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Area of Science:

  • Wireless Communication
  • Optics and Photonics
  • Machine Learning

Background:

  • Line-of-sight blockage is a major challenge in sub-terahertz wireless networks.
  • The extended near-field range of sub-terahertz nodes enables near-field wavefront shaping.
  • Airy beams offer self-accelerating properties and curved trajectories to circumvent blockers.

Purpose of the Study:

  • To address the challenge of finding optimal Airy beam trajectories for sub-terahertz wireless networks.
  • To develop a physics-informed machine learning framework for effective Airy beam shaping.
  • To investigate the potential of engineered Airy beams in overcoming blockages and expanding network coverage.

Main Methods:

  • Developed a physics-informed machine learning framework integrating near-field electromagnetics, ray optics, and wave optics.
  • Engineered Airy beam trajectories using the developed framework.
  • Conducted experimental validation of the proposed approach.

Main Results:

  • The physics-informed machine learning framework successfully identified optimal Airy beam trajectories.
  • Experimentally verified that correctly configured Airy beams significantly increase link budget in high-blockage scenarios.
  • Demonstrated superior performance compared to traditional near-field beam focusing.

Conclusions:

  • Airy beams, when optimally configured via physics-informed machine learning, offer a viable solution for sub-terahertz wireless networks with blockages.
  • This approach enhances link budgets, expands coverage, and reduces blind spots.
  • The study provides crucial insights for practical implementation of curved beams in wireless communication.