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面向低空智联网的分布式鲁棒任务卸载方法

贾子晔 姜官旺 崔灿 张磊 吴启晖

贾子晔, 姜官旺, 崔灿, 张磊, 吴启晖. 面向低空智联网的分布式鲁棒任务卸载方法[J]. 电子与信息学报, 2025, 47(5): 1450-1460. doi: 10.11999/JEIT240799
引用本文: 贾子晔, 姜官旺, 崔灿, 张磊, 吴启晖. 面向低空智联网的分布式鲁棒任务卸载方法[J]. 电子与信息学报, 2025, 47(5): 1450-1460. doi: 10.11999/JEIT240799
JIA Ziye, JIANG Guanwang, CUI Can, ZHANG Lei, WU Qihui. Distributionally Robust Task Offloading for Low-Altitude Intelligent Networks[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1450-1460. doi: 10.11999/JEIT240799
Citation: JIA Ziye, JIANG Guanwang, CUI Can, ZHANG Lei, WU Qihui. Distributionally Robust Task Offloading for Low-Altitude Intelligent Networks[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1450-1460. doi: 10.11999/JEIT240799

面向低空智联网的分布式鲁棒任务卸载方法

doi: 10.11999/JEIT240799
基金项目: 国家自然科学基金(62301251),江苏省自然科学基金(BK20220883),东南大学移动通信全国重点实验室开放研究基金(2024D04),航空科学基金(2023Z071052007)
详细信息
    作者简介:

    贾子晔:女,副教授,硕士生导师,研究方向为天地一体化网络、低空智联网、卫星网络等

    姜官旺:男,硕士生,研究方向为低空智联网

    崔灿:女,硕士生,研究方向为低空智联网

    张磊:男,教授,硕士生导师,研究方向为嵌入式系统与边缘计算、人工智能与无线自组网

    吴启晖:男,教授,博士生导师,研究方向为认知信息论、天地一体化智能信息网络、电磁空间频谱认知智能管控、无人机认知集群

    通讯作者:

    张磊 Zhang_lei@nuaa.edu.cn

  • 中图分类号: TN92

Distributionally Robust Task Offloading for Low-Altitude Intelligent Networks

Funds: The National Natural Science Foundation of China (62301251), The Natural Science Foundation of Jiangsu Province (BK20220883), The Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (2024D04), The Aeronautical Science Foundation of China (2023Z071052007)
  • 摘要: 作为新质生产力的低空智联网(LAIN),主要由低空各类飞行器组成,可辅助实现空基多接入边缘计算(MEC)功能,能够有效应对实时数据处理和超低时延通信的挑战。考虑到任务的数据量大小具有随机性,该文聚焦研究基于LAIN的MEC网络中任务的卸载决策问题,以优化最差情况下的系统时延,保障服务的鲁棒性。首先,为刻画任务量的不确定性,利用历史数据构建基于概率距离度量的不确定集合,并建模了相应的分布鲁棒优化问题,以最小化最差任务大小概率分布情况下的系统时延。然后,为求解该最小化最大混合整数规划问题,将问题分解为嵌套的内层问题和外层问题,并基于分支定界法和二进制鲸鱼算法机制,设计了一种基于不确定任务大小的分布式鲁棒任务卸载优化算法(DRTOOA)。最后,通过仿真验证,结果表明所提DRTOOA能够优化系统时延,且具有较高的求解效率。
  • 图  1  基于LAIN的MEC模型

