Image-Based Beam Tracking With Deep Learning for mmWave V2I Communication Systems

Effective beam alignment is essential for vehicle-to-infrastructure (V2I) millimeter wave (mmWave) communication systems, particularly in high-mobility vehicle scenarios. This paper explores a three-dimensional (3D) vehicle environment and introduces a novel deep learning (DL)-based beam search method that incorporates an image-based coding (IBC) technique. The mmWave beam search is approached as an image processing problem based on situational awareness. We propose IBC to leverage the locations, sizes, and information of vehicles, and utilize convolutional neural network (CNN) to train the image dataset. Consequently, the optimal beam pair index(BPI)can be determined. Simulation results demonstrate that the proposed beam search method achieves satisfactory performance in terms of accuracy and robustness compared to conventional methods.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2024-09-26
DOI
https://doi.org/10.1109/tits.2024.3438875
Citations
6
Primary Topic
Millimeter-Wave Propagation and Modeling
Type
article
Field-Weighted Citation Impact
1.25

Funders

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article

Image-Based Beam Tracking With Deep Learning for mmWave V2I Communication Systems

T.S. Durrani, Kai Mao, Farman Ali, Qiuming Zhu et al.
6 citations
IEEE Transactions on Intelligent Transportation Systems
Millimeter-Wave Propagation and Modeling
1.25
article

Image-Based Beam Tracking With Deep Learning for mmWave V2I Communication Systems

T.S. Durrani, Kai Mao, Farman Ali, Qiuming Zhu, Weizhi Zhong, Xin Liu, Lulu Zhang, Yi He, Zhipeng Lin, Haowen Jin
article en
6 citations

Abstract

Effective beam alignment is essential for vehicle-to-infrastructure (V2I) millimeter wave (mmWave) communication systems, particularly in high-mobility vehicle scenarios. This paper explores a three-dimensional (3D) vehicle environment and introduces a novel deep learning (DL)-based beam search method that incorporates an image-based coding (IBC) technique. The mmWave beam search is approached as an image processing problem based on situational awareness. We propose IBC to leverage the locations, sizes, and information of vehicles, and utilize convolutional neural network (CNN) to train the image dataset. Consequently, the optimal beam pair index(BPI)can be determined. Simulation results demonstrate that the proposed beam search method achieves satisfactory performance in terms of accuracy and robustness compared to conventional methods.

IEEE Transactions on Intelligent Transportation SystemsVol. 25(11)
National Academy of Engineering (US), University of Strathclyde (GB), Dalian University of Technology (CN), Qurtuba University of Science and Information Technology (PK), Nanjing University of Aeronautics and Astronautics (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province
Openalex Percentile: Top 20%
Millimeter-Wave Propagation and Modeling
1.25
DOISourceOpenAlex
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