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无人机高能效立体覆盖中轨迹与资源优化

赵楠 黄香港 邓娜 邹德岳

赵楠, 黄香港, 邓娜, 邹德岳. 无人机高能效立体覆盖中轨迹与资源优化[J]. 电子与信息学报. doi: 10.11999/JEIT240151
引用本文: 赵楠, 黄香港, 邓娜, 邹德岳. 无人机高能效立体覆盖中轨迹与资源优化[J]. 电子与信息学报. doi: 10.11999/JEIT240151
ZHAO Nan, HUANG Xianggang, DENG Na, ZOU Deyue. Trajectory and Resource Optimization in Energy-Efficient 3D Coverage of Unmanned Aerial Vehicle[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240151
Citation: ZHAO Nan, HUANG Xianggang, DENG Na, ZOU Deyue. Trajectory and Resource Optimization in Energy-Efficient 3D Coverage of Unmanned Aerial Vehicle[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240151

无人机高能效立体覆盖中轨迹与资源优化

doi: 10.11999/JEIT240151
基金项目: 国家重点研发计划(2020YFB1807002),国家自然科学基金(62371086, 62271099)
详细信息
    作者简介:

    赵楠:男,教授,博士生导师,研究方向为下一代无线通信

    黄香港:男,硕士生,研究方向为无人机通信

    邓娜:女,副教授,硕士生导师,研究方向为无人机通信

    邹德岳:男,副教授,硕士生导师,研究方向为下一代无线通信

    通讯作者:

    赵楠 zhaonan@dlut.edu.cn

  • 中图分类号: TN929.5

Trajectory and Resource Optimization in Energy-Efficient 3D Coverage of Unmanned Aerial Vehicle

Funds: The National Key R&D Program of China (2020YFB1807002), The National Natural Science Foundation of China (62371086, 62271099)
  • 摘要: “泛在覆盖”将成为6G的主流网络形式,完成在高山、丘陵、沙漠等网络盲区的通信部署,实现全域无线覆盖,但在远区大规模部署地面基站较为困难。为此,该文将无人机(UAV)通信与非正交多址接入(NOMA)相结合,提出一种高能效立体覆盖方案最大化网络吞吐量能效。首先,建立系统模型,基于K-Means算法与Gale-Shapley算法提出用户配对方案。其次,在用户配对完成后,将初始问题拆分为两个优化子问题并分别转化为凸。最后,利用块坐标上升法交替优化无人机轨迹和发射功率最大化能量效率。仿真结果表明,相较于其它基准方案,该文方案可以显著提高大规模无线覆盖下空地网络的吞吐量能效。
  • 图  1  高能效立体覆盖方案系统模型

