一种基于合作协同进化的智能超表面辅助无人机通信系统联合波束成形方法

仲伟志; 万诗晴; 段洪涛; 范振雄; 林志鹏; 黄洋; 毛开

doi:10.11999/JEIT240561

一种基于合作协同进化的智能超表面辅助无人机通信系统联合波束成形方法

doi: 10.11999/JEIT240561

1.
南京航空航天大学电磁频谱认知动态系统工业与信息化部重点实验室南京 211106
2.
国家无线电监测中心北京 100144

基金项目: 国家自然科学基金(62217250)，江苏省重点研发计划(产业前瞻与关键核心技术)(BE2022067, BE2022067-1, BE2022067-3)，南京航空航天大学研究生科研与实践创新计划(xcxjh20231507)

详细信息

作者简介:
仲伟志：女，副教授，研究方向为高频通信、无人机通信、频谱感知等

万诗晴：女，研究生，研究方向为无人机通信、可重构智能表面联合波束赋形

段洪涛：男，正高级工程师，研究方向为无人机通信与反制，频谱管理，短波及超短波监测等

范振雄：男，高级工程师，研究方向为无人机通信与反制，超短波干扰定位及查找，短波测向等

林志鹏：男，副研究员，研究方向为高维信道参数估计、大规模阵列信号处理、无人机通信、频谱信号感知及重构等

黄洋：男，副教授，研究方向为电磁博弈、频谱管控、物联网技术、B5G及未来无线网络等

毛开：男，博士生，研究方向为信道测量与建模

通讯作者:
仲伟志　zhongwz@nuaa.edu.cn

中图分类号: TN929
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出版历程
- 收稿日期: 2024-07-04
- 修回日期: 2024-11-08
- 网络出版日期: 2024-11-13

A Joint Beamforming Method Based on Cooperative Co-evolutionary in Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle Communication System

1.
Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
State Ratio Monitoring Center, Beijing 100144, China

Funds: The National Natural Science Foundation of China (62271250), The Key Technologies R&D Program of Jiangsu (Prospective and Key Technologies for Industry) (BE2022067, BE2022067-1, BE2022067-3), Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics (xcxjh20231507)

摘要

摘要: 针对传统联合波束成形方法在智能超表面(RIS)辅助无人机(UAV)通信系统优化中存在的局限性，包括针对RIS仅考虑相移矩阵优化、优化方法缺乏应用普适性等问题，该文面向RIS辅助无人机通信服务多用户场景，创新性提出一种基于合作协同进化(CCEA)的联合波束优化方法。该方法利用两个子种群的独立进化将联合波束成形问题分解成RIS反射波波束设计和发射端波束设计两个子问题进行求解，通过进化过程中的信息交互与协作来实现联合波束成形设计。数值仿真结果表明，相较于仅考虑RIS相移矩阵设计的联合波束优化，CCEA通过设计RIS反射波波束形状改变了反射波在3维空间中的能量分布，进而提升了接收端信干噪比和频谱效率；此外，基于种群的CCEA算法能够产生更加多样的解，因此在UAV和用户的不同位置设置下均能实现反射波对用户方向的有效覆盖，相对于传统方法能够避免局部最优、具有更强的应用普适性。
- 无人机通信 /
- 智能超表面 /
- 联合波束成形 /
- 合作协同进化
Abstract: Considering the limitations of traditional joint beamforming methods in optimizing Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communication systems, such as solely focusing on the phase shift matrix optimization of RIS and the lack of universality in the optimization approach, a joint beamforming method based on Cooperative Co-Evolutionary Algorithm (CCEA) for the RIS-assisted UAV multi-user communication system is proposed. This method decomposes the joint beamforming problem into subproblems involving RIS reflection beam design and transmitter beam design, which are solved through information exchange and collaboration during the independent evolutionary process of two subpopulations. Simulation results demonstrate that compared to joint beamforming optimization only considering RIS phase shift matrix design, CCEA changes the energy distribution of the reflection wave in three-dimensional space by optimizing the RIS reflection wave shape, leading to improved reception-side signal-to-interference-plus-noise ratio (SINR) and spectral efficiency. Additionally, CCEA generates more diverse solutions that effectively cover user directions at various UAV and user positions, avoiding local optima and exhibiting greater applicability across different scenarios compared to traditional methods.
- Unmanned Aerial Vehicle (UAV) communication /
- Reconfigurable Intelligent Surface(RIS) /
- Joint beamforming /
- Cooperative Co-Evolutionary Algorithm (CCEA)

