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Summary

This study introduces a Double Deep Q-learning algorithm for optimizing vehicle-to-infrastructure (V2I) communication in 5G networks. The new method enhances the reliability and accuracy of transmitting crucial road safety information for autonomous driving.

Keywords:
5GDDQLNR-V2Iautomatic drivingultra-reliable

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Area of Science:

  • Telecommunications Engineering
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Fifth-generation mobile communication technology (5G) necessitates higher reliability and efficiency for vehicle-to-infrastructure (V2I) connections.
  • New Radio-Vehicle to Infrastructure (NR-V2I) systems utilize Link Adaptation (LA) to manage dynamic V2I environments, aiming to boost information transmission efficacy.
  • Challenges in V2I communication include Doppler shift and fast time-varying channels, which degrade transmission reliability and accuracy.

Purpose of the Study:

  • To propose a Double Deep Q-learning (DDQL) based LA scheduling algorithm for optimizing the modulation and coding scheme (MCS) in V2I communication for autonomous vehicles.
  • To enhance the reliability and accuracy of V2I information transmission, particularly road safety data, in complex and dynamic channel conditions.

Main Methods:

  • The study proposes a Double Deep Q-learning (DDQL) algorithm integrating Deep Neural Network (DNN) and Double Q-Network (DDQN) to optimize the MCS.
  • The algorithm addresses the overestimation issue in traditional Deep Q-learning (DQL) for Q-Network learning.
  • Space Division Multiplexing (SDM) is employed alongside MCS for scheduling in V2I communications.

Main Results:

  • The proposed DDQL algorithm demonstrates adaptability to complex V2I channel environments with varying vehicle speeds.
  • The algorithm effectively selects optimal scheduling schemes using a combination of MCS for V2I road information transmission.
  • The integration of SDM significantly improves the accuracy of transmitting road safety information.

Conclusions:

  • The developed DDQL algorithm enhances V2I communication reliability and efficiency for autonomous driving.
  • Optimized MCS and SDM contribute to accurate transmission of road safety information, fostering cooperative driving.
  • The approach effectively mitigates challenges posed by Doppler shift and time-varying channels in V2I scenarios.