Volume 31 Issue 3
May  2022
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ZHANG Xiao, WANG Xuehe, XU Xinping, ZHAO Yingchao. Demand Learning and Cooperative Deployment of UAV Networks[J]. Chinese Journal of Electronics, 2022, 31(3): 408-415. doi: 10.1049/cje.2021.00.278
Citation: ZHANG Xiao, WANG Xuehe, XU Xinping, ZHAO Yingchao. Demand Learning and Cooperative Deployment of UAV Networks[J]. Chinese Journal of Electronics, 2022, 31(3): 408-415. doi: 10.1049/cje.2021.00.278

Demand Learning and Cooperative Deployment of UAV Networks

doi: 10.1049/cje.2021.00.278
Funds:  This work was supported in part by the National Natural Science Foundation of China (61902437), the Hubei Provincial Natural Science Foundation of China (2020CFB629), the Research Start-up Funds of South-Central University for Nationalities (YZZ18006), and the Institutional Strategic Grant of Caritas Institute of Higher Education, HK, China (ISG210101)
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  • Author Bio:

    received the Ph.D. degree from Department of Computer Science in City University of Hong Kong, Hong Kong, China, 2016. He was a Postdoctoral Research Fellow at Singapore University of Technology and Design from 2016 to 2019. Currently, he is an Associate Professor with College of Computer Science, South-Central University for Nationalities, and the School of Computing and Information Sciences, Caritas Institute of Higher Education, Hong Kong, China. His research interests include algorithms design and analysis, combinatorial optimization, wireless and UAV networking. (Email: xiao.zhang@my.cityu.edu.hk)

    (corresponding author) received the Ph.D. degree in electrical and electronic engineering from Nanyang Technological University, Singapore, in 2016. She was a Postdoctoral Research Fellow with the Pillar of Engineering Systems and Design, Singapore University of Technology and Design. She is an Assistant Professor of Infocomm Technology Cluster with Singapore Institute of Technology. Her research interests cover transportation, control theory, network economics and game theory. (Email: wangxuehe@mail.sysu.edu.cn)

    received the B.S. degree from the Department of Mathematics, Nanjing University, China, in 2015. He received the Ph.D. degree from Singapore University of Technology and Design, Singapore. His research interests include algorithmic game theory, mechanism design, and distributed network control. (Email: xinping.xu@ntu.edu.sg)

    received the B.E. and Ph.D. degrees from the Department of Computer Science and Technology, Tsinghua University, Beijing, China, in 2004 and 2009, respectively. She is currently an Associate Professor with the School of Computing and Information Sciences, Caritas Institute of Higher Education, Hong Kong, China. Her current research interests include algorithmic game theory, algorithm designs, and computational complexity analysis and scheduling. (Email: yczhao@cihe.edu.hk)

