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Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications.

Ricardo Suarez Del Valle1, Abdulkadir Kose2, Haeyoung Lee3

  • 1Department of Electronic Engineering, University of Surrey, Guildford GU2 7XH, UK.

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|July 12, 2025
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Summary

This study introduces a Contextual Multi-Armed Bandit (C-MAB) algorithm to improve millimeter wave (mmWave) vehicle-to-everything (V2X) communications. The novel approach enhances link stability and coverage using intelligent reflecting surfaces (IRSs).

Keywords:
RISV2Xmachine learningmmWavemulti-armed bandit

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Area of Science:

  • Engineering
  • Computer Science

Background:

  • Millimeter wave (mmWave) technology offers high bandwidth and low latency for vehicle-to-everything (V2X) communications.
  • Challenges in mmWave V2X include high propagation losses and limited coverage in urban environments.
  • Intelligent Reflecting Surfaces (IRSs) can mitigate these issues by improving signal paths.

Purpose of the Study:

  • To introduce a novel Contextual Multi-Armed Bandit (C-MAB) algorithm for dynamic beam and IRS selection in mmWave V2X.
  • To enhance link stability, coverage, and resource utilization in challenging vehicular environments.

Main Methods:

  • Development of a Contextual Multi-Armed Bandit (C-MAB) algorithm.
  • Dynamic adaptation of beam and IRS selections based on real-time environmental context.
  • Simulation of mmWave V2X scenarios with and without IRS assistance.

Main Results:

  • The C-MAB algorithm significantly improves link stability.
  • Average beam sojourn times are doubled compared to SNR-based and standard MAB strategies.
  • Performance gains of up to four times are achieved with IRS assistance.

Conclusions:

  • The proposed C-MAB approach optimizes resource allocation for mmWave V2X communications.
  • This method substantially enhances coverage, data rate, and resource utilization.
  • C-MAB offers a promising solution for reliable mmWave V2X connectivity.