• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 59 Issue 4
Jul.  2024
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Article Contents
ZHANG Xianyu, CHEN Yong, ZHANG Yu, YANG Hua. Joint Optimization of Resource Allocation and Deployment Location in Unmanned Aerial Vehicle-Assisted Communication[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 917-924. doi: 10.3969/j.issn.0258-2724.20230400
Citation: ZHANG Xianyu, CHEN Yong, ZHANG Yu, YANG Hua. Joint Optimization of Resource Allocation and Deployment Location in Unmanned Aerial Vehicle-Assisted Communication[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 917-924. doi: 10.3969/j.issn.0258-2724.20230400

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

doi: 10.3969/j.issn.0258-2724.20230400
  • Received Date: 15 Aug 2023
  • Rev Recd Date: 04 Nov 2023
  • Available Online: 29 May 2024
  • Publish Date: 28 Nov 2023
  • 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.

     

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