无人机通信中的资源分配及部署位置联合优化

张先玉; 陈勇; 张余; 杨华

doi:10.3969/j.issn.0258-2724.20230400

无人机通信中的资源分配及部署位置联合优化

doi: 10.3969/j.issn.0258-2724.20230400

张先玉^{1, 2,},
陈勇^{1, 3, ,},
张余¹,
杨华⁴

1.
国防科技大学第六十三研究所，江苏南京 210003
2.
解放军 75841部队，湖南长沙 410000
3.
陆军工程大学通信工程学院，江苏南京 210003
4.
军事科学院战争研究院，北京 100091

基金项目: 中国博士后科学基金（2021MD703980）

详细信息

作者简介:
张先玉（1986—），男，高级工程师，博士，研究方向为下一代移动通信技术、电磁频谱管理、通信抗干扰等，E-mail：zhangxy_sat@126.com

通讯作者:
陈勇（1975—），男，研究员，博士研究生，研究方向为电磁频谱管理、无线通信技术等，E-mail：chy63s@126.com

中图分类号: TN929.53
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- 被引次数: 0
出版历程
- 收稿日期: 2023-08-15
- 修回日期: 2023-11-04
- 网络出版日期: 2024-05-29
- 刊出日期: 2023-11-28

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

ZHANG Xianyu^{1, 2
,},
CHEN Yong^{1, 3
, ,},
ZHANG Yu¹,
YANG Hua⁴

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

摘要

摘要:
为提升基于正交频分多址接入模式无人机辅助无线通信系统的网络性能，首先，以提升用户的公平性为系统方案设计指标，将包括子信道分配、调制模式选择、功率分配等通信资源和无人机位置联合建模为一个混合整数非线性优化问题；进一步，利用迭代优化的方式解决变量耦合性及非凸性等问题，将最大-最小问题转换为两个子问题：子信道分配和调制方式选择联合优化、无人机位置和子信道功率联合优化；然后，通过适当变换将子信道分配和调制方式选择联合优化建模为0-1线性优化问题进行求解，而无人机位置和子信道功率联合优化建模为凸优化问题求解；最后，进行实验仿真验证. 研究结果表明，所提联合优化算法相比基本方案可有效提升网络用户的公平性.
- 无人机 /
- 正交频分多址 /
- 子信道分配 /
- 功率控制 /
- 调制模式 /
- 混合整数非线性规划
Abstract:
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.
- unmanned aerial vehicles (UAVs) /
- orthogonal frequency division multiple access /
- sub-channel allocation /
- power control /
- modulation mode /
- mixed-integer nonlinear programming

HTML全文

图 1 所考虑的无人机通信系统模型示意

Figure 1. System model of considered UAV-assisted communication

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图 2 网络节点部署位置示意

Figure 2. Deployment location of network nodes

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图 3 无人机各子信道的优化分配情况

Figure 3. Optimal sub-channel allocation of UAV

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图 4 调制方式选择

Figure 4. Modulation mode selection

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图 5 功率分配方案

Figure 5. Power allocation schemes

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图 6 用户获取速率

Figure 6. Achievable user rate

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图 7 不同误码率及发射功率下最小获取用户速率

Figure 7. Minimum achievable user rates with different BERs and transmitted powers

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图 8 不同无人机高度下的用户最小获取信息速率

Figure 8. Minimum achievable user rates under different UAV heights

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表 1 部分关键系统仿真参数

Table 1. Some key system simulation parameters

参数	数值
$M$ /个	20
$H$ /m	100
$N$ /个	100
$B$ /MHz	10
${\beta _0}$ /dB	−50
$\delta _0^2$ /(dBm·Hz⁻¹)	−169
$P_{\max }^{\mathrm{e}}$	${10^{ - 4}}$
${P_{\mathrm{T}}}$ /dBm	22

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