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Handover for V2V communication in 5G using convolutional neural networks.

Sarah M Alhammad1, Doaa Sami Khafaga1, Mahmoud M Elsayed2

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

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|August 22, 2024
PubMed
Summary

This study uses deep learning (DL) with 5G networks for vehicle detection and obstacle identification, achieving 97% accuracy. A novel handover prediction method enhances performance in heterogeneous networks.

Keywords:
Connected autonomous vehicles (CAVs)Convolutional neural networks (CNN)Deep learning (DL)Received signal strength indicator (RSSI)

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Area of Science:

  • Telecommunications Engineering
  • Artificial Intelligence
  • Computer Vision

Background:

  • Vehicle communication is crucial for traffic flow and safety.
  • 5G technology offers high data rates and quality of service for advanced transportation systems.
  • Deep learning (DL) excels at processing large datasets for feature identification.

Purpose of the Study:

  • To detect vehicles and identify obstacles using DL in 5G environments.
  • To develop a novel horizontal handover prediction method for heterogeneous networks.
  • To improve vehicle communication efficiency and safety.

Main Methods:

  • Utilized the VGG19 deep learning model via transfer learning for vehicle and obstacle detection.
  • Proposed a new horizontal handover prediction algorithm based on channel characteristics.
  • Implemented and evaluated algorithms within simulated 5G network environments.

Main Results:

  • Achieved a 97% success rate in identifying vehicles.
  • Successfully predicted the next station for horizontal handover.
  • Demonstrated the effectiveness of DL algorithms in 5G vehicle communication.

Conclusions:

  • The proposed DL-based vehicle detection and handover prediction methods are effective in 5G environments.
  • The VGG19 model and novel handover algorithm show high accuracy and reliability.
  • This research contributes to safer and more efficient intelligent transportation systems.