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图 1 RIS辅助无人机MU-MISO通信系统

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图 2 CCEA性能比较

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图 3 不同UAV、用户位置下得到的RIS反射波波束方向图

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图 4 不同RIS阵元设置下系统频谱效率

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1 基于CCEA的RIS辅助无人机通信联合波束成形优化算法

(1)输入初始位置信息${{\boldsymbol{w}}_{\rm{U}}},{{\boldsymbol{w}}_{\rm{R}}},{{\boldsymbol{w}}_k}$和其他基本系统参数；获得　$ {{\boldsymbol{H}}_{{\mathrm{U}} -{\mathrm{ R}}}} $、$ {{\boldsymbol{h}}_{{\mathrm{R}} - k}} $、${{\boldsymbol{h}}_{{\mathrm{U}} - k}}$
(2)根据式(16)及式(9)生成初始子种群：　${\bf{pop}}_0^{\boldsymbol{\varPhi}} = [{{\boldsymbol{\varPhi}} _1},{{\boldsymbol{\varPhi}} _2},\cdots,{{\boldsymbol{\varPhi}} _{{\text{pop}}}}]$、$ {\bf{pop}}_0^G = [{{\boldsymbol{G}}_1},{{\boldsymbol{G}}_2},\cdots,{{\boldsymbol{G}}_{{\text{pop}}}}] $
(3)随机初始化两个子种群的代表解${{\boldsymbol{\varPhi}} _{{\text{best}}}}$、$ {{\boldsymbol{G}}_{{\text{best}}}} $
(4)根据式(17)计算两个子种群组合解的初始综合适应度，并将子种群中的个体根据综合适应度排序，即
$\begin{aligned} {\text{Fitnes}}{{\text{s}}_1}({\bf{pop}}_0^{\boldsymbol{\varPhi}} ;{{\boldsymbol{G}}_{{\text{best}}}}) =\;& [{\text{Fitness}}({{\boldsymbol{\varPhi}} _1},{{\boldsymbol{G}}_{{\text{best}}}}),\cdots,\\& {\text{Fitness}}({{\boldsymbol{\varPhi}} _{{\text{pop}}}},{{\boldsymbol{G}}_{{\text{best}}}})]\end{aligned}$;
$\begin{aligned} {\text{Fitnes}}{{\text{s}}_2}({{\boldsymbol{\varPhi}} _{{\text{best}}}};{\bf{pop}}_0^{\boldsymbol{G}}) = \;& [{\text{Fitness}}({{\boldsymbol{\varPhi}} _{{\text{best}}}},{{\boldsymbol{G}}_1}),\cdots,\\& {\text{Fitness}}({{\boldsymbol{\varPhi}} _{{\text{best}}}},{{\boldsymbol{G}}_{{\text{pop}}}})]\end{aligned}$
(5)for i = 1, 2, ···, inter do
(6) 　从${\bf{pop}}_{i - 1}^{\boldsymbol{\varPhi}} $和$ {\bf{pop}}_{i - 1}^{\boldsymbol{G}} $选择${\text{pop}} \times {\text{Selectrate}}$个体作为父代
(7) 　if rand< 交叉概率Crossrate
(8) 　　在两个子种群父代中随机选择个体进行染色体交叉；
(9) 　end if
(10) if rand< 突变概率Mutationrate
(11) 　在两个子种群的个体随机选择染色体进行突变；
(12) end if
(13) 得到两个子种群对应的子代种群${\bf{pop}}_i^{\boldsymbol{\varPhi}} $和$ {\bf{pop}}_i^{\boldsymbol{G}} $
(14) 利用式(17)计算组合解综合适应度：${\text{Fitnes}}{{\text{s}}_1}({\bf{pop}}_i^{\boldsymbol{\varPhi }};{{\boldsymbol{G}}_{{\text{best}}}})$ 　和${\text{Fitnes}}{{\text{s}}_2}({{\boldsymbol{\varPhi}} _{{\text{best}}}};{\bf{pop}}_i^{\boldsymbol{G}})$，并将得到的解按适应度降序排序；
(15) if max(${\text{Fitnes}}{{\text{s}}_1}({\bf{pop}}_i^\varPhi ;{{\boldsymbol{G}}_{{\text{best}}}})$)>${\text{Fitness}}({{\boldsymbol{\varPhi}} _{{\text{best}}}},{{\boldsymbol{G}}_{{\text{best}}}})$
(16) 　更新${{\boldsymbol{\varPhi }}_{{\text{best}}}}$；
(17) end if
(18) if max(${\text{Fitnes}}{{\text{s}}_2}({{\boldsymbol{\varPhi }}_{{\text{best}}}};{\bf{pop}}_i^{\boldsymbol{G}})$)>${\text{Fitness}}({{\boldsymbol{\varPhi}} _{{\text{best}}}},{{\boldsymbol{G}}_{{\text{best}}}})$
(19) 　更新$ {{\boldsymbol{G}}_{{\text{best}}}} $；
(20) end if
(21) 计算并更新代表解的适应度${\text{Fitness}}({{\boldsymbol{\varPhi}} _{{\text{best}}}},{{\boldsymbol{G}}_{{\text{best}}}})$
(22)end for
(23)得到$[{{\boldsymbol{\varPhi}} _{{\text{best}}}},{{\boldsymbol{G}}_{{\text{best}}}}]$作为联合波束成形优化解；