    图  2  算法流程图

    图  3  不同距离度量方法下不同算法中系统时延与不确定集容差的关系

    图  4  多种不确定集构造方式下算法运行时间

    图  5  系统时延与不确定集容差的关系

    图  6  不同距离度量方法下数据大小的概率分布与不确定集容差的关系

    图  7  不同距离度量下系统时延与HAP限额的关系

    图  8  不同距离度量下系统时延与GU数量的关系

    1  整体算法

     (1) 初始化参数:$ r = 1 $,$ L(r) = + \infty $;
     (2) 将参考分布$ \mathbb{P}_i^0 $赋值给最差分布$ {\mathbb{P}_i} $;
     (3) repeat
     (4)  $ r = r + 1 $;
     (5)  将最差分布$ {\mathbb{P}_i} $带入问题$ {\text{P1}} $,得到问题$ {\text{P2}} $,利用算法2求解问题$ {\text{P2}} $,得到$ {\boldsymbol{x}} $, $ {\boldsymbol{y}} $, $ {\boldsymbol{z}} $和最小时延$ L(r) $;
     (6)  将$ {\boldsymbol{x}} $, $ {\boldsymbol{y}} $和$ {\boldsymbol{z}} $带入问题$ {\text{P1}} $,得到问题$ {\text{P3}} $,利用优化求解器GUROBI求解问题$ {\text{P3}} $,得到最差分布$ {\mathbb{P}_i} $;
     (7) until $ L(r - 1) - L(r) \le \delta $
    下载: 导出CSV

    2  整数问题求解

     (1) 将问题$ {\text{P2}} $的整数变量$ {\boldsymbol{x }}$, $ {\boldsymbol{y}} $和$ {\boldsymbol{z}} $松弛为0~1的连续变量$ {\boldsymbol{x}}' $, $ {\boldsymbol{y}}' $和$ {\boldsymbol{z}}' $,得到问题$ {\text{P2}}' $,利用问题求解器求解问题$ {\text{P2}}' $,得到0~1的连续值$ \hat {\boldsymbol{x}} $;
     (2) repeat
     (3)  在$ \hat {\boldsymbol{x}} $中选择第1个出现的非整数值作为分支变量$ {\boldsymbol{x}}_{}^* $;
     (4)  添加约束 $ {\boldsymbol{x}}_{}^* = {{\textit{0}}} $到问题$ {\text{P2}}' $,求解得到优化结果$ {\text{LAT0}} $;
     (5)  添加约束 $ {\boldsymbol{x}}_{}^* = {{\textit{1}}} $到问题$ {\text{P2}}' $,求解得到优化结果$ {\text{LAT1}} $;
     (6)  if $ {\text{LAT0 < LAT1}} $
     (7)   $ {\boldsymbol{x}}_{}^* ={{\textit{0}}} $;
     (8)   添加约束 $ {\boldsymbol{x}}_{}^* = {{\textit{0}}} $到问题$ {\text{P2}}' $,并更新问题$ {\text{P2}}' $;
     (9)  else
     (10)   $ {\boldsymbol{x}}_{}^* = {{\textit{1}}} $;
     (11)   添加约束 $ {\boldsymbol{x}}_{}^* = {{\textit{1}}} $到问题$ {\text{P2}}' $,并更新问题$ {\text{P2}}' $;
     (12) endif
     (13) 利用优化求解器GUROBI求解问题$ {\text{P2}}' $,得到0~1的连续值$ \hat {\boldsymbol{x}} $;
     (14) until$ \hat {\boldsymbol{x }}$中全为整数
     (15) $ {\boldsymbol{x}} = \hat {\boldsymbol{x}} $ ,将$ {\boldsymbol{x}} $代入问题$ {\text{P2}} $;
     (16) 初始化:鲸鱼数量,最大迭代次数,鲸鱼位置($ {\boldsymbol{y}} $和$ {\boldsymbol{z}} $取值);
     (17) 根据式(50)计算每只鲸鱼适应度值,选出最优鲸鱼的位置(最优$ {\boldsymbol{y}} $和$ {\boldsymbol{z}} $取值);
     (18) repeat
     (19) 更新鲸鱼位置 ;
     (20) 计算每只鲸鱼适应度值;
     (21) 更新最优鲸鱼的位置;
     (22) until达到收敛条件或最大迭代次数
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-09-18
  • 修回日期:  2025-03-05
  • 网络出版日期:  2025-03-14
  • 刊出日期:  2025-05-01

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