sensor networks,” in 2006 IEEE 63rd Vehicular Technology Conference, vol. 3. IEEE, 2006, pp. 1033–1037.

    Google Scholar 

  21. W. Feng, N. Zhao, S. Ao, J. Tang, X. Zhang, Y. Fu, D. K. So, and K.-K. Wong, “Joint 3D trajectory design and time allocation for UAV-enabled wireless power transfer networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 9, pp. 9265–9278, 2020.

    Article  Google Scholar 

  22. S. Jung, J. Kim, and J.-H. Kim, “Joint message-passing and convex optimization framework for energy-efficient surveillance UAV scheduling,” Electronics, vol. 9, no. 9, p. 1475, 2020.

    Google Scholar 

  23. S. Park, W.-Y. Shin, M. Choi, and J. Kim, “Joint mobile charging and coverage-time extension for unmanned aerial vehicles,” IEEE Access, vol. 9, pp. 94 053–94 063, 2021.

    Google Scholar 

  24. J.-H. Lee, K.-H. Park, M.-S. Alouini, and Y.-C. Ko, “Trajectory optimization of energy efficient FSOC-UAV with atmospheric and geometric loss,” in 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2018, pp. 35–37.

    Google Scholar 

  25. Y. Zeng and R. Zhang, “Energy-efficient UAV communication with trajectory optimization,” IEEE Transactions on Wireless Communications, vol. 16, no. 6, pp. 3747–3760, 2017.

    Article  Google Scholar 

  26. H. Ghazzai, M. B. Ghorbel, A. Kadri, and M. J. Hossain, “Energy efficient 3D positioning of micro unmanned aerial vehicles for underlay cognitive radio systems,” in 2017 IEEE International Conference on Communications (ICC). IEEE, 2017, pp. 1–6.

    Google Scholar 

  27. K. Goodrich and M. Moore, “On-demand mobility (ODM) technical pathway: enabling ease of use and safety,” Tech. Rep., 2015.

    Google Scholar 

  28. D. Kwon, J. Jeon, S. Park, J. Kim, and S. Cho, “Multiagent DDPG-based deep learning for smart ocean federated learning IoT networks,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9895–9903, 2020.

    Article  Google Scholar 

  29. W. J. Yun, S. Jung, J. Kim, and J.-H. Kim, “Distributed deep reinforcement learning for autonomous aerial evtol mobility in drone taxi applications,” ICT Express, vol. 7, no. 1, pp. 1–4, 2021.

    Article  Google Scholar 

  30. P. Zhan, K. Yu, and A. L. Swindlehurst, “Wireless relay communications with unmanned aerial vehicles: Performance and optimization,” IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no. 3, pp. 2068–2085, 2011.

    Article  Google Scholar 

  31. J.-H. Lee, K.-H. Park, M.-S. Alouini, and Y.-C. Ko, “Free space optical communication on UAV-assisted backhaul networks: Optimization for service time,” in 2019 IEEE Globecom Workshops (GC Wkshps). IEEE, 2019, pp. 1–6.

    Google Scholar 

  32. M. Alzenad, M. Z. Shakir, H. Yanikomeroglu, and M.-S. Alouini, “FSO-based vertical backhaul/fronthaul framework for 5G+ wireless networks,” IEEE Communications Magazine, vol. 56, no. 1, pp. 218–224, 2018.

    Article  Google Scholar 

  33. J.-H. Lee, K.-H. Park, Y.-C. Ko, and M.-S. Alouini, “Throughput maximization of mixed FSO/RF UAV-aided mobile relaying with a buffer,” IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 683–694, 2020.

    Article  Google Scholar 

  34. M. Mozaffari, A. Taleb Zadeh Kasgari, W. Saad, M. Bennis, and M. Debbah, “Beyond 5G with UAVs: Foundations of a 3D wireless cellular network,” IEEE Transa. on Wireless Commun., vol. 18, no. 1, pp. 357–372, 2019.

    Article  Google Scholar 

  35. Q. Wu, Y. Zeng, and R. Zhang, “Joint trajectory and communication design for multi-UAV enabled wireless networks,” IEEE Trans. Wireless Commun., vol. 17, no. 3, pp. 2109–2121, Mar. 2018.

    Article  Google Scholar 

  36. Z. Kang, C. You, and R. Zhang, “3D placement for multi-UAV relaying: An iterative Gibbs-sampling and block coordinate descent optimization approach,” arXiv:2006.09658 [cs.IT], Jun. 2020.

    Google Scholar 

  37. J. H. Lee, K. H. Park, Y. C. Ko, and M. S. Alouini, “A UAV-mounted free space optical communication: Trajectory optimization for flight time,” IEEE Trans. Wireless Commun., vol. 19, no. 3, pp. 1610–1621, Mar. 2020.

    Article  Google Scholar 

  38. ——, “Throughput maximization of mixed FSO/RF UAV-aided mobile relaying with a buffer,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 683–694, 2021.

