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This study introduces an adaptive beamforming scheme to enhance urban vehicle-to-vehicle (V2V) communications at intersections. The neuroevolution-based approach improves communication range and response time, crucial for road safety applications.

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Area of Science:

  • Vehicular communication systems
  • Wireless networking
  • Intelligent transportation systems

Background:

  • Urban V2V communication is hindered by obstacles causing signal shadowing.
  • Reduced communication range and performance impact road safety applications.
  • Environmental factors like vehicle size and weather further complicate V2V links.

Purpose of the Study:

  • To enhance urban V2V communication at intersections.
  • To mitigate shadowing effects using an adaptive transmission scheme.
  • To improve the reliability of road safety applications.

Main Methods:

  • Proposed a neuroevolution of augmenting topologies (NEAT)-based adaptive beamforming scheme.
  • Controlled antenna array radiation patterns to adapt to the urban environment.
  • Utilized the IEEE 802.11p standard for the physical layer.

Main Results:

  • The NEAT-based adaptive beamforming scheme significantly improved communication range.
  • The proposed method demonstrated a reduced response time compared to baseline.
  • Outperformed isotropic antennas and traditional machine learning methods like genetic algorithms and particle swarm optimization.

Conclusions:

  • The adaptive beamforming scheme effectively mitigates shadowing in urban V2V intersection scenarios.
  • This approach enhances the performance of critical road safety applications.
  • NEAT offers a promising solution for robust vehicular communication systems.