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  1. Home
  3. Experimental Validation Of Xpm Mitigation Using A Generalizable Multi-task Learning Neural Network.
  1. Home
  3. Experimental Validation Of Xpm Mitigation Using A Generalizable Multi-task Learning Neural Network.

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Experimental validation of XPM mitigation using a generalizable multi-task learning neural network.

Sasipim Srivallapanondh, Pedro Freire, Bernhard Spinnler

    Optics Letters
    |December 13, 2024

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    We developed a multi-task learning (MTL) neural network equalizer for long-haul optical transmission. This approach enhances generalization and performance compared to single-task models, with reduced complexity.

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    Area of Science:

    • Optical Communications Engineering
    • Machine Learning in Telecommunications
    • Signal Processing for Optical Networks

    Background:

    • Long-haul dense wavelength-division multiplexing (DWDM) optical systems face signal impairments requiring advanced equalization.
    • Traditional post-equalizers struggle with generalization and complexity in high-capacity optical transmission.
    • Neural network (NN)-based equalizers offer potential but require robust generalization.

    Purpose of the Study:

    • To develop efficient neural network (NN)-based post-equalizers for coherent-detection DWDM systems.
    • To enhance the generalization capability of NN equalizers using multi-task learning (MTL).
    • To evaluate the performance and complexity of MTL equalizers against existing methods.

    Main Methods:

  • Implementation of a multi-task learning (MTL) framework for a shared neural network (NN) equalizer.
  • Training and validation of the MTL equalizer model using experimental data from long-haul DWDM systems.
  • Comparative analysis against single-task NN equalizer counterparts and previous approaches.

    • Main Results:

    • The developed MTL equalizer model demonstrates superior performance and generalization compared to single-task NNs.
    • Experimental data validates the effectiveness of MTL in improving equalizer performance.
    • Significant reduction in equalizer complexity is achievable with MTL without compromising performance.

    Conclusions:

    • Multi-task learning (MTL) provides an effective strategy for enhancing NN-based post-equalizers in long-haul DWDM optical systems.
    • MTL offers improved performance and generalization over single-task approaches.
    • The proposed MTL equalizer achieves a favorable balance between performance and computational complexity.