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Vertical Cavity Surface Emitting Laser Performance Maturing through Machine Learning for High-Yield Optical Wireless

Ammar Armghan1, Khaled Aliqab1, Farman Ali2

  • 1Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.

Micromachines
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning optimizes vertical cavity surface emitting lasers (VCSELs) for high-speed optical wireless networks (OWNs). This enhances data transmission by purifying laser beams and mitigating interference, improving 5G and 6G mobility.

Keywords:
machine learningmathematical approachmultiple users with high data transmissionoptical wireless networkvertical cavity surface emitting laser

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Area of Science:

  • Optical Communications
  • Wireless Networking
  • Machine Learning Applications

Background:

  • Optical Wireless Networks (OWNs) are crucial for advancing 5G and 6G mobility.
  • High-speed data transmission relies on narrow bandwidth laser beams, similar to those used in optical fibers.
  • Vertical Cavity Surface Emitting Lasers (VCSELs) offer potential for efficient optical communication.

Purpose of the Study:

  • To evaluate VCSEL performance using machine learning (ML) for purified optical beams.
  • To enable OWN to support high-speed, multi-user data transmission.
  • To optimize VCSEL array paths for DC injection, AC signal modulation, and multi-user data transmission.

Main Methods:

  • Applied ML techniques to a designed VCSEL array.
  • Developed mathematical models for VCSEL narrow beams, OWN, and energy loss.
  • Validated the mathematical model with a simulation model using key performance matrices.

Main Results:

  • The ML methodology successfully narrowed the VCSEL beam.
  • Nonlinear interference within the OWN was significantly mitigated.
  • Increased energy efficiency was observed in the optical wireless network.

Conclusions:

  • The presented ML-based approach effectively purifies VCSEL beams for OWN.
  • This technique enhances OWN's capability for high-speed, multi-user data transmission.
  • The study demonstrates improved energy efficiency and reduced interference in optical wireless networks.