Joint Optimization of Resource Allocation and Deployment Location in Unmanned Aerial Vehicle-Assisted Communication

被引:0
|
作者
Zhang, Xianyu [1 ,2 ]
Chen, Yong [1 ,3 ]
Zhang, Yu [1 ]
Yang, Hua [4 ]
机构
[1] The 63rd Research Institute, National University of Defense Technology, Nanjing,210003, China
[2] Unit 75841 of PLA, Changsha,410000, China
[3] College of Communication Engineering, Army Engineering University of PLA, Nanjing,210003, China
[4] Institute of War, the Academy of Military Science, Beijing,100091, China
关键词
Antennas - Benchmarking - Convex optimization - Integer programming - Iterative methods - Linear programming - Nonlinear programming - Orthogonal frequency division multiplexing - Power control - Unmanned aerial vehicles (UAV);
D O I
10.3969/j.issn.0258-2724.20230400
中图分类号
学科分类号
摘要
To enhance the performance of the unmanned aerial vehicle (UAV)-assisted communication network based on the orthogonal frequency division multiple access (OFDMA) mode, the rational network allocation and optimal allocation of communication resources were studied. Firstly, in order to maximize the fairness of the network, a mixed-integer nonlinear maximum-minimum optimization problem was modeled by combining the communication resources including sub-channel allocation, modulation mode selection, and power allocation with UAV position. Then, the iterative optimization method was used to solve the problems of variable coupling and non-convex, and the maximum-minimum problem was converted into two sub-problems: joint optimization of sub-channel allocation and modulation mode selection and joint optimization of UAV position and sub-channel power. Finally, by means of appropriate transformations, the two subproblems were modeled into 0–1 linear optimization problem and convex optimization problem for solution. The experimental simulation results show that the proposed algorithm can jointly optimize multidimensional system parameters such as network allocation and communication resources, effectively enhance the fairness of network users, and improve network performance compared with other benchmark schemes. © 2024 Science Press. All rights reserved.
引用
收藏
页码:917 / 924
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