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Jingming XIA, Yufeng LIU, Ling TAN, “Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing,” Chinese Journal of Electronics, vol. 33, no. 2, article no. , 2024 doi: 10.23919/cje.2022.00.159
Citation: Jingming XIA, Yufeng LIU, Ling TAN, “Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing,” Chinese Journal of Electronics, vol. 33, no. 2, article no. , 2024 doi: 10.23919/cje.2022.00.159

Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing

doi: 10.23919/cje.2022.00.159
Funds:  This work was supported by the National Natural Science Foundation of China (61871032)
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  • Author Bio:

    Jingming XIA was born in Jiangsu Province in 1980. He received the B.S. and M.S. degrees in Information Engineering and the Ph.D. degree in Atmospheric Science from the Nanjing University of Information Science and Technology, Nanjing, China, in 2005 and 2012, respectively. Since 2013, he has been an Assistant Professor with the Artificial Intelligence Department, Nanjing University of Information Science and Technology. He is the author of two books and more than 30 articles. His research interests include the application of machine learning and deep learning in meteorology. (Email: xiajingming@nuist.edu.cn)

    Yufeng LIU was born in Hebei Province in 1996. She received the B.S. degree in Information Engineering from Hebei GEO University, Shijiazhuang, China, in 2020. Since September 2020, she has been working towards her M.S. degree in the School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China. Her current research interests include unmannedaerial-vehicle communications, mobile-edge computing, and machine learning. (Email: yufengliu@nuist.edu.cn)

    Ling TAN (corresponding author) was born in Jiangsu Province in 1979. She received the B.S. and M.S. degrees in Information Engineering from Nanjing Normal University, in 2005, and the Ph.D. degree in Information and Network from the Nanjing University of Posts and Telecommunications, Nanjing, in 2012. Since 2020, she has been an Professor with the School of Computer and Software, Nanjing University of Information Science and Technology. She is the author of three books and more than 20 articles. Her research interests include the application of machine learning and deep learning in image processing. (Email: cillatan0@nuist.edu.cn)

  • Received Date: 2022-06-02
  • Accepted Date: 2022-12-09
  • Available Online: 2023-01-16
  • Since the computing capacity and battery energy of unmanned aerial vehicle (UAV) are constrained, UAV as aerial user is hard to handle the high computational complexity and time-sensitive applications. This paper investigates a cellular-connected multi-UAV network supported by mobile edge computing (MEC). Multiple UAVs carrying tasks fly from a given initial position to a termination position within a specified time. To handle the large number of tasks carried by UAVs, we propose a energy cost of all UAVs based problem to determine how many tasks should be offloaded to high-altitude balloons (HABs) for computing, which UAV-HAB association, the trajectory of UAV, and calculation task splitting are jointly optimized. However, the formulated problem has nonconvex structure. Hence, an efficient iterative algorithm by applying successive convex approximation (SCA) and the block coordinate descent (BCD) methods is put forward. Specifically, in each iteration, the UAV-HAB association, calculation task splitting, and UAV trajectory are alternately optimized. Especially, for the nonconvex UAV trajectory optimization problem, an approximate convex optimization problem is settled. The numerical results indicate that the scheme of this paper proposed is guaranteed to converge and also significantly reduces the entire power consumption of all UAVs compared to the benchmark schemes.
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