无人机辅助空中计算的轨迹和功率联合优化方法

李松; 李佳琦; 王博文; 陈瑞瑞; 孙彦景; 张晓光

doi:10.11999/JEIT230917

无人机辅助空中计算的轨迹和功率联合优化方法

doi: 10.11999/JEIT230917

李松^{1, 2},
李佳琦¹,
王博文¹,
陈瑞瑞¹,
孙彦景^{1, 2},
张晓光^1, ,

1.
中国矿业大学信息与控制工程学院徐州 221116
2.
徐州市智能安全与应急协同工程研究中心徐州 221116

基金项目: 国家自然科学基金(62071472, 62101556)，中央高校基本科研业务费项目(2020ZDPYMS26)，江苏省自然科学基金(BK20200650, BK20210489)，江苏省未来网络科研基金(FNSRFP-2021-YB-12)

详细信息

作者简介:
李松：男，副教授，硕士生导师，研究方向为工业物联网、边缘计算等

李佳琦：男，硕士生，研究方向为空中计算

王博文：男，副教授，硕士生导师，研究方向为地下空间无人机应急网络、工业物联网、社交物联网

陈瑞瑞：男，讲师，硕士生导师，研究方向为涡旋电磁波通信、无人机通信、智能通信

孙彦景：男，教授，博士生导师，研究方向为无线通信与组网、工业物联网

张晓光：女，教授，硕士生导师，研究方向为应急通信、智能感知与精确定位、机器故障诊断

通讯作者:
张晓光　xiaoguangzh168@cumt.edu.cn

中图分类号: TN929
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- 被引次数: 0
出版历程
- 收稿日期: 2023-08-23
- 修回日期: 2023-12-05
- 网络出版日期: 2023-12-13
- 刊出日期: 2024-06-30

A Joint Optimization Method for Trajectory and Power of Unmanned Aerial Vehicle assisted Over-the-Air Computation

LI Song^{1, 2},
LI Jiaqi¹,
WANG Bowen¹,
CHEN Ruirui¹,
SUN Yanjing^{1, 2},
ZHANG Xiaoguang^{1
, ,}

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency Collaboration, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (62071472, 62101556), The Fundamental Research Funds for the Central Universities (2020ZDPYMS26), The Natural Science Foundation of Jiangsu Province of China (BK20200650, BK20210489), Future Network Research Foundation of Jiangsu Province (FNSRFP-2021-YB-12)

摘要

摘要: 无人机(UAV)辅助的空中计算(AirComp)系统为大范围、分布式数据的快速聚合提供了有效的解决方法。该文研究了通过无人机辅助空中计算系统联合轨迹规划与功率优化方法。UAV作为移动基站，通过调整其运动轨迹和地面传感器节点发射功率，实现AirComp系统聚合数据均方误差的最优化。在UAV轨迹、传感器功率限制下，联合优化UAV轨迹、去噪因子和传感器功率，使时间平均均方误差最小化。基于块坐标下降和逐次凸逼近方法，提出无人机飞行轨迹与功率联合优化算法，并通过仿真验证了所提算法的性能。
- 空中计算 /
- 轨迹规划 /
- 功率控制 /
- 无人机
Abstract: The Unmanned Aerial Vehicle (UAV)assisted over-the-Air Computation(AirComp) system provides an effective solution for the fast aggregation of large-scale and distributed data. In this paper, a joint trajectory planning and power optimization method through UAV-assisted AirComp system is investigated. As a mobile base station, UAV is used to optimize the mean square error of the aggregated data of the AirComp system by adjusting its trajectory and transmitting power of the ground sensors. Under the limitations of UAV trajectory and sensor power, the UAV flight trajectory, the scaling factor and sensor power are jointly optmized to minimize the time-averaged mean square error. Based on the block coordinate descent and successive convex approximation methods, the joint optimization algorithm of UAV flight trajectory and power is proposed. Simulation results verify the performance of the proposed algorithm.
- Over-the-Air Computation (AirComp) /
- Trajectory planning /
- Power control /
- Unmanned Aerial Vehicle (UAV)

HTML全文

图 1 无人机基站AirComp系统模型

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图 2 时间平均均方方差迭代图

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图 3 不同飞行时间的UAV飞行轨迹

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图 4 时间平均MSE与飞行时间的关系

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图 5 时间平均MSE与噪声功率的关系

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图 6 时间平均MSE与信道估计误差的关系

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算法1　无人机飞行轨迹与功率优化算法
定义无人机飞行时间N；传感器的最大发射功率$\{ {P_k}[n]\} $；传感　器的平均发射功率$\{ \overline {{P_k}} [n]\} $；收敛精度$\xi $；环境参数u和v；误差　幅度参数A。
(1) 定义$r = 0$,${R^0} = 1$；初始化无人机的飞行轨迹$ \{ {{\boldsymbol{q}}^0}[n]\} $；传　感器初始发射功率$\{ p_k^0[n]\} $；
(2) 循环
(3) r=r+1
(4) 利用$ \{ {{\boldsymbol{q}}^{r - 1}}[n]\} $和$ \{ p_k^{r - 1}[n]\} $，根据式(16)求解$\{ {\eta ^r}[n]\} $并且　更新$\{ \eta [n]\} $；
(5) 利用$ \{ {{\boldsymbol{q}}^{r - 1}}[n]\} $和$\{ {\eta ^r}[n]\} $，根据式(18)求解$ \{ p_k^r[n]\} $并且更　新$ \{ p_k^{}[n]\} $；
(6) 利用$\{ {\eta ^r}[n]\} $和$ \{ p_k^r[n]\} $，根据式(26)求解$ \{ {{\boldsymbol{q}}^r}[n]\} $并且更新　$ \{ {\boldsymbol{q}}[n]\} $；
(7) 根据式(10)和式(11)计算${\overline {{\text{MSE}}} ^r}$，并赋值${R^r} = {\overline {{\text{MSE}}} ^r}$；
(8) 若${{({R^r} - {R^{r - 1}})} \mathord{\left/ {\vphantom {{({R^r} - {R^{r - 1}})} {{R^r}}}} \right. } {{R^r}}} \le \xi $，认为算法收敛，退出循环；
(9) 问题得以求解得到次优解：去噪因子$\{ \eta [n]\} $，传感器发射功　率$\{ p_k^{}[n]\} $和UAV最优飞行轨迹$\{ {\boldsymbol{q}}[n]\} $；

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