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This study introduces a novel optical encoding model using Laguerre-Gaussian beams and machine learning for efficient data transmission. The model achieves a low bit error rate (BER) of 10^-9, demonstrating robust performance in optical communication systems.

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Area of Science:

  • Optical physics
  • Information theory
  • Machine learning

Background:

  • Orbital angular momentum (OAM) offers unique properties for optical data transmission.
  • Laguerre-Gaussian (LG) beams are key to OAM-based encoding.
  • Efficient and robust optical encoding is crucial for advanced communication.

Purpose of the Study:

  • To design a robust optical encoding model for efficient data transmission.
  • To leverage orbital angular momentum (OAM) properties of Laguerre-Gaussian (LG) beams.
  • To integrate machine learning for decoding and verify model robustness.

Main Methods:

  • Developed an optical encoding model using the intensity profile of superimposed OAM-carrying LG modes.
  • Utilized LG beam indices (p, ℓ) for data encoding.
  • Implemented a support vector machine (SVM) algorithm for data decoding.

Main Results:

  • Achieved a bit error rate (BER) of 10^-9.
  • Demonstrated robustness with a signal-to-noise ratio (SNR) of 10.2 dB in one SVM model.
  • Successfully decoded data using machine learning-based detection.

Conclusions:

  • The proposed optical encoding model is robust and efficient for data transmission.
  • SVM-based decoding effectively verifies the model's performance.
  • OAM properties of LG beams provide a viable foundation for high-performance optical communication.