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Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements
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Ultralow-complexity fiber nonlinearity compensation based on gradient-driven pruned Kolmogorov-Arnold network
Optics Letters
|December 15, 2025
Summary
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.
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.
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