空中可重構(gòu)智能面輔助車輛通信信道建模研究

潘旭婷; 石旺旗; 熊柏蘋; 郭道省; 江浩

doi:10.11999/JEIT240874

空中可重構(gòu)智能面輔助車輛通信信道建模研究

doi: 10.11999/JEIT240874 cstr: 32379.14.JEIT240874

潘旭婷¹,
石旺旗¹,
熊柏蘋²,
郭道省³,
江浩^{1, 2, ,}

1.
南京信息工程大學(xué)人工智能學(xué)院南京 210044
2.
東南大學(xué)移動通信國家重點實驗室南京 210096
3.
陸軍工程大學(xué)通信工程學(xué)院南京 210007

基金項目: 國家自然科學(xué)基金(62471238, U22A2002)，江西省科技創(chuàng)新基地資助計劃(20242BCC32016)

詳細(xì)信息

作者簡介:
潘旭婷：女，博士生，研究方向為RIS無線通信信道建模與仿真等

石旺旗：男，碩士生，研究方向為RIS無線通信信道建模與仿真等

熊柏蘋：男，博士生，研究方向為RIS無線信道建模與仿真、無人機(jī)通信等

郭道省：男，教授，研究方向為衛(wèi)星通信、通信抗干擾、空天地一體化網(wǎng)絡(luò)、物理層安全、無人機(jī)通信等

江浩：男，副教授，研究方向為RIS無線信道建模與仿真、近場通信，高能效通信等

通訊作者:
江浩　jianghao@nuist.edu.com

中圖分類號: TN929.5
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- 文章訪問數(shù): 358
- HTML全文瀏覽量: 139
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-10-16
- 修回日期: 2025-01-04
- 網(wǎng)絡(luò)出版日期: 2025-01-17
- 刊出日期: 2025-03-01

Research on Channel Modeling for Aerial Reconfigurable Intelligent Surfaces-assisted Vehicle Communications

1.
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
3.
College of Communication Engineering, Army Engineering University, Nanjing 210007, China

Funds: The National Natural Science Foundation of China (62471238, U22A2002), Jiangxi Province Science and Technology Development Programme (20242BCC32016)

