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Modulation format recognition based on constellation diagrams and the Hough transform.

Safie El-Din Nasr Mohamed, Rasha M Al-Makhlasawy, Ashraf A M Khalaf

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    This study introduces a Hough transform (HT) method for wireless optical modulation format recognition (MFR). The technique accurately identifies modulation formats up to 100%, even at low optical signal-to-noise ratios (OSNR).

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning

    Background:

    • High-speed wireless communication is crucial for modern personal and professional environments.
    • Accurate identification of optical modulation formats is essential for reliable wireless communication systems.

    Purpose of the Study:

    • To present a novel scheme for wireless optical modulation format recognition (MFR) using the Hough transform (HT).
    • To evaluate the performance of deep learning classifiers (AlexNet, VGG16, VGG19) for MFR under varying optical signal-to-noise ratios (OSNR).

    Main Methods:

    • Utilizing the Hough transform (HT) for efficient feature extraction from constellation diagrams.
    • Employing AlexNet, VGG16, and VGG19 deep learning models for modulation format classification.
    • Analyzing the impact of sample size and OSNR levels (5–30 dB) on classification accuracy for eight modulation formats (PSK and QAM).

    Main Results:

    • The proposed HT-based scheme achieves up to 100% classification accuracy in identifying wireless optical modulation formats.
    • High accuracy is maintained even at low optical signal-to-noise ratios (OSNR) below 10 dB.
    • The study provides insights into the effect of sample size on classifier performance for each modulation format.

    Conclusions:

    • The developed scheme enables blind identification of wireless optical modulation formats with exceptional accuracy.
    • The Hough transform combined with deep learning classifiers offers a robust solution for MFR in high-speed wireless optical communication.
    • The method demonstrates effectiveness across various modulation types and challenging low OSNR conditions.