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

PAN Xuting; SHI Wangqi; XIONG Baiping; GUO Daoxing; JIANG Hao

doi:10.11999/JEIT240874

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PAN Xuting, SHI Wangqi, XIONG Baiping, GUO Daoxing, JIANG Hao. Research on Channel Modeling for Aerial Reconfigurable Intelligent Surfaces-assisted Vehicle Communications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240874

Citation:

PAN Xuting, SHI Wangqi, XIONG Baiping, GUO Daoxing, JIANG Hao. Research on Channel Modeling for Aerial Reconfigurable Intelligent Surfaces-assisted Vehicle Communications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240874

Citation:

PAN Xuting, SHI Wangqi, XIONG Baiping, GUO Daoxing, JIANG Hao. Research on Channel Modeling for Aerial Reconfigurable Intelligent Surfaces-assisted Vehicle Communications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240874

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

doi: 10.11999/JEIT240874

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)

Received Date: 2024-10-16
Rev Recd Date: 2025-01-04

Available Online: 2025-01-17

Abstract

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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References(16)

References

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