Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System

ZHONG Weizhi; HE Yi; DUAN Hongtao; WAN Shiqing; FAN Zhenxiong; ZHU Qiuming; LIN Zhipeng

doi:10.11999/JEIT231324

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ZHONG Weizhi, HE Yi, DUAN Hongtao, WAN Shiqing, FAN Zhenxiong, ZHU Qiuming, LIN Zhipeng. Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231324

Citation:

ZHONG Weizhi, HE Yi, DUAN Hongtao, WAN Shiqing, FAN Zhenxiong, ZHU Qiuming, LIN Zhipeng. Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231324

Citation:

ZHONG Weizhi, HE Yi, DUAN Hongtao, WAN Shiqing, FAN Zhenxiong, ZHU Qiuming, LIN Zhipeng. Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231324

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Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System

doi: 10.11999/JEIT231324

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 Primary Research and Development Plan of Jiangsu Province (Industry foresight and key core technology)(BE2022067, BE2022067-1, BE2022067-3), The National Natural Science Foundation of China (62271250), Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics (xcxjh20231507)

Received Date: 2023-11-30
Rev Recd Date: 2024-06-14

Available Online: 2024-06-21

Abstract

Abstract

In order to address the limitations of the joint beamforming method based on channel prior knowledge, which is constrained by multivariate Vehicle-to-Infrastructure (V2I) communication scenes and suffers from large overhead caused by channel estimation, a wireless propagation link prediction-based joint beamforming method assisted by environmental situation awareness is proposed in this paper. Firstly, a model of Reconfigurable Intelligent Surface (RIS) assisted mmWave communication system for V2I networks is established using a ray tracer. To build a dataset, diverse data of wireless propagation links is obtained by changing the environmental situation. Then, this dataset is used to train a machine learning-based wireless propagation link prediction model. Finally, the joint beamforming problem under the constraint of maximum transmission power is modeled. Additionally, based on the prediction outcome, the beamforming matrix of base station and the phase shift matrix of RIS are optimized using Alternating Iterative Optimization Algorithm (AIOA) to maximize the minimum Signal to Interference plus Noise Ratio (SINR) among synchronous communication vehicle users. Simulation results validate the effectiveness of the proposed method. Introducing non-channel prior knowledge driven reduces channel detection overhead and improves feasibility in applying the proposed method to V2I scenes.

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References(21)

References

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