Research on RIS Assisted Vehicle Communication Method Based on Deep Learning

Hua Tan; Chenguang He; Dezhi Li

doi:10.1007/978-3-031-86196-3_26

Research on RIS Assisted Vehicle Communication Method Based on Deep Learning

Hua Tan
, Chenguang He^*
, Dezhi Li

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings
Editors	Hsiao-Hwa Chen, Weixiao Meng
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	301-311
Number of pages	11
ISBN (Print)	9783031861956
DOIs	https://doi.org/10.1007/978-3-031-86196-3_26
State	Published - 2025
Externally published	Yes
Event	14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024 - Harbin, China Duration: 23 Aug 2024 → 25 Aug 2024

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	605 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024
Country/Territory	China
City	Harbin
Period	23/08/24 → 25/08/24

Keywords

deep learning graph neural network architecture
reconfigurable intelligent surface
vehicle communication

Access to Document

10.1007/978-3-031-86196-3_26

Cite this

Tan, H., He, C., & Li, D. (2025). Research on RIS Assisted Vehicle Communication Method Based on Deep Learning. In H.-H. Chen, & W. Meng (Eds.), Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings (pp. 301-311). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 605 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-86196-3_26

Tan, Hua ; He, Chenguang ; Li, Dezhi. / Research on RIS Assisted Vehicle Communication Method Based on Deep Learning. Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings. editor / Hsiao-Hwa Chen ; Weixiao Meng. Springer Science and Business Media Deutschland GmbH, 2025. pp. 301-311 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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title = "Research on RIS Assisted Vehicle Communication Method Based on Deep Learning",

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.",

keywords = "deep learning graph neural network architecture, reconfigurable intelligent surface, vehicle communication",

author = "Hua Tan and Chenguang He and Dezhi Li",

note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.; 14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024 ; Conference date: 23-08-2024 Through 25-08-2024",

year = "2025",

doi = "10.1007/978-3-031-86196-3\_26",

language = "英语",

isbn = "9783031861956",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "301--311",

editor = "Hsiao-Hwa Chen and Weixiao Meng",

booktitle = "Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings",

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Tan, H, He, C & Li, D 2025, Research on RIS Assisted Vehicle Communication Method Based on Deep Learning. in H-H Chen & W Meng (eds), Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 605 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 301-311, 14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024, Harbin, China, 23/08/24. https://doi.org/10.1007/978-3-031-86196-3_26

Research on RIS Assisted Vehicle Communication Method Based on Deep Learning. / Tan, Hua; He, Chenguang; Li, Dezhi.
Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings. ed. / Hsiao-Hwa Chen; Weixiao Meng. Springer Science and Business Media Deutschland GmbH, 2025. p. 301-311 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 605 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Publisher Copyright: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

PY - 2025

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N2 - 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.

AB - 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.

KW - deep learning graph neural network architecture

KW - reconfigurable intelligent surface

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BT - Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings

A2 - Chen, Hsiao-Hwa

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T2 - 14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

Y2 - 23 August 2024 through 25 August 2024

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Tan H, He C, Li D. Research on RIS Assisted Vehicle Communication Method Based on Deep Learning. In Chen HH, Meng W, editors, Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 301-311. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-031-86196-3_26