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一种面向多任务的无人机辅助的通信网络资源分配与轨迹优化研究

裴二荣 娄宇涵 李永刚 黎伟

裴二荣, 娄宇涵, 李永刚, 黎伟. 一种面向多任务的无人机辅助的通信网络资源分配与轨迹优化研究[J]. 电子与信息学报, 2024, 46(7): 2748-2756. doi: 10.11999/JEIT230974
引用本文: 裴二荣, 娄宇涵, 李永刚, 黎伟. 一种面向多任务的无人机辅助的通信网络资源分配与轨迹优化研究[J]. 电子与信息学报, 2024, 46(7): 2748-2756. doi: 10.11999/JEIT230974
PEI Errong, LOU Yuhan, LI Yonggang, LI Wei. Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2748-2756. doi: 10.11999/JEIT230974
Citation: PEI Errong, LOU Yuhan, LI Yonggang, LI Wei. Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2748-2756. doi: 10.11999/JEIT230974

一种面向多任务的无人机辅助的通信网络资源分配与轨迹优化研究

doi: 10.11999/JEIT230974
基金项目: 国家自然科学基金(62071077),重庆成渝科技创新项目(KJCXZD2020026)
详细信息
    作者简介:

    裴二荣:男,教授,研究方向为无线移动通信

    娄宇涵:男,硕士生,研究方向为无人机通信、移动边缘计算

    李永刚:男,副教授,研究方向为无线移动通信

    黎伟:男,博士,研究方向为无线移动通信

    通讯作者:

    娄宇涵 1162961114@qq.com

  • 中图分类号: TN929.5

Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network

Funds: The National Natural Science Foundation of China (62071077), Chongqing Chengyu Science and Technology Innovation Project (KJCXZD2020026)
  • 摘要: 装载各种有效荷载的无人机(UAV)能够实现传感、通信和计算等多任务,因而常被部署到数据采集(DA)和辅助计算等领域。但是到目前为止,绝大多数研究仅专注于单一功能的无人机辅助的通信网络资源分配与轨迹优化,对于面向多任务的资源分配和轨迹优化问题还未解决。为此,该文提出一种综合考虑无人机数据采集、数据广播以及计算任务卸载的无人机辅助的通信网络资源优化的分配策略,旨在通过联合优化传输占空比、用户发射功率与无人机轨迹,在满足目标位置采集数据实时广播的前提下,最大化用户卸载量。为了解决多变量耦合优化问题,提出了基于块坐标下降(BCD)和连续凸逼近(SCA)的高效迭代优化算法,将耦合优化问题分解为3个子问题进行迭代优化。最后,大量仿真结果表明,该算法在公平性和总卸载计算量方面都优于其他测试方案。
  • 图  1  系统模型

    图  2  无人机时隙分配策略

    图  3  6种方案在每个时隙的总卸载吞吐量

    图  4  6种方案的收敛性

    图  5  无人机采集数据下行吞吐量

    图  6  无人机飞行高度H对系统性能的影响

    图  7  用户传输能量对系统性能的影响

    图  8  不同的$ {D_k} $和$ {d_{{\text{set}}}} $对系统性能的影响

    1  面向多任务的无人机轨迹和资源迭代优化算法

     (1) 初始化最大误差 $ \varepsilon $ 、总量吐量迭代值obj、最大迭代次数 $ \alpha $ 与
      $ {{{D}}^i}\left( n \right) = \left\{ {{\boldsymbol{A}}_n^i,{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $
     (2) $ {\bf{while}} $ $ i \lt \alpha $ $ {\bf{do}} $
     (3)   $ i = i + 1, $
     (4)  给定 $ \left\{ {{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $ ,求解占空比子问题P1,得到最优情况
        $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $ ;
     (5)  给定 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^i} \right\} $ ,求解功率子问题P2,得到最优情况
        $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ ;
     (6)  给定 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ ,求解轨迹子问题P3.1,得到最优情
        况 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^{i + 1},{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ ;
     (7)  将 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^{i + 1},{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ 带入目标函数中计算当前总吞吐
        量迭代值objnew;
     (8)   $ {\bf{if }}{\text{ abs}}\left( {{\text{objnew}} - {\text{obj}}} \right) \le \varepsilon {\text{ }}{\bf{then}} $ break
     (9)  else $ {\text{obj}} = {\text{objnew}} $ ;
     (10) end while
     (11) 输出最优参数值 $ \left\{ {{\boldsymbol{A}}_n^*,{\boldsymbol{Q}}_n^*,{\boldsymbol{P}}_{m,n}^*} \right\} = \left\{ {{\boldsymbol{A}}_n^i,{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $ ,
       计算获得当前最大的总吞吐量为 $ {\text{ob}}{{\text{j}}^*} = {\text{objnew}} $ ;
    下载: 导出CSV

    表  1  关键的仿真参数

    参数 取值 参数 取值
    飞行高度$ H $ 100 m 最大传输能量$ {E_{\max }} $ 0.4 J
    飞行时隙$ N $ 60 最小传输速率$ {R_{\min }} $ 4×104 bit/s
    飞行周期$ T $ 60 s 最大平均功率$ {P_{\max }} $ 500 mW
    信道带宽$ B $ 1 MHz 目标区域半径$ {d_{{\text{set}}}} $ 10 m
    最大速度$ {V_{\max }} $ 20 m/s 功率谱密度$ {N_0} $ –90 dBm/Hz
    信道功率增益$ {\beta _0} $ –60 dB 目标位置数据量$ {D_k} $ 7×106 bit
    下载: 导出CSV
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    PEI Errong, CHEN Xinhu, CHEN Qimei, et al. 3D trajectory and power optimization method based on full spectrum sharing[J]. Journal of Electronics & Information Technology, 2024, 3(46): 835–847. doi: 10.11999/JEIT230261.
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出版历程
  • 收稿日期:  2023-09-06
  • 修回日期:  2024-01-25
  • 网络出版日期:  2024-02-27
  • 刊出日期:  2024-07-29

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