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    A new gradient-driven pruned Kolmogorov-Arnold network (GDP-KAN) efficiently compensates for fiber nonlinearity in wavelength-division multiplexing (WDM) systems. This ultralow-complexity method significantly enhances transmission distance and data capacity.

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    Area of Science:

    • Optical Communications
    • Artificial Intelligence in Engineering

    Background:

    • Fiber nonlinearity limits transmission distance and data capacity in WDM coherent optical systems.
    • Conventional neural network equalizers suffer from high computational complexity.

    Purpose of the Study:

    • To propose an efficient fiber nonlinearity compensation method for WDM systems.
    • To reduce computational complexity while maintaining performance.

    Main Methods:

    • A gradient-driven pruned Kolmogorov-Arnold network (GDP-KAN) utilizing learnable spline activation functions.
    • A gradient-driven pruning strategy based on attribution score for network sparsification.
    • Experimental validation using 8-channel WDM transmission over 1600 km SSMF with 64 GBaud PDM 16-QAM signals.

    Main Results:

    • The GDP-KAN achieved ultralow complexity (300 RMPB).
    • Outperformed 1 step per span digital backpropagation (DBP) by 0.63 dB Q^2 factor gain with 12% complexity.
    • Reduced RMPB by 68.75% compared to MLP-based equalizers without performance degradation.

    Conclusions:

    • The GDP-KAN offers a highly efficient solution for fiber nonlinearity compensation in WDM systems.
    • Achieves superior performance with significantly reduced computational complexity.
    • Enables enhanced transmission distance and data capacity for optical communication systems.