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Updated: Aug 26, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Modulation format recognition with transfer learning assisted convolutional neural network using multiple Stokes

Zhiruo Guo, Bo Liu, Jianxin Ren

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    This study introduces a new modulation format recognition (MFR) method using multi-core fiber (MCF) for elastic optical networks. The technique utilizes transfer learning with convolutional neural networks and Stokes sectional planes, significantly reducing training data and improving accuracy for next-generation fiber systems.

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning

    Background:

    • Elastic optical networks (EONs) require robust modulation format recognition (MFR) for efficient data transmission.
    • Traditional MFR methods struggle with the complexities introduced by multi-core fiber (MCF) transmission, such as polarization mixing and carrier frequency skew.
    • Developing advanced MFR schemes is crucial for the scalability and performance of next-generation space division multiplexing (SDM) fiber systems.

    Purpose of the Study:

    • To propose and experimentally validate a novel MFR scheme tailored for MCF-based EONs.
    • To leverage transfer learning (TL) and convolutional neural networks (CNNs) with Stokes sectional planes for enhanced feature representation and recognition accuracy.
    • To demonstrate the scheme's effectiveness in recognizing diverse modulation formats under challenging transmission conditions, including low optical signal-to-noise ratio (OSNR).

    Main Methods:

    • A modulation format recognition (MFR) scheme employing multiple Stokes sectional planes as signal features.
    • Integration of a transfer learning (TL) assisted convolutional neural network (CNN) for MFR.
    • Utilizing Stokes space mapping for improved feature representation, offering insensitivity to polarization mixing, carrier frequency skew, and phase offset.
    • Simultaneous input of multiple Stokes sectional planes to enhance neural network accuracy.

    Main Results:

    • The proposed MFR scheme achieved high recognition accuracy for nine modulation formats (BPSK, QPSK, 8PSK, US-8QAM, US-16QAM, US-32QAM, PS-8QAM, PS-16QAM, PS-32QAM) in a polarization division multiplexing (PDM)-EONs system over 5 km MCF at 12.5 GBaud.
    • The scheme demonstrated high accuracy even at low optical signal-to-noise ratio (OSNR).
    • A reduction of over 40% in required training samples compared to traditional CNN approaches was observed.
    • The MFR scheme exhibited significant tolerance to MCF crosstalk and enabled short training times for large-capacity SDM-EONs.

    Conclusions:

    • The proposed Stokes sectional planes-based MFR scheme with TL-assisted CNN is effective for MCF-based EONs.
    • The method offers superior feature representation and robustness against transmission impairments compared to traditional techniques.
    • This approach significantly reduces training data requirements and complexity, paving the way for efficient MFR in next-generation SDM fiber transmission systems.