一種基于合作協(xié)同進(jìn)化的智能超表面輔助無(wú)人機(jī)通信系統(tǒng)聯(lián)合波束成形方法

仲偉志; 萬(wàn)詩(shī)晴; 段洪濤; 范振雄; 林志鵬; 黃洋; 毛開(kāi)

doi:10.11999/JEIT240561

一種基于合作協(xié)同進(jìn)化的智能超表面輔助無(wú)人機(jī)通信系統(tǒng)聯(lián)合波束成形方法

doi: 10.11999/JEIT240561 cstr: 32379.14.JEIT240561

1.
南京航空航天大學(xué)電磁頻譜認(rèn)知?jiǎng)討B(tài)系統(tǒng)工業(yè)與信息化部重點(diǎn)實(shí)驗(yàn)室南京 211106
2.
國(guó)家無(wú)線電監(jiān)測(cè)中心北京 100144

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(62217250)，江蘇省重點(diǎn)研發(fā)計(jì)劃(產(chǎn)業(yè)前瞻與關(guān)鍵核心技術(shù))(BE2022067, BE2022067-1, BE2022067-3)，南京航空航天大學(xué)研究生科研與實(shí)踐創(chuàng)新計(jì)劃(xcxjh20231507)

詳細(xì)信息

作者簡(jiǎn)介:
仲偉志：女，副教授，研究方向?yàn)楦哳l通信、無(wú)人機(jī)通信、頻譜感知等

萬(wàn)詩(shī)晴：女，碩士生，研究方向?yàn)闊o(wú)人機(jī)通信、可重構(gòu)智能表面聯(lián)合波束賦形

段洪濤：男，正高級(jí)工程師，研究方向?yàn)闊o(wú)人機(jī)通信與反制，頻譜管理，短波及超短波監(jiān)測(cè)等

范振雄：男，高級(jí)工程師，研究方向?yàn)闊o(wú)人機(jī)通信與反制，超短波干擾定位及查找，短波測(cè)向等

林志鵬：男，副研究員，研究方向?yàn)楦呔S信道參數(shù)估計(jì)、大規(guī)模陣列信號(hào)處理、無(wú)人機(jī)通信、頻譜信號(hào)感知及重構(gòu)等

黃洋：男，副教授，研究方向?yàn)殡姶挪┺?、頻譜管控、物聯(lián)網(wǎng)技術(shù)、B5G及未來(lái)無(wú)線網(wǎng)絡(luò)等

毛開(kāi)：男，博士生，研究方向?yàn)樾诺罍y(cè)量與建模

通訊作者:
仲偉志　zhongwz@nuaa.edu.cn

中圖分類(lèi)號(hào): TN929
計(jì)量
- 文章訪問(wèn)數(shù): 462
- HTML全文瀏覽量: 160
- PDF下載量: 66
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-07-04
- 修回日期: 2024-11-07
- 網(wǎng)絡(luò)出版日期: 2024-11-13
- 刊出日期: 2025-02-28

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 Radio 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), The Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics (xcxjh20231507)