  • Received Date: 2021-08-06
  • Accepted Date: 2022-02-08
  • Available Online: 2022-03-07
  • Publish Date: 2022-05-05
  • Unmanned aerial vehicle (UAV) as a powerful tool has found its applicability in assisting mobile users to deal with computation-intensive and delay-sensitive applications (e.g., edge computing, high-speed Internet access, and local caching). However, deployment of UAV-aided mobile services (UMS) faces challenges due to the UAV limitation in wireless coverage and energy storage. Aware of such physical limitations, a future UMS system should be intelligent enough to self-plan trajectories and best offer computational capabilities to mobile users. There are important issues regarding the UAV-user interaction, UAV-UAV cooperation for sustainable service provision, and dynamic UMS pricing. These networking and resource management issues are largely overlooked in the literature and this article presents intelligent solutions for cooperative UMS deployment and operation. Mobile users’ locations are generally private information and changing over time. How to learn on-demand users’ truthful location reporting is important for determining optimal UAV deployment in serving all the users fairly. After addressing the truthful UAV-user interaction issue via game theory, we further study the UAV network sustainability for UMS provision by minimizing the energy consumption cost during deployment and seeking UAV-UAV cooperation. Finally, for profit-maximizing purpose, we analyze the cooperative UAVs’ deployment, capacity allocation, and dynamic service pricing.
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  • [1]
    P. Yang, X. Cao, X. Xi, et al., “Three-dimensional drone-cell deployment for congestion mitigation in cellular networks,” IEEE Trans. on Vehicular Technology, vol.67, no.10, pp.9867–9881, 2018. doi: 10.1109/TVT.2018.2857211
    X. Li, H. Yao, J. Wang, S. Wu, et al., “Rechargeable multi-UAV aided seamless coverage for QoS-guaranteed IoT networks,” IEEE Internet of Things Journal, vol.6, no.6, pp.10902–10914, 2019. doi: 10.1109/JIOT.2019.2943147
    X. Li, H. Yao, J. Wang, et al., “A near-optimal UAV-aided radio coverage strategy for dense urban areas,” IEEE Trans. Veh. Technol., vol.68, no.9, pp.9098–9109, 2019. doi: 10.1109/TVT.2019.2927425
    X. Xi, X. Cao, P. Yang, et al., “Network resource allocation for eMBB payload and URLLC control information communication multiplexing in a multi-UAV relay network,” IEEE Trans. Commun., vol.69, no.3, pp.1802–1817, 2021. doi: 10.1109/TCOMM.2020.3042970
    X. Jiang, M. Sheng, N. Zhao, et al., “Green UAV communications for 6G: A survey,” Chinese Journal of Aeronautics, in press, DOI: 10.1016/j.cja.2021.04.025, 2021
    H. Wu, Z. Wei, Y. Hou, et al., “Cell-edge user offloading via flying UAV in non-uniform heterogeneous cellular networks,” IEEE Trans. Wireless Commun., vol.19, no.4, pp.2411–2426, 2020. doi: 10.1109/TWC.2020.2964656
    X. Diao, J. Zheng, Y. Cai, et al., “Fair data allocation and trajectory optimization for UAV-assisted mobile edge computing,” IEEE Communications Letters, vol.23, no.12, pp.2357–2361, 2019. doi: 10.1109/LCOMM.2019.2943461
    J. Wang, C. Jiang, Z. Han, et al., “Taking drones to the next level: Cooperative distributed unmanned-aerial-vehicular networks for small and mini drones,” IEEE Vehicular Technology Magazine, vol.12, no.3, pp.73–82, 2017. doi: 10.1109/MVT.2016.2645481
    W. Ding, Z. Yang, M. Chen, et al., “Resource allocation for UAV assisted wireless networks with QoS constraints,” in Proc. of IEEE Wireless Communications and Networking Conference (WCNC), Virtual Conference, pp.1−7, 2020.
    W. Shi, Y. Sun, M. Liu, et al., “Joint UL/DL resource allocation for UAV-aided full-duplex NOMA communications,” IEEE Trans. Commun., vol.69, no.12, pp.8474–8487, 2021. doi: 10.1109/TCOMM.2021.3110298
    Y. Gu, A. Lo, and I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks,” IEEE Commun. Surveys Tuts., vol.11, no.1, pp.13–32, 2009.
    V. Hassija, V. Saxena, and V. Chamola, “Scheduling drone charging for multi-drone network based on consensus time-stamp and game theory,” Computer Communications, vol.149, pp.51–61, 2020. doi: 10.1016/j.comcom.2019.09.021
    X. Zhang and L. Duan, “Energy-saving deployment algorithms of UAV swarm for sustainable wireless coverage,” IEEE Trans. Veh. Technol., vol.69, no.9, pp.10320–10335, 2020. doi: 10.1109/TVT.2020.3004855
    X. Xu, L. Duan, and M. Li, “Strategic learning approach for deploying UAV-provided wireless services,” IEEE Trans. Mobile Comput., vol.20, no.3, pp.1230–1241, 2021. doi: 10.1109/TMC.2019.2953726
    M. Mozaffari, W. Saad, M. Bennis, et al., “Unmanned aerial vehicle with underlaid device-to-device communications: Performance and tradeoffs,” IEEE Trans. Wireless Commun., vol.15, no.6, pp.3949–3963, 2016. doi: 10.1109/TWC.2016.2531652
    S. Weber, J. G. Andrews, and N. Jindal, “The effect of fading, channel inversion, and threshold scheduling on ad hoc networks,” IEEE Trans. Inform. Theory, vol.53, no.11, pp.4127–4149, 2007. doi: 10.1109/TIT.2007.907482
    A. D. Procaccia and M. Tennenholtz, “Approximate mechanism design without money,” ACM Trans. Econ. and Comput., vol.1, no.4, article no.18, 2013.
    L. Duan, J. Huang, and J. Walrand, “Economic analysis of 4G upgrade timing,” IEEE Trans. Mobile Comput., vol.14, no.5, pp.975–989, 2015. doi: 10.1109/TMC.2014.2338299
    Y. Zeng and R. Zhang, “Energy-efficient UAV communication with trajectory optimization,” IEEE Trans. Wireless Commun., vol.16, no.6, pp.3747–3760, 2017. doi: 10.1109/TWC.2017.2688328
    M. A. Figliozzi, “Lifecycle modeling and assessment of unmanned aerial vehicles (Drones) CO2e emissions,” Transportation Research Part D: Transport and Environment, vol.57, pp.251–261, 2017.
    X. Wang and L. Duan, “Economic analysis of unmanned aerial vehicle (UAV) provided mobile services,” IEEE Trans. Mobile Comput., vol.20, no.5, pp.1804–1816, 2021. doi: 10.1109/TMC.2020.2973088
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