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    This study introduces a novel artificial intelligence (AI) technique using a Dammann vortex grating (DVG) and a CNN-LSTM model for improved orbital angular momentum shift keying (OAM-SK) decoding. The DVG-CNN-LSTM method enhances recognition accuracy with a compact architecture and low computational overhead.

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    Area of Science:

    • Optical Communications
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Orbital Angular Momentum Shift Keying (OAM-SK) is a promising technology for high-capacity optical communication.
    • Conventional methods for OAM-SK decoding, often relying solely on Convolutional Neural Networks (CNNs), face limitations in accuracy and complexity.
    • There is a need for advanced techniques to improve the decoding performance of OAM-SK signals.

    Purpose of the Study:

    • To present a novel artificial intelligence (AI)-based technique for decoding 16/32-ary OAM-SK signals.
    • To integrate a Dammann vortex grating (DVG) with a deep learning model for enhanced OAM-SK decoding.
    • To demonstrate the effectiveness of the proposed DVG-CNN-LSTM method in improving recognition accuracy and computational efficiency.

    Main Methods:

    • A 5×5 Dammann vortex grating (DVG) was employed to generate an OAM-SK light array across multiple diffraction orders (-12th to +12th).
    • The generated optical array was transformed into a sequential signal by exploiting the systematic evolution of the OAM-SK beam's light pattern across diffraction orders.
    • A Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model was utilized for recognizing and decoding the sequential signal.

    Main Results:

    • The DVG-CNN-LSTM method achieved significantly enhanced recognition accuracy for OAM-SK decoding compared to conventional approaches.
    • The proposed technique utilizes a compact architecture, resulting in low computational overhead.
    • The advanced acquisition of the OAM-SK light pattern by the DVG is attributed to the improved decoding performance.

    Conclusions:

    • The DVG-CNN-LSTM method represents the first successful integration of a deep learning model with DVG for improved OAM-SK decoding.
    • This innovative approach offers a marked enhancement in recognition accuracy and computational efficiency for OAM-SK signal processing.
    • The study highlights the potential of combining optical elements like DVG with AI for next-generation optical communication systems.