摘要

摘要: 針對(duì)傳統(tǒng)聯(lián)合波束成形方法在智能超表面(RIS)輔助無(wú)人機(jī)(UAV)通信系統(tǒng)優(yōu)化中存在的局限性，包括針對(duì)RIS僅考慮相移矩陣優(yōu)化、優(yōu)化方法缺乏應(yīng)用普適性等問(wèn)題，該文面向RIS輔助無(wú)人機(jī)通信服務(wù)多用戶(hù)場(chǎng)景，創(chuàng)新性提出一種基于合作協(xié)同進(jìn)化(CCEA)的聯(lián)合波束優(yōu)化方法。該方法利用兩個(gè)子種群的獨(dú)立進(jìn)化將聯(lián)合波束成形問(wèn)題分解成RIS反射波波束設(shè)計(jì)和發(fā)射端波束設(shè)計(jì)兩個(gè)子問(wèn)題進(jìn)行求解，通過(guò)進(jìn)化過(guò)程中的信息交互與協(xié)作來(lái)實(shí)現(xiàn)聯(lián)合波束成形設(shè)計(jì)。數(shù)值仿真結(jié)果表明，相較于僅考慮RIS相移矩陣設(shè)計(jì)的聯(lián)合波束優(yōu)化，CCEA通過(guò)設(shè)計(jì)RIS反射波波束形狀改變了反射波在3維空間中的能量分布，進(jìn)而提升了接收端信干噪比(SINR)和頻譜效率；此外，基于種群的CCEA算法能夠產(chǎn)生更加多樣的解，因此在UAV和用戶(hù)的不同位置設(shè)置下均能實(shí)現(xiàn)反射波對(duì)用戶(hù)方向的有效覆蓋，相對(duì)于傳統(tǒng)方法能夠避免局部最優(yōu)、具有更強(qiáng)的應(yīng)用普適性。
- 無(wú)人機(jī)通信 /
- 智能超表面 /
- 聯(lián)合波束成形 /
- 合作協(xié)同進(jìn)化
Abstract: Objective: High-quality wireless communication enabled by Unmanned Aerial Vehicles (UAVs) is set to play a crucial role in the future. In light of the limitations posed by traditional terrestrial communication networks, the deployment of UAVs as nodes within aerial access networks has become a vital component of emerging technologies in Beyond Fifth Generation (B5G) and Sixth Generation (6G) communication systems. However, the presence of infrastructure obstructions, such as trees and buildings, in complex urban environments can hinder the Line-of-Sight (LoS) link between UAVs and ground users, leading to a significant degradation in channel quality. To address this challenge, researchers have proposed the integration of Reconfigurable Intelligent Surfaces (RIS) into UAV communication systems, providing an energy-efficient and flexible passive beamforming solution. RIS consists of numerous adjustable electromagnetic units, with each element capable of independently configuring various phase shifts. By adjusting both the amplitude and phase of incoming signals, RIS can intelligently reflect signals from multiple transmission paths, thereby achieving directional signal enhancement or nulling through beamforming. Given the limitations of conventional joint beamforming methods—such as their exclusive focus on optimizing the RIS phase shift matrix and lack of universality—a novel joint beamforming approach based on a Cooperative Co-Evolutionary Algorithm (CCEA) is proposed. This method aims to enhance Spectrum Efficiency (SE) in multi-user scenarios involving RIS-assisted UAV communications. Methods: The proposed approach begins by optimizing the RIS phase shift matrix, followed by the design of the beam shape for RIS-reflected waves. This process modifies the spatial energy distribution of RIS reflections to improve the Signal-to-Interference-plus-Noise Ratio (SINR) at the receiver. To address challenges in existing optimization algorithms, an Evolutionary Algorithm (EA) is introduced for the first time, and a cooperative co-evolutionary structure based on EA is developed to decouple joint beamforming subproblems. The central concept of CCEA revolves around decomposing complex problems into several subproblems, which are then solved through distributed parallel evolution among subpopulations. The evaluation of individuals within each subpopulation, representing solutions to their respective subproblems, relies on collaboration among different populations. Specifically, this involves merging individuals from one subpopulation with representative individuals from others to create composite solutions. Subsequently, the overall fitness of these composite solutions is assessed to evaluate individual performance within each subpopulation. Results and Discussions: The simulation results demonstrate that, in