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恶意干扰下的无人机辅助边缘计算加权能耗与时延智能优化

杨和林 郑梦婷 刘帅 肖亮 谢显中 熊泽辉

杨和林, 郑梦婷, 刘帅, 肖亮, 谢显中, 熊泽辉. 恶意干扰下的无人机辅助边缘计算加权能耗与时延智能优化[J]. 电子与信息学报. doi: 10.11999/JEIT230986
引用本文: 杨和林, 郑梦婷, 刘帅, 肖亮, 谢显中, 熊泽辉. 恶意干扰下的无人机辅助边缘计算加权能耗与时延智能优化[J]. 电子与信息学报. doi: 10.11999/JEIT230986
YANG Helin, ZHENG Mengting, LIU Shuai, XIAO Liang, XIE Xianzhong, XIONG Zehui. Intelligent Weighted Energy Consumption and Delay Optimization for UAV-Assisted MEC Under Malicious Jamming[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230986
Citation: YANG Helin, ZHENG Mengting, LIU Shuai, XIAO Liang, XIE Xianzhong, XIONG Zehui. Intelligent Weighted Energy Consumption and Delay Optimization for UAV-Assisted MEC Under Malicious Jamming[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230986

恶意干扰下的无人机辅助边缘计算加权能耗与时延智能优化

doi: 10.11999/JEIT230986
基金项目: 国家自然科学基金(62371408, 62301467, U21A20444, 61971366),中央高校基本科研业务费专项资金(20720220080),厦门市自然科学基金项目(3502Z202371010),“小米青年学者”项目,科技部重点研发计划项目(2023YFB3107603)
详细信息
    作者简介:

    杨和林:男,副教授,博士生导师,研究方向为无线通信、边缘计算、智能通信、资源分配

    郑梦婷:女,硕士生,研究方向为边缘计算、无人机通信、资源调度

    刘帅:男,博士生,研究方向为边缘计算、抗干扰通信、无线资源调度、无人机通信

    肖亮:女,教授,博士生导师,研究方向为无线通信、智能通信、通信安全、物理层安全

    谢显中:男,教授,博士生导师,研究方向为通信网理论与技术、物联网、可见光通信、无人机通信

    熊泽辉:男,助理教授,博士生导师,研究方向为边缘计算、无线网络技术、强化学习、无线通信

    通讯作者:

    杨和林 helinyang066@xmu.edu.cn

  • 中图分类号: TN92

Intelligent Weighted Energy Consumption and Delay Optimization for UAV-Assisted MEC Under Malicious Jamming

Funds: The National Natural Science Foundation of China (62371408, 62301467, U21A20444, 61971366), The Fundamental Research Funds for the Central Universities (20720220080), Natural Science Foundation of Xiamen(3502Z202371010), Xiaomi Young Talents Program, National Key Research and Development Program of China (2023YFB3107603)
  • 摘要: 近年来,将移动边缘计算(MEC)服务器搭载在无人机(UAV)上为地面移动用户提供服务备受学术界和工业界广泛的关注。但在恶意干扰环境下,如何有效调度资源降低系统时延和能耗成为关键问题。为此,针对干扰机影响下无人机辅助边缘计算的问题,该文建立一个以最小化加权能耗与时延为目标的模型,联合优化无人机飞行轨迹、资源调度和任务分配来提升无人机辅助移动边缘计算系统性能。鉴于优化问题难求解以及恶意干扰行为动态多变,该文提出了一种基于双延迟深度确定性策略梯度(TD3)的资源调度算法,同时结合优先经验回放(PER)机制提高算法收敛速度和稳定性,高效对抗恶意干扰攻击。仿真结果表明所提算法较其他算法,能够有效降低系统的时延和能耗,并具有很好的收敛性与稳定性。
  • 图  1  干扰环境下无人机辅助边缘计算系统模型

    图  2  无人机辅助边缘计算系统中基于TD3的资源调度算法框

    图  3  PER-TD3算法与其他算法的收敛性能比较

    图  4  不同用户CPU计算能力下5个方案开销对比

    图  5  不同计算任务下5个方案开销对比

    图  6  不同用户数目下5个方案开销对比

    图  7  无人机飞行轨迹

    1  基于PER-TD3的时延和能耗加权最小化的算法流程

     (1) 设置无人机辅助边缘计算系统环境,初始化在线网络参数${w_1},{w_2},\theta $和目标网络参数:$w_1^{'} \leftarrow {w_1},w_2^{'} \leftarrow {w_2},{\theta ^{'}} \leftarrow \theta $。
     (2) 初始化经验回放池。
     (3) 循环训练轮数${\text{Episode}} = 1,2, \cdots ,E$:
     (4)  重置参数并得到初始状态${s_1}$;
     (5)  循环训练步数${\text{Step}} = 1,2, \cdots ,N$:
     (6)   通过Actor选择加入噪声的动作:${a_n}$;
     (7)   无人机执行动作${a_n}$,进入下一个状态${s_{n + 1}}$,并从环境中获得奖励${r_n}$,并计算当前经验的优先级${p_n}$;
     (8)   如果经验池未满,将四元组$\left( {{s_n},{a_n},{r_n},{s_{n + 1}}} \right)$及其优先级存储至经验池中;
     (9)   如果经验池满,在经验池按照优先级选取小批量样本$\left( {{s_t},{a_t},{r_t},{s_{t + 1}}} \right)$输入网络中;
     (10)   基于策略平滑由Actor目标网络输出动作:$ {a_{t + 1}} = {u^{'}}\left( {{s_{t + 1}}\left| {{\theta ^{'}}} \right.} \right) + \varepsilon $;
     (11)   计算目标值:${y_t} = {r_t} + \gamma \min \left( {Q_1^{'}\left( {{s_{t + 1}},{a_{t + 1}}\left| {w_1^{'}} \right.} \right),Q_2^{'}\left( {{s_{t + 1}},{a_{t + 1}}\left| {w_2^{'}} \right.} \right)} \right)$;
     (12)   根据TD值更新优先级,计算重要性采样权重,更新损失函数,更新网络参数;
     (13)   直到${\text{Step}} = N$;
     (14) 直到${\text{Episode}} = E$;
     (15)计算获得无人机飞行轨迹、资源调度和任务分配策略,输出系统的能耗和时延。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-09-08
  • 修回日期:  2024-04-08
  • 网络出版日期:  2024-05-07

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