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

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

大连理工大学信息与通信工程学院大连 116024

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
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- 被引次数: 0
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
- 刊出日期: 2024-09-26

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

School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China

Funds: The National Key R&D Program of China (2020YFB1807002), The National Natural Science Foundation of China (62371086, 62271099)

摘要

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。
- 无人机 /
- 非正交多址接入 /
- 凸优化 /
- 用户配对 /
- 资源分配
Abstract: Ubiquitous coverage will become the main form of 6G networks, and complete the deployment in the mountains, hills, deserts and other blind area, to achieve full-area wireless coverage. However, the large-scale deployment of terrestrial base stations in remote areas is extremely difficult. For this reason, combining Unmanned Aerial Vehicle (UAV) communications with Non-Orthogonal Multiple Access (NOMA) technology, an energy-efficient three-dimensional coverage scheme to maximize the energy efficiency of network throughput is proposed in this paper. First, the system model is established and a user pairing algorithm is proposed based on the K-Means algorithm and the Gale-Shapley algorithm. Then, after user pairing is completed, the initial problem is split into two optimization subproblems, which are transformed to convex respectively. Finally, the block coordinate ascent method is used to alternately optimize the UAV trajectory and transmit power to maximize the energy efficiency. Simulation results show that compared with benchmarks, the proposed scheme can significantly improve the throughput energy efficiency of air-ground networks under large-scale wireless coverage.
- Unmanned Aerial Vehicle (UAV) /
- Non-Orthogonal Multiple Access (NOMA) /
- Convex optimization /
- User pairing /
- Resource allocation

HTML全文

图 1 高能效立体覆盖方案系统模型

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图 2 算法2的能效优化收敛性分析

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图 3 地面用户分布及部分用户配对情况

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图 4 能效最优与速率最优方案的空中基站轨迹。

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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

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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

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图 7 不同方案的吞吐量能效随周期变化

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1 用户配对算法

(1) 输入w_k, k $\in {\mathcal{K}}$。
(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：C_t, t$\in ${1,2}。
(4) repeat
(5) 　for i in ${\mathcal{K}} $ do
(6) 　　for j = 1 to 2 do
(7) 　　　计算w_i与μ_j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 $\exists \;{u_x} \in {\mathcal{C}_1},$ u_x没有配对且未向${\mathcal{C}_2}$中的所有用户请求配　　　　　对do
(23) u_y←$\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 u_y未配对 then
(25) 　令u_x和u_y配对。
(26) else if u_x和u_y的距离相较于u_y现有的配对用户u_z更远 then
(27) 　取消u_y和u_z的配对，令u_x和u_y配对。
(28) else
(29) 　u_y拒绝u_x的请求。
(30) end if
(31) end while
(32) 输出用户配对。

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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], $\mu $和误差容限e。
(3) repeat
(4) 　i←i+1。
(5) 　代入Q^[i–1]，$\mu $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。

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