航空学报 > 2023, Vol. 44 Issue (19): 328486-328486   doi: 10.7527/S1000-6893.2023.28486

智能反射面辅助无人机移动边缘计算任务数据最大化方法

李伟, 郭艳(), 李宁, 刘存涛, 袁昊   

  1. 陆军工程大学 通信工程学院,南京 210016
  • 收稿日期:2023-01-09 修回日期:2023-02-06 接受日期:2023-03-22 出版日期:2023-10-15 发布日期:2023-03-31
  • 通讯作者: 郭艳 E-mail:guoyan_1029@sina.com
  • 基金资助:
    江苏省自然科学基金(BK20211227);国家自然科学基金(61871400);国家人才项目(2022-JCJQ-ZQ-01);军队高层次人才创新工程(KYZYJQJY2101)

Intelligent reflector surface assisted UAV mobile edge computing task data maximization method

Wei LI, Yan GUO(), Ning LI, Cuntao LIU, Hao YUAN   

  1. College of Communications Engineering,Army Engineering University,Nanjing 210016,China
  • Received:2023-01-09 Revised:2023-02-06 Accepted:2023-03-22 Online:2023-10-15 Published:2023-03-31
  • Contact: Yan GUO E-mail:guoyan_1029@sina.com

摘要:

针对实际通信环境较为复杂,用户设备(UE)和无人机(UAV)之间的链路易受阻塞,通信质量差的问题,提出了智能反射面(IRS)重构通信环境,增强无人机移动边缘计算网络性能。在考虑到设备能耗和计算能力的约束下,通过联合优化智能反射面相移控制、无人机飞行轨迹、发射功率和时隙划分最大化用户任务数据。将以上高度复杂的非凸问题分为3个子问题,首先推导出智能反射面相移闭式解,而后引入松弛变量、一阶泰勒表达式和逐次凸逼近技术将原问题转换为发射功率、时隙划分和轨迹优化2个近似凸子问题,最后基于块坐标下降法迭代求解。仿真结果表明:提出的智能反射面辅助的无人机移动边缘计算相比于传统的无人机移动边缘计算,可以有效提高任务数据量。

关键词: 无人机(UAV), 智能反射面(IRS), 移动边缘计算(MEC), 轨迹优化, 资源分配

Abstract:

Due to the complex communication environment, the link between User Equipment (UE) and Unmanned Aerial Vehicle (UAV) is easily blocked and the communication quality is poor. This paper proposes to use an Intelligent Reflector Surface (IRS) to reconstruct the communication environment and enhance the performance of UAV mobile edge computing network. Considering the constraints of equipment energy consumption and computing power, the user task data is maximized by jointly optimizing intelligent reflector movement control, UAV flight trajectory, transmission power and time slot division. The above highly complex nonconvex problems are divided into three sub-problems. Firstly, the closed-form solution for intelligent reflector surface shift is derived. Then, the relaxation variable, first-order Taylor expression and successive convex approximation technique are introduced to transform the original problem into two approximate convex problems, namely transmission power, and slot division and trajectory optimization. Finally, the iterative solution is obtained based on the block coordinate descent method. The simulation results show that compared with the traditional UAV moving edge calculation, the proposed method can effectively improve the amount of mission data.

Key words: Unmanned Aerial Vehicle (UAV), Intelligent Reflector Surface (IRS), Mobile Edge Computing (MEC), trajectory optimization, resource management

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