comparison to joint beamforming, which focuses solely on designing the RIS phase shift matrix, further optimizing the shape of the reflected beam from the RIS significantly enhances the accuracy and effectiveness of the main lobe coverage over the user's position, resulting in improved SE. Although Maximum Ratio Transmission (MRT) precoding can maximize the output SINR of the desired signal, it may also lead to considerable inter-user interference, which subsequently diminishes the SE. Therefore, the implementation of joint beamforming is essential. The optimization algorithms proposed in this paper are effective for both the actual amplitude-phase shift model and the ideal RIS amplitude-phase shift model. However, factors such as dielectric loss associated with the actual circuit structure of the RIS can attenuate the strength of the reflected wave reaching the client, thereby reducing the SINR at the receiving end and ultimately lowering the SE. Additionally, the increase in SE achievable through Deep Reinforcement Learning (DRL) and Alternating Optimization (AO) is limited when compared to CCEA. Unlike the optimization of individual action strategies employed in DRL, the CCEA algorithm produces a greater variety of solutions by utilizing crossover and mutation among individuals within the population, thereby mitigating the risk of local optimization. Moreover, CCEA can optimize the spatial distribution of the reflected waves through a more sophisticated design of the RIS reflecting beam shape. This results in an enhanced signal intensity at the receiving end, allowing for a higher SE compared to AO and DRL, which primarily focus on optimizing the RIS phase shift matrix. Conclusions: In light of the limitations observed in previous joint beamforming optimization methods, this paper introduces a novel joint beamforming optimization approach based on CCEA. This method effectively decomposes the joint beam optimization problem into two distinct sub-problems: the design of the RIS reflection beam waveform and the beamforming design at the transmitter. These sub-problems are addressed through independent parallel evolution, utilizing two separate sub-populations. Notably, for RIS passive beamforming, this approach innovatively optimizes the RIS phase shift matrix alongside the design of the RIS reflected beam shape for the first time. Numerical simulation results indicate that, compared to joint beamforming strategies that focus solely on optimizing the RIS phase shift matrix, a more meticulous design of the RIS reflected waveform can significantly alter the intensity distribution of reflected waves in 3D space. This alignment enables the reflected beam to converge on the user’s location while mitigating interference, thereby enhancing the system’s SE. Furthermore, the CCEA algorithm demonstrates the capability to achieve effective coverage of RIS reflected beams for users, regardless of varying base station and user locations. The optimization process leads to a reduction in Peak Side Lobe Level (PSLL) and an improvement in SE by at least 5 dB, showing its spatial applicability across diverse scenarios. Future research will aim to further investigate the application of evolutionary algorithms and swarm intelligence optimization techniques in joint beamforming optimization, as well as explore the potential of RIS beam waveform design to optimize communication systems, adapting to increasingly complex and diversified communication requirements.
- Unmanned Aerial Vehicle (UAV) communication /
- Reconfigurable Intelligent Surface (RIS) /
- Joint beamforming /
- Cooperative Co-Evolutionary Algorithm (CCEA)