    图  2  算法2的能效优化收敛性分析

    图  3  地面用户分布及部分用户配对情况

    图  4  能效最优与速率最优方案的空中基站轨迹。

    图  5  能效最优与速率最优方案下空中基站的瞬时速度与瞬时加速度

    图  6  飞行时刻为60 s时空中基站对用户的信号功率辐射图。

    图  7  不同方案的吞吐量能效随周期变化。

    1  用户配对算法

     (1) 输入wk, k $\in $ K
     (2) 从地面用户坐标中随机选取2个作为初始聚类中心:{μ1, μ2}。
     (3) 初始化用户簇:Ct, t$\in ${1,2}。
     (4) repeat
     (5)  for i in K do
     (6)   for j = 1 to 2 do
     (7)    计算wiμj之间的距离${d_{i,j}} \triangleq \left\| {{{\boldsymbol{w}}_i} - {{\boldsymbol{\mu}} _j}} \right\|$。
     (8)   end for
     (9)   定义${\lambda _i} = \arg \mathop {\min }\limits_j {d_{i,j}}$。更新${\mathcal{C}_{{\lambda _i}}} = {\mathcal{C}_{{\lambda _i}}} \cup \left\{ {{u_i}} \right\}$。
     (10) end for
     (11) for j = 1 to 2 do
     (12)  更新聚类中心:${{\boldsymbol{\mu}} _j} = \sum\nolimits_{{u_i} \in {\mathcal{C}_j}} {{{{{\boldsymbol{w}}_i}} \mathord{\left/ {\vphantom {{{{\boldsymbol{w}}_i}} {|{\mathcal{C}_j}|}}} \right. } {|{\mathcal{C}_j}|}}} $。
     (13) end for
     (14) until聚类中心不发生变化。
     (15) while |$\mathcal{C}_1 $||$\mathcal{C}_2 $| do
     (16) if |$\mathcal{C}_1 $|>|$\mathcal{C}_2 $| then
     (17)  定义$\tau = \arg \mathop {\min }\limits_i {{{d_{i,1}}} \mathord{\left/ {\vphantom {{{d_{i,1}}} {{d_{i,2}}}}} \right. } {{d_{i,2}}}}$,更新
         ${\mathcal{C}_1} = {\mathcal{C}_1}\backslash \{ {u_\tau }\} ,{\mathcal{C}_2} = {\mathcal{C}_2} \cup \{ {u_\tau }\} $。
     (18) else if |$\mathcal{C}_1 $|<|$\mathcal{C}_2 $| then
     (19)  定义$\tau = \arg \mathop {\min }\limits_i {{{d_{i,2}}} \mathord{\left/ {\vphantom {{{d_{i,2}}} {{d_{i,1}}}}} \right. } {{d_{i,1}}}}$,更新
         ${\mathcal{C}_2} = {\mathcal{C}_2}\backslash \{ {u_\tau }\} ,{\mathcal{C}_1} = {\mathcal{C}_1} \cup \{ {u_\tau }\} $。
     (20) end if
     (21) end while
     (22) while$\exists \;{u_x} \in {\mathcal{C}_1},$ux没有配对且未向${\mathcal{C}_2}$中的所有用户请求配
     对do
     (23) uy←$\mathcal{C}_2 $中没有被${u_x}$请求配对过且距离其最远的用户。
     (24) if uy未配对 then
     (25)  令uxuy配对。
     (26) else if uxuy的距离相较于uy现有的配对用户uz更远 then
     (27)  取消uyuz的配对,令uxuy配对。
     (28) else
     (29)  uy拒绝ux的请求。
     (30) end if
     (31) end while
     (32) 输出用户配对。
    下载: 导出CSV

    2  能效最大化资源分配算法

     (1) 通过算法1确定用户配对。
     (2) 初始化i←0,Q[i],P[i],μ和误差容限e。
     (3) repeat
     (4)  ii+1。
     (5)  代入Q[i-1]μ解决问题(P4),得到最优解Q*,更新
     Q[i]Q*
     (6)  代入P [i-1]解决问题(P6),得到最优解P*,更新P[i]P*
     (7)  更新
    $ {\boldsymbol{\mu}} = \dfrac{{\displaystyle\sum\limits_{m = 1}^M {\displaystyle\sum\limits_{n = 1}^N {\left( {R_m^{\text{s}}[n] + R_m^{\text{w}}[n]} \right)} } }}{{\displaystyle\sum\limits_{n = 1}^N {\left( {{P_{\max }} + {P_{{\text{Base}}}} + {{\text{c}}_1}{{\left\| {{\boldsymbol{v}}[n]} \right\|}^3} + \dfrac{{{{\text{c}}_2}}}{{\left\| {{\boldsymbol{v}}[n]} \right\|}}\left( {1 + \dfrac{{{{\left\| {{\boldsymbol{a}}[n]} \right\|}^2}}}{{{{\text{g}}^2}}}} \right)} \right)} }} $。
     (8)  计算第i次迭代中(P1)目标函数值obj[i]
     (9) until |obj[i]–obj[i–1]| <e。
     (10) 输出Q,P
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-03-07
  • 修回日期:  2024-05-14
  • 网络出版日期:  2024-05-22

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