    Article  Google Scholar 

  39. J.-H. Lee, K.-H. Park, M.-S. Alouini, and Y.-C. Ko, “Spectral-efficient network design for high-altitude platform station networks with mixed RF/FSO systems,” IEEE Trans. Wireless Commun., pp. 1–15, 2022.

    Google Scholar 

  40. E. Kalantari, M. Z. Shakir, H. Yanikomeroglu, and A. Yongacoglu, “Backhaul-aware robust 3D drone placement in 5G+ wireless networks,” in Proc. IEEE International Conf. on Commun. (ICC), 2017, pp. 109–114.

    Google Scholar 

  41. C. T. Cicek, H. Gultekin, B. Tavli, and H. Yanikomeroglu, “Backhaul-aware optimization of UAV base station location and bandwidth allocation for profit maximization,” IEEE Access, vol. 8, pp. 154 573–154 588, 2020.

    Google Scholar 

  42. W. Khawaja, I. Guvenc, D. W. Matolak, U.-C. Fiebig, and N. Schneckenburger, “A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2361–2391, 2019.

    Article  Google Scholar 

  43. J. Gomez-Ponce, T. Choi, N. A. Abbasi, A. Adame, A. Alvarado, C. Bullard, R. Shen, F. Daneshgaran, H. S. Dhillon, and A. F. Molisch, “Air-to-Ground directional channel sounder with drone and 64-antenna dual-polarized cylindrical array,” in Proc. IEEE International Conf. on Commun. Wrk. (ICC Workshops), 2021, pp. 1–6.

    Google Scholar 

  44. T. Choi, J. Gomez-Ponce, C. Bullard, I. Kanno, M. Ito, T. Ohseki, K. Yamazaki, and A. F. Molisch, “Using a drone sounder to measure channels for cell-free massive MIMO systems,” in IEEE Wireless Commun. and Networking Conf. (WCNC), 2022, pp. 2506–2511.

    Google Scholar 

  45. C. Yan, L. Fu, J. Zhang, and J. Wang, “A comprehensive survey on UAV communication channel modeling,” IEEE Access, vol. 7, pp. 107 769–107 792, 2019.

    Google Scholar 

  46. R. Wang, C. U. Bas, O. Renaudin, S. Sangodoyin, U. T. Virk, and A. F. Molisch, “A real-time MIMO channel sounder for vehicle-to-vehicle propagation channel at 5.9 GHz,” in Proc. IEEE International Conf. on Commun. (ICC), 2017, pp. 1–6.

    Google Scholar 

  47. J.-H. Lee, K.-H. Park, M.-S. Alouini, and Y.-C. Ko, “Optimal resource allocation and placement for terrestrial and aerial base stations in mixed RF/FSO backhaul networks,” in Proc. IEEE Vehicular Technology Conf. (VTC), Antwerp, Belgium, May 2020.

    Google Scholar 

  48. M. Mozaffari, W. Saad, M. Bennis, Y. Nam, and M. Debbah, “A tutorial on UAVs for wireless networks: Applications, challenges, and open problems,” IEEE Communi. Sur. Tut., vol. 21, no. 3, pp. 2334–2360, 2019.

    Article  Google Scholar 

  49. S. Sukhbaatar, R. Fergus et al., “Learning multiagent communication with backpropagation,” in Proc. NIPS, 2016.

    Google Scholar 

  50. W. J. Yun, S. Park, J. Kim, M. Shin, S. Jung, D. A. Mohaisen, and J.-H. Kim, “Cooperative multiagent deep reinforcement learning for reliable surveillance via autonomous multi-UAV control,” IEEE Transactions on Industrial Informatics, vol. 18, no. 10, pp. 7086–7096, 2022.

    Article  Google Scholar 

  51. Samsung Electronics, “Samsung brings advanced ultra-fine pixel technologies to new mobile image sensors,” September 2021.

    Google Scholar 

  52. Space X. (2020) Starlink. [Online]. Available: https://www.starlink.com/

  53. Federal Communication Commission (FCC). (2018, Mar.) FCC authorizes SpaceX to provide broadband satellite services. [Online]. Available: https://www.fcc.gov/document/fcc-authorizes-spacex-provide-broadband-satellite-services

  54. J.-H. Lee, J. Park, M. Bennis, and Y.-C. Ko, “Integrating LEO satellites and Multi-UAV reinforcement learning for hybrid FSO/RF Non-Terrestrial Networks,” arXiv:2010.10138 [cs.NI], 2020.

    Google Scholar 

  55. ——, “Integrating LEO satellite and UAV relaying via reinforcement learning for non-terrestrial networks,” in Proc. IEEE Global Commun. Conf. (Globecom), Taipei, Taiwan, 2019, pp. 1–6.

    Google Scholar 

  56. 3GPP TR38.821, “Study on new radio (NR) to support non-terrestrial networks,” Jun. 2021.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joongheon Kim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Park, S., Lee, JH., Jung, S., Kim, J. (2024). UAV Communications and Networks. In: Lin, X., Zhang, J., Liu, Y., Kim, J. (eds) Fundamentals of 6G Communications and Networking. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-37920-8_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37920-8_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37919-2

  • Online ISBN: 978-3-031-37920-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics