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Fast remodeling for nonlinear distortion mitigation based on transfer learning.

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    This summary is machine-generated.

    Transfer learning (TL) significantly accelerates deep learning network (DNN) training for compensating nonlinear distortions in optical PAM-4 transmissions. This method reduces training time by over half in single-channel and by three-fourths in multi-channel systems without performance loss.

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    Area of Science:

    • Optical Communications Engineering
    • Signal Processing
    • Machine Learning Applications

    Background:

    • Nonlinear distortions pose a significant challenge in high-speed optical communication systems, particularly in Pulse Amplitude Modulation with 4 levels (PAM-4) direct-detection transmission.
    • Traditional compensation methods often require extensive training data and computational resources, limiting their practical application.
    • The presence of partial correlation in nonlinear distortions suggests potential for knowledge transfer between different transmission scenarios.

    Purpose of the Study:

    • To propose and demonstrate a transfer learning (TL)-assisted deep learning network (DNN) for efficient nonlinear distortion compensation.
    • To investigate the effectiveness of TL in reducing training complexity and time for optical PAM-4 modulation systems.
    • To evaluate the performance of TL in both single-channel and multi-channel direct-detection optical transmissions.

    Main Methods:

    • Development of a deep learning network (DNN) architecture tailored for nonlinear distortion compensation.
    • Implementation of a transfer learning (TL) strategy to leverage pre-trained DNN parameters for new or related tasks.
    • Experimental validation using Nyquist PAM-4 modulation in both single-channel and five-channel direct-detection optical transmission setups.

    Main Results:

    • In single-channel transmissions, TL reduced the required training iterations or dataset size by more than half compared to retraining from scratch, with no performance degradation.
    • In five-channel transmissions, where channels share the same fiber, TL enabled parameter sharing among channels, leading to significantly faster remodeling.
    • Experimental results demonstrated that TL required only one-fourth of the training iterations compared to retraining, maintaining performance parity.

    Conclusions:

    • The proposed TL-assisted DNN is a highly effective method for compensating nonlinear distortions in optical PAM-4 modulation and direct-detection systems.
    • Transfer learning offers substantial benefits in reducing training time and complexity, making it a practical approach for real-world optical communication deployments.
    • The correlation of nonlinear distortions across multiple channels further enhances the efficiency of TL, enabling rapid adaptation and improved system performance.