Joint Resources Allocation and 3D Trajectory Optimization for UAV-enabled Space-Air-Ground Integrated Networks

Zhenzhen Hu, Fanzi Zeng, Zhu Xiao, Bin Fu, Hongbo Jiang, Hailiang Xiong, Yongdong Zhu, Mamoun Alazab

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)


    In view of the complementary characteristics of the UAV communication network and satellite communication network, we consider UAVs, satellites, and ground devices to form a space-air-ground integrated network (SAGIN) that can provide service to ground users after a natural disaster. In this scenario, an unmanned aerial vehicle (UAV) is employed as a base station (BS) or mobile edge computing (MEC) server to provide communication/computation services to ground users. In addition, the UAV MEC server can offload computing tasks to a satellite. To achieve the goal of satisfying the users' quality-of-experience (QoE) requirements and simultaneously maximizing the energy efficiency, we investigate the problem of joint optimization of the UAV 3D trajectory with resource allocation. Thus, this article establishes a joint optimization problem. Given that the above optimization problem contains a fractional multivariate objective function and nonconvex constraints, we solve the optimization problem by using an effective iterative algorithm. The simulation results show that the proposed optimization scheme achieves high energy efficiency.

    Original languageEnglish
    Pages (from-to)14214-14229
    Number of pages16
    JournalIEEE Transactions on Vehicular Technology
    Issue number11
    Early online date2023
    Publication statusPublished - 1 Nov 2023

    Bibliographical note

    Funding Information:
    This work was supported by NSFC under Grants 62072167, 62232007, U20A20181, and 62272152, in part by the National Key Research and Development Program of China under Grant 2022YFE0137700, in part by the Science and Technology Innovation Program of Hunan Province under Grant 2021RC4023, in part by the Key R&D Program of Hunan Province under Grants 2021WK2001 and 2022GK2020, in part by the Hunan Natural Science Foundation of China under Grant 2022JJ30171, in part by the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy [Shenzhen (SZ)] under Grants GML-KF-22-22 and GML-KF-22-23, and in part by the Science and Technology Program in Qingdao under Grant 22-3-7-CSPZ-2-nsh.

    Publisher Copyright:
    © 1967-2012 IEEE.


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