5G for Future Wireless Networks. Second EAI International Conference, 5GWN 2019, Changsha, China, February 23-24, 2019, Proceedings

Research Article

Joint Scheduling and Trajectory Design for UAV-Aided Wireless Power Transfer System

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  • @INPROCEEDINGS{10.1007/978-3-030-17513-9_1,
        author={Yi Wang and Meng Hua and Zhi Liu and Di Zhang and Haibo Dai and Ying Hu},
        title={Joint Scheduling and Trajectory Design for UAV-Aided Wireless Power Transfer System},
        proceedings={5G for Future Wireless Networks. Second EAI International Conference, 5GWN 2019, Changsha, China, February 23-24, 2019, Proceedings},
        proceedings_a={5GWN},
        year={2019},
        month={4},
        keywords={Unmanned aerial vehicle (UAV) Wireless power transfer (WPT) SNs scheduling Trajectory optimization},
        doi={10.1007/978-3-030-17513-9_1}
    }
    
  • Yi Wang
    Meng Hua
    Zhi Liu
    Di Zhang
    Haibo Dai
    Ying Hu
    Year: 2019
    Joint Scheduling and Trajectory Design for UAV-Aided Wireless Power Transfer System
    5GWN
    Springer
    DOI: 10.1007/978-3-030-17513-9_1
Yi Wang1,*, Meng Hua2,*, Zhi Liu3,*, Di Zhang4,*, Haibo Dai5,*, Ying Hu6,*
  • 1: Zhengzhou University of Aeronautics
  • 2: Southeast University
  • 3: Shizuoka University
  • 4: Zhengzhou University
  • 5: Nanjing University of Posts and Telecommunications
  • 6: Jiangsu University of Science and Technology
*Contact email: yiwang@zua.edu.cn, mhua@seu.edu.cn, liu@shizuoka.ac.jp, iedzhang@zzu.edu.cn, hbdai@njupt.edu.cn, zfhy116118@126.com

Abstract

In this paper, we focus on an unmanned aerial vehicle (UAV)-aided wireless power transfer (WPT) system, where an energy transmitter is deployed on UAV and sends wireless energy to multiple energy-limited sensor nodes (SNs) for energy supplement. How to exploit the UAV’s mobility via trajectory design and adopt suitable scheduling scheme of SNs will directly influence the whole charging efficiency over a given charging period. From the perspective of fairness among SNs, our aim is to maximize the minimum energy received by all SNs by jointly optimizing the UAV’s trajectory and SNs’ scheduling scheme with the UAV’s maximum speed constraint as well as the initial/final location constraint. However, the established problem is in a non-convex mixed integer form, which is difficult to tackle. Therefore, we first decompose the original problem into two subproblems and then develop an efficient iterative algorithm by using the successive convex optimization technique, which leads to a suboptimal solution. Numerical results are provided to demonstrate the superiority of our proposed algorithm over the benchmarks.