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表 1 系统参数

参数符号	参数值	参数符号	参数值	参数符号	参数值	参数符号	参数值
${P_{\max }}$	30 dBm	${\sigma ^2}$	–114 dBm	f	2.4 GHz	$\rho $	0.01
${\alpha _{{\mathrm{U}} - {\mathrm{R}}}}$	2.2	${\alpha _{{\mathrm{U}} - k}}$	3.5	${\alpha _{{\mathrm{R}} - k}}$	2.8	K	2
M	4	${L_x}$	4	${L_y}$	4	$\phi $	$0.43\pi $
${A_{\min }}$	0.2	$\alpha $	1.6	${K_1}$	10	${K_2}$	10

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表 2 CCEA算法参数

参数名	参数值	参数名	参数值
种群个体数量pop	20	选择率 Selectrate	0.2
交叉概率 Crossrate	0.6	突变率 Mutationrate	0.1
最大迭代次数 inter	1 000	k₁	0.5
c₁	60	c₂	1.0

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表 3 图5对应的位置参数及频谱效率对比

	UAV和用户位置参数	用户1方位 $[\theta _1^{{\text{azi}}},\theta _1^{{\text{ele}}}]$	用户2方位 $[\theta _2^{{\text{azi}}},\theta _2^{{\text{ele}}}]$	初始频谱效率 (bit/(s·Hz))	优化后的频谱效率 (bit/(s·Hz))
图5(a)	$ {{\boldsymbol{w}}_{\rm{U}}} = {(25\,{\text{m}},30\,{\text{m}},30\,{\text{m}})^{\mathrm{T}}} $ $ {{\mathbf{w}}_1} = {(47\,m,15\,m,0\,m)^{\mathrm{T}}} $ $ {{\boldsymbol{w}}_2} = {(25\,m,5\,m,0\,m)^{\mathrm{T}}} $	[59.04°, 64.99°]	[30.96°, 23.21°]	6.511 7	26.868 9
图5(b)	$ {w_{\rm{U}}} = {(25\,{\text{m}},15\,{\text{m}},30\,{\text{m}})^{\mathrm{T}}} $ $ {{\mathbf{w}}_1} = {(47\,m,15\,m,0\,m)^{\mathrm{T}}} $ $ {{\boldsymbol{w}}_2} = {(25\,m,5\,m,0\,m)^{\mathrm{T}}} $	[59.04°, 64.99°]	[30.96°, 23.21°]	3.320 4	28.713 6
图5(c)	$ {{\mathbf{w}}_{\rm{U}}} = {(25\,{\text{m}},30\,{\text{m}},30\,{\text{m}})^{\mathrm{T}}} $ $ {{\boldsymbol{w}}_1} = {(30\,m,15\,m,0\,m)^{\mathrm{T}}} $ $ {{\boldsymbol{w}}_2} = {(5\,m,5\,m,0\,m)^{\mathrm{T}}} $	[36.67°, 26.36°]	[6.30°, 15.47°]	6.234 8	27.642 8

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