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Cost-effective and data size-adaptive OPM at intermediated node using convolutional neural network-based image

Danshi Wang, Mengyuan Wang, Min Zhang

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    This study introduces a cost-effective optical performance monitoring (OPM) method using asynchronous delay-tap plots (ADTP) and convolutional neural networks (CNNs). The technique accurately estimates optical signal-to-noise ratio (OSNR) and identifies modulation formats in optical networks.

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    Area of Science:

    • Optical communications
    • Signal processing
    • Machine learning

    Background:

    • Optical performance monitoring (OPM) is crucial for future optical networks.
    • Existing methods may lack adaptability and accuracy, especially under challenging conditions like strong chromatic dispersion.
    • Electrical domain processing offers a flexible approach for OPM in both end-to-end and link monitoring scenarios.

    Purpose of the Study:

    • To propose a cost-effective and data size-adaptive OPM scheme.
    • To leverage image processing techniques with convolutional neural networks (CNNs) for analyzing asynchronous delay-tap plots (ADTP).
    • To enable accurate optical signal-to-noise ratio (OSNR) estimation and modulation format identification (MFI) in optical transmission links.

    Main Methods:

    • An OPM framework based on electrical domain processing was designed.
    • Asynchronous delay-tap plots (ADTP) were converted into two-dimensional (2D) digital vectors for CNN analysis.
    • Convolutional neural networks (CNNs) were trained and applied to ADTP images for OSNR estimation and MFI.

    Main Results:

    • Simulations achieved 100% accuracy for OSNR estimation and MFI across various chromatic dispersions (CDs) and modulation formats (16, 32, 64 QAM).
    • The ADTP-based CNN method demonstrated superior accuracy and faster convergence compared to asynchronous amplitude histograms (AAH), particularly with strong CDs.
    • Experimental results showed high OSNR estimation accuracies (97.81% for 16QAM, 96.56% for 64QAM) and MFI accuracy (99.84%) with low standard deviation (<0.45 dB).

    Conclusions:

    • The proposed ADTP-based image processing approach using CNNs is a feasible and effective method for optical link monitoring at intermediate nodes.
    • This technique offers a cost-effective, adaptive, and highly accurate solution for OPM in advanced optical networks.
    • The method shows significant advantages over traditional techniques, especially in handling complex signal impairments like strong chromatic dispersion.