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Related Experiment Video

Updated: Jul 30, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Autonomous Self-Adaptive and Self-Aware Optical Wireless Communication Systems.

Maged Abdullah Esmail1

  • 1Smart Systems Engineering Laboratory, Department of Communications and Networks Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia.

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|May 13, 2023
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Summary
This summary is machine-generated.

This study introduces machine learning (ML) for self-adaptive Free Space Optics (FSO) networks. ML accurately classifies modulation formats and predicts channel impairments, enhancing network autonomy.

Keywords:
FSOclassifiermachine learningoptical networksrandom forestregressorturbulence

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Area of Science:

  • Optical Networks
  • Artificial Intelligence
  • Machine Learning

Background:

  • Future optical networks require autonomous functions for resource optimization.
  • Self-adaptive and self-aware communication networks are crucial for dynamic environments.

Purpose of the Study:

  • To develop self-adaptive Free Space Optics (FSO) networks using artificial intelligence.
  • To employ machine learning (ML) for classifying modulation formats/baud rates and predicting channel impairments in FSO systems.

Main Methods:

  • Utilized machine learning techniques for FSO system analysis.
  • Considered four modulation formats and four baud rates common in commercial FSO systems.
  • Investigated two primary channel impairments affecting FSO performance.

Main Results:

  • Achieved 100% classification accuracy for modulation formats/baud rates, even in harsh conditions.
  • Demonstrated prediction accuracy for channel impairments ranging from 71% to 100%.

Conclusions:

  • ML effectively enables self-adaptive and self-awareness-free FSO networks.
  • The proposed ML approach enhances FSO network resilience and performance prediction.