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圖 1 RIS輔助無(wú)人機(jī)MU-MISO通信系統(tǒng)

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圖 2 CCEA性能比較

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圖 3 不同UAV、用戶(hù)位置下得到的RIS反射波波束方向圖

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圖 4 不同RIS陣元設(shè)置下系統(tǒng)頻譜效率

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1 基于CCEA的RIS輔助無(wú)人機(jī)通信聯(lián)合波束成形優(yōu)化算法

(1)輸入初始位置信息${{\boldsymbol{w}}_{\rm{U}}},{{\boldsymbol{w}}_{\rm{R}}},{{\boldsymbol{w}}_k}$和其他基本系統(tǒng)參數(shù)；獲得　$ {{\boldsymbol{H}}_{{\mathrm{U}} {\text{-}}{\mathrm{ R}}}} $, $ {{\boldsymbol{h}}_{{\mathrm{R}}{\text{-}} k}} $, ${{\boldsymbol{h}}_{{\mathrm{U}} {\text{-}} k}}$
(2)根據(jù)式(16)及式(9)生成初始子種群：　${\bf{pop}}_0^{\boldsymbol{\varPhi}} = [{{\boldsymbol{\varPhi}} _1},{{\boldsymbol{\varPhi}} _2},\cdots,{{\boldsymbol{\varPhi}} _{{\text{pop}}}}]$, $ {\bf{pop}}_0^{\boldsymbol{G}} = [{{\boldsymbol{G}}_1},{{\boldsymbol{G}}_2},\cdots,{{\boldsymbol{G}}_{{\text{pop}}}}] $
(3)隨機(jī)初始化兩個(gè)子種群的代表解${{\boldsymbol{\varPhi}} _{{\text{best}}}}$, $ {{\boldsymbol{G}}_{{\text{best}}}} $
(4)根據(jù)式(17)計(jì)算兩個(gè)子種群組合解的初始綜合適應(yīng)度，并將子種群中的個(gè)體根據(jù)綜合適應(yīng)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) 　從${\bf{pop}}_{i - 1}^{\boldsymbol{\varPhi}} $和$ {\bf{pop}}_{i - 1}^{\boldsymbol{G}} $選擇${\text{pop}} \times {\text{Selectrate}}$個(gè)體作為父代
(7) 　if rand< 交叉概率Crossrate
(8) 　　在兩個(gè)子種群父代中隨機(jī)選擇個(gè)體進(jìn)行染色體交叉；
(9) 　end if
(10) if rand< 突變概率Mutationrate
(11) 　在兩個(gè)子種群的個(gè)體隨機(jī)選擇染色體進(jìn)行突變；
(12) end if
(13) 得到兩個(gè)子種群對(duì)應(yīng)的子代種群${\bf{pop}}_i^{\boldsymbol{\varPhi}} $和$ {\bf{pop}}_i^{\boldsymbol{G}} $
(14) 利用式(17)計(jì)算組合解綜合適應(yīng)度：${\text{Fitnes}}{{\text{s}}_1}({\bf{pop}}_i^{\boldsymbol{\varPhi }};{{\boldsymbol{G}}_{{\text{best}}}})$ 　和${\text{Fitnes}}{{\text{s}}_2}({{\boldsymbol{\varPhi}} _{{\text{best}}}};{\bf{pop}}_i^{\boldsymbol{G}})$，并將得到的解按適應(yīng)度降序排序；
(15) if max(${\text{Fitnes}}{{\text{s}}_1}({\bf{pop}}_i^{\boldsymbol{\varPhi}} ;{{\boldsymbol{G}}_{{\text{best}}}})$)>${\text{Fitness}}({{\boldsymbol{\varPhi}} _{{\text{best}}}},{{\boldsymbol{G}}_{{\text{best}}}})$
(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}}}} $；
(20) end if
(21) 計(jì)算并更新代表解的適應(yīng)度${\text{Fitness}}({{\boldsymbol{\varPhi}} _{{\text{best}}}},{{\boldsymbol{G}}_{{\text{best}}}})$
(22)end for
(23)得到$[{{\boldsymbol{\varPhi}} _{{\text{best}}}},{{\boldsymbol{G}}_{{\text{best}}}}]$作為聯(lián)合波束成形優(yōu)化解；

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表 1 系統(tǒng)參數(shù)

參數(shù)符號(hào)	參數(shù)值	參數(shù)符號(hào)	參數(shù)值	參數(shù)符號(hào)	參數(shù)值	參數(shù)符號(hào)	參數(shù)值
${P_{\max }}$	30 dBm	${\sigma ^2}$	–114 dBm	f	2.4 GHz	$\rho $	0.01
${\alpha _{{\mathrm{U}} {\text{-}} {\mathrm{R}}}}$	2.2	${\alpha _{{\mathrm{U}} {\text{-}} k}}$	3.5	${\alpha _{{\mathrm{R}} {\text{-}} k}}$	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算法參數(shù)

參數(shù)名	參數(shù)值	參數(shù)名	參數(shù)值
種群個(gè)體數(shù)量pop	20	選擇率 Selectrate	0.2
交叉概率 Crossrate	0.6	突變率 Mutationrate	0.1
最大迭代次數(shù) inter	1 000	k₁	0.5
c₁	60	c₂	1.0

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表 3 圖3對(duì)應(yīng)的位置參數(shù)及頻譜效率對(duì)比

	UAV和用戶(hù)位置參數(shù)	用戶(hù)1方位 $[\theta _1^{{\text{azi}}},\theta _1^{{\text{ele}}}]$	用戶(hù)2方位 $[\theta _2^{{\text{azi}}},\theta _2^{{\text{ele}}}]$	初始頻譜效率 (bit/(s·Hz))	優(yōu)化后的頻譜效率 (bit/(s·Hz))
圖3(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
圖3(b)	$ {{\boldsymbol{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
圖3(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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