Research Article
Research on RIS Assisted Vehicle Communication Method Based on Deep Learning
@INPROCEEDINGS{10.1007/978-3-031-86196-3_26, author={Hua Tan and Chenguang He and Dezhi Li}, title={Research on RIS Assisted Vehicle Communication Method Based on Deep Learning}, proceedings={Wireless and Satellite Systems. 14th EAI International Conference, WiSATS 2024, Harbin, China, August 23--25, 2024, Proceedings, Part I}, proceedings_a={WISATS}, year={2025}, month={3}, keywords={reconfigurable intelligent surface deep learning graph neural network architecture vehicle communication}, doi={10.1007/978-3-031-86196-3_26} }- Hua Tan
Chenguang He
Dezhi Li
Year: 2025
Research on RIS Assisted Vehicle Communication Method Based on Deep Learning
WISATS
Springer
DOI: 10.1007/978-3-031-86196-3_26
Abstract
This thesis proposes an innovative system framework for vehicular communication utilizing Reconfigurable Intelligent Surfaces (RIS) to support millimeter-wave (mmWave) scenarios, addressing the high transmission rate demands of 6G communication. Due to significant path loss in mmWave propagation, RIS is introduced to enhance coverage and communication rates. Additionally, the thesis employs a deep learning-based Graph Neural Network (GNN) algorithm to optimize beamforming at the base station and phase shift matrices at the RIS, bypassing complex channel estimation processes. Simulation results demonstrate that the proposed algorithm exhibits excellent performance and generalization capabilities, enabling rapid response in vehicular communication scenarios.
