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

被引:32
作者
Hu, Zhenzhen [1 ,2 ]
Zeng, Fanzi [1 ]
Xiao, Zhu [1 ]
Fu, Bin [1 ]
Jiang, Hongbo [1 ]
Xiong, Hailiang [3 ]
Zhu, Yongdong [4 ]
Alazab, Mamoun [5 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
[2] Univ South China, Coll Comp Sci, Hengyang 421001, Peoples R China
[3] Shandong Univ, Sch Informat Sci & Engn, Qingdao 266237, Peoples R China
[4] Zhejiang Lab, Hangzhou 311121, Peoples R China
[5] Charles Darwin Univ, Coll Engn IT & Environm, Darwin, NT 0810, Australia
基金
中国国家自然科学基金;
关键词
Space-air-ground; energy-efficiency; 3D trajectory optimization; resources allocation; ENERGY-EFFICIENCY; INTERNET; MAXIMIZATION;
D O I
10.1109/TVT.2023.3280482
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 simultaneouslymaximizing 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.
引用
收藏
页码:14214 / 14229
页数:16
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