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This study introduces a new channel estimation method for vehicle-to-everything (V2X) systems using sparse Bayes tensor and direction of arrival (DOA) tracking. It enhances accuracy in high-mobility scenarios, crucial for autonomous driving and traffic safety.

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channel estimationdirection of arrival (DOA) trackingmmWave massive MIMOvehicle to everything (V2X)

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Mobile Computing

Background:

  • Vehicle-to-everything (V2X) communications are vital for traffic safety, autonomous driving, and environmental impact reduction.
  • Accurate channel estimation is essential for V2X systems, especially in high-mobility scenarios.
  • Existing static channel estimation algorithms are inadequate for the rapidly time-varying channels in V2X millimeter-wave massive MIMO systems due to high vehicle mobility.

Purpose of the Study:

  • To propose a novel channel estimation method for V2X millimeter-wave massive MIMO systems that addresses the challenges of high mobility.
  • To enhance the accuracy and reliability of channel estimation in fast time-varying vehicular environments.
  • To improve the performance of V2X communication systems for applications like autonomous driving and traffic safety.

Main Methods:

  • Developed a sparse Bayes tensor and direction of arrival (DOA) tracking inspired channel estimation approach.
  • Transformed channel estimation into a sparse recovery problem by exploiting sparse scattering characteristics.
  • Incorporated super-resolution angle grids and a measurement matrix to mitigate quantization errors and increase dictionary resolution.
  • Utilized the low-rank characteristics of received signals alongside traditional sparse priors.
  • Implemented a DOA tracking method based on sparse Bayes tensor to estimate DOA at subsequent moments for tracking fast time-varying channels.

Main Results:

  • The proposed method effectively handles the rapid time-varying nature of wireless channels in high-mobility V2X scenarios.
  • Simulation results demonstrate superior performance compared to existing state-of-the-art methods across various vehicle speeds.
  • The approach provides significant angle information updates, crucial for tracking fast time-varying vehicular channels.

Conclusions:

  • The proposed sparse Bayes tensor and DOA tracking inspired channel estimation is a highly effective solution for V2X millimeter-wave massive MIMO systems.
  • This method significantly improves channel estimation accuracy and tracking capabilities in dynamic vehicular environments.
  • The findings validate the theoretical analysis and highlight the potential of the proposed approach to advance V2X communication technologies.