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IEEE Transactions on Intelligent Transportation Systems : a Publication of the IEEE Intelligent Transportation Systems Council
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Summary
This summary is machine-generated.

This study introduces a reinforcement learning (RL) method for beam tracking in 5G vehicle-to-everything (V2X) networks. The approach enhances tracking accuracy and data rates for vehicle-to-infrastructure (V2I) communications, even with high vehicle speeds.

Keywords:
V2IVehicular networksbeam trackingreinforcement learning

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Area of Science:

  • Wireless Communications
  • Network Engineering
  • Artificial Intelligence

Background:

  • Beam tracking is critical for 5G vehicle-to-everything (V2X) networks, especially for vehicle-to-infrastructure (V2I) communications at higher frequencies (e.g., 5G FR2).
  • Challenges include path loss, shorter time slots, high vehicle velocities, and localization errors, creating a trade-off between beam tracking accuracy and data rate.
  • Existing methods struggle to maintain performance within the stringent time constraints of high-frequency V2I communications.

Purpose of the Study:

  • To develop an efficient beam tracking method for 5G V2X networks that balances tracking accuracy and data rate.
  • To address the challenges of high-frequency V2I communications, including short time slots and vehicle dynamics.
  • To improve the temporal efficiency of beam tracking within the constraints of 5G FR2 communications.

Main Methods:

  • Proposed a reinforcement learning (RL) assisted, high-resolution codebook-based beam tracking method.
  • Evaluated and selected the twin delayed deep deterministic policy gradient (TD3) framework for its efficiency in determining beam patterns.
  • Integrated recurrent neural networks (RNNs), informed by Hurst exponent analysis, to enhance RL framework performance.

Main Results:

  • The proposed RL-assisted method effectively determines proper beam patterns within short durations.
  • Demonstrated significant improvements in beam tracking accuracy compared to conventional approaches.
  • Achieved enhanced data rates and superior temporal efficiency for V2I communications.

Conclusions:

  • The TD3-based RL framework, enhanced with RNNs, provides a robust solution for beam tracking in 5G V2X.
  • The method successfully navigates the trade-off between tracking accuracy and data rate in high-frequency V2I scenarios.
  • The proposed approach offers a promising direction for optimizing V2I communication performance in dynamic vehicular environments.