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针对物联网(Internet of Things, IoT)边缘计算(Mobile Edge Computing, MEC)网络中建筑物遮挡、信道质量差导致的能耗增大的问题，提出一种智能反射表面(Intelligent Reflecting Surface, IRS)辅助无人机(Unmanned Aerial Vehicle, UAV)反向散射边缘计算网络能效最大化方案。通过优化反向散射过程中用户的反射系数、UAV的传输功率、用户设备的计算资源、IRS的相移矩阵和UAV的轨迹实现系统能效最大化。针对强耦合非凸优化问题，采用基于Dinkelbach设计的交替迭代优化算法，利用拉格朗日对偶、连续凸近似(Successive Convex Approximation, SCA)及最大最小化算法迭代求解。最后，将所提方案与固定UAV轨迹和IRS相移方案及单独固定UAV轨迹方案进行对比，仿真结果表明，所提方案在不同参数下可有效降低系统能耗，降幅最高可达51.2%,能够更有效地优化资源分配并实现系统网络能效最大化。
Abstract：To address the increased energy consumption caused by building blockages and poor channel conditions in mobile edge computing(MEC) of Internet of Things(IoT) networks, an energy efficiency maximization framework for an intelligent reflecting surface(IRS)-assisted unmanned aerial vehicle(UAV) backscatter MEC network is proposed. It jointly optimizes the user reflection coefficients during backscatter communication, UAV transmit power, computational resource allocation at user devices, IRS phase shift matrix, and UAV trajectory to maximize system energy efficiency. For the strongly coupled and non-convex characteristics of the formulated optimization problem, an alternating iterative optimization algorithm based on Dinkelbach's method is developed. It adopts Lagrangian duality, successive convex approximation(SCA), and max-min optimization techniques to iteratively solve the problem. Finally, compared with benchmark schemes employing fixed UAV trajectories and IRS phase shifts or only fixed UAV trajectories, simulation results show that the proposed scheme can reduce system energy consumption by up to 51.2% under varying system parameters. It can effectively optimize the resource allocation and realize maximal energy efficiency for system network.
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基本信息:
DOI：10.13682/j.issn.2095-6533.2025.04.002
中图分类号:TN929.5;V279
引用信息:
[1]刘伯阳,赵云,党儒鸽,等.IRS辅助无人机反向散射边缘计算网络能效最大化方案[J].西安邮电大学学报,2025,30(04):9-20.DOI:10.13682/j.issn.2095-6533.2025.04.002.
基金信息:
国家自然科学基金项目(61901366); 陕西省自然科学基础研究计划项目(2020JQ-851); 陕西省普通高校青年杰出人才支持计划项目