摘要

摘要: 可重構(gòu)智能表面(RIS)能夠調(diào)控入射電磁波以優(yōu)化通信系統(tǒng)性能，是第6代(6G)無線通信技術(shù)的關(guān)鍵創(chuàng)新。將可重構(gòu)智能表面部署于無人機(jī)(UAV)上，借助無人機(jī)的靈活運(yùn)動軌跡和按需部署特性，可以有效解決因樹木和建筑等障礙物遮擋所引起的信息傳輸效率下降的問題。針對空中可重構(gòu)智能表面輔助的車對車(V2V)通信場景，該文提出了一種基于幾何的3維信道模型，該模型綜合考慮了無人機(jī)在3個自由度下的旋轉(zhuǎn)和任意軌跡移動，以及無人姿態(tài)變化對于信道模型的影響，引入了時變空間相位。此外，還考慮了發(fā)射端、接收端和無人機(jī)的實時運(yùn)動速度和方向，給出了復(fù)信道脈沖響應(yīng)(CIRs)的表達(dá)式，并對空域互相關(guān)函數(shù)(CCFs)、時域自相關(guān)函數(shù)(ACFs)和信道容量等關(guān)鍵信道統(tǒng)計特性進(jìn)行了詳細(xì)分析。仿真結(jié)果表明，所提信道模型能夠準(zhǔn)確捕獲信道特性，為未來可重構(gòu)智能面輔助無線通信的系統(tǒng)設(shè)計和優(yōu)化提供了有價值的理論參考。
- 車對車通信 /
- 可重構(gòu)智能表面 /
- 無人機(jī) /
- 信道模型
Abstract: Objective The Internet of Vehicles (IoV) is a global innovation focus, enabling ubiquitous interconnection among vehicles, roads, and people, thereby reducing traffic congestion and improving traffic safety. Vehicle-to-Vehicle (V2V) communication represents one of the most prominent application scenarios in IoV. This study addresses the reduced efficiency of V2V communication caused by environmental obstacles such as buildings and trees. It proposes the deployment of Reconfigurable Intelligent Surfaces (RIS) on Unmanned Aerial Vehicles (UAVs), leveraging their high mobility and on-demand deployment capability to enhance V2V communication under 6G networks. The model improves communication link quality and stability by utilizing the reflective properties of aerial RIS to mitigate signal attenuation and interference. This research develops a geometry-based Three-Dimensional (3D) dynamic channel model that incorporates the effects of UAV rotation, trajectory movement, and attitude changes on channel characteristics, enabling adaptation to dynamic and non-stationary communication scenarios. The findings provide a theoretical foundation for designing and optimizing RIS-assisted wireless communication systems through statistical analyses in the temporal, spatial, and frequency domains. Methods RIS can regulate incident electromagnetic waves to optimize communication system performance and are regarded as a crucial innovation in Sixth Generation (6G) wireless communication technology. Deploying RIS on UAVs effectively addresses reduced information transmission efficiency caused by obstacles such as trees and buildings, leveraging UAVs’ flexible trajectories and on-demand deployment capabilities. This study proposes a geometry-based 3D dynamic channel model, considering the UAV’s trajectory, three degrees of rotational freedom (pitch, yaw, and roll angles), and attitude changes. Channel propagation components are divided into aerial RIS array components and Non-Line-of-Sight (NLoS) components. Each RIS unit is modeled as an independent reflector capable of altering the propagation path by adjusting its phase and amplitude. The model incorporates time-varying spatial phases and Doppler frequency shifts, capturing the characteristics of dynamic propagation environments. Mathematical expressions for the Complex Impulse Responses (CIRs) are derived, along with analytical formulas for spatial Cross-Correlation Functions (CCFs), temporal Auto-Correlation Functions (ACFs), Frequency Correlation Functions (FCFs), and channel capacity. Various V2V communication scenarios are simulated by adjusting the velocity, direction, and acceleration of transmitters, receivers, and UAVs. Numerical simulations validate the proposed model’s effectiveness by defining four UAV trajectories and various vehicle motion states. Additionally, the temporal, spatial, and frequency correlation characteristics under different motion states are investigated. Finally, the effects of RIS physical attributes, such as the number and size of units, and UAV altitude on channel capacity are analyzed, along with dynamic variations in the power delay profile. Results and Discussions Simulation results demonstrate that the proposed channel model accurately captures channel characteristics. Specifically, the model presents various UAV flight trajectories (Fig. 5) and analyzes the temporal autocorrelation properties under different motion states of the transmitter and receiver (Fig. 6). It is observed that the temporal correlation exhibits significant non-stationarity across different motion states. However, the introduction of RIS significantly mitigates the decline in correlation. The model also compares the temporal autocorrelation properties corresponding to different UAV flight attitudes and altitudes (Fig. 7, Fig. 9). It is found that as the UAV’s initial altitude increases, multipath effects decrease, and the rate of decline in temporal autocorrelation function values gradually slows. Subsequently, the spatial cross-correlation of the proposed channel model is investigated for different propagation paths, revealing an increase in correlation with the Rician factor (Fig. 8). The frequency correlation function values are also examined under varying distances between the transmitter and receiver (Fig. 10), showing that while the correlation declines, it gradually stabilizes as the frequency interval increases. Finally, the impact of the RIS’s physical properties on channel capacity and the power delay profile is studied (Fig. 11, Fig. 12). It is observed that increasing the size and number of RIS array elements enhances channel capacity. Additionally, as delay increases, the power exhibits multiple smaller peaks before gradually decaying. These findings provide a valuable theoretical foundation for the future design and optimization of RIS-assisted wireless communication systems. Conclusions This paper presents a geometry-based 3D non-stationary channel model for V2V communications, innovatively incorporating aerial RIS implemented by UAVs equipped with RIS. The model accounts for the time-varying motion trajectories of ground vehicle terminals and UAVs, as well as the fading effects due to UAV attitude variations. Analytical expressions for spatiotemporal-frequency correlation functions and channel capacity are derived from the proposed model, ensuring the accuracy of channel transmission characteristics. By adjusting the model’s parameter configurations, it can accurately characterize the effects of various motion trajectories, dynamic states, UAV flight altitudes, and rotational angles on channel properties. These findings provide valuable insights for the design and performance analysis of RIS-assisted V2V communication systems.
- Vehicle-to-Vehicle(V2V) communications /
- Reconfigurable Intelligent Surface (RIS) /
- Unmanned Aerial Vehicle(UAV) /
- Channel model

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圖 1 空中智能超表面輔助V2V無線傳輸示意

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圖 2 無人機(jī)3維旋轉(zhuǎn)角度的解釋

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圖 3 發(fā)射端和接收端的傳播鏈路

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圖 4 無人機(jī)姿態(tài)變化對于空中RIS陣列分量信號傳輸路徑的影響

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圖 5 無人機(jī)搭載RIS的4種運(yùn)動軌跡

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圖 6 空中智能反射面使能V2V信道模型在車輛不同運(yùn)動狀態(tài)下的時間自相關(guān)特性

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圖 7 空中智能反射面使能V2V信道模型在不同t時刻無人機(jī)飛行姿態(tài)下的時間自相關(guān)性

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圖 8 空中智能反射面使能V2V通信信道模型在不同K值的空間互相關(guān)特性

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圖 9 空中智能反射面使能V2V通信信道模型在不同無人機(jī)高度下的時域自相關(guān)特性

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圖 10 空中智能反射面使能V2V通信信道模型在不同發(fā)射機(jī)和接收機(jī)距離下的頻率相關(guān)特性

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圖 11 空中智能反射面使能V2V通信信道在不同RIS陣列單元數(shù)量和尺寸下對應(yīng)的信道容量

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圖 12 空中智能反射面使能V2V信道模型在不同t時刻的功率延遲分布

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參考文獻(xiàn)(16)

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