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. The authors 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.

• Record URL:
  • https://doi.org/10.1109/TITS.2024.3438875
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Zhong, Weizhi
  • Zhang, Lulu
  • Jin, Haowen
  • Liu, Xin
  • Zhu, Qiuming
  • He, Yi
  • Ali, Farman
  • Lin, Zhipeng
  • 0009-0000-0284-7692
  • Mao, Kai
  • Durrani, Tariq S
• Publication Date: 2024-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 19110-19116
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 11
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Computer memory; Deep learning; Graphs; Millimeter wave communication systems; Vehicle to infrastructure communications
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01952555
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 17 2025 4:55PM