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Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements
Published on: February 28, 2016
Ultralow-complexity fiber nonlinearity compensation based on gradient-driven pruned Kolmogorov-Arnold network
Abstract:
Fiber nonlinearity compensation is crucial for enhancing the transmission distance and data capacity of wavelength-division multiplexing (WDM) coherent optical communication systems, while existing equalizers based on conventional neural networks face a fundamental limitation of prohibitive computational complexity. In this Letter, we propose a gradient-driven pruned Kolmogorov-Arnold network (GDP-KAN) for efficient fiber nonlinearity compensation. Leveraging learnable spline activation functions, the KAN architecture achieves superior performance with a lightweight structure. Furthermore, we introduce a gradient-driven pruning strategy based on attribution score, which sparsifies the network according to the signal characteristics of nonlinear impairments, enabling ultralow-complexity operation. We carry out an experiment of 8-channel WDM transmission over 1600 km standard single-mode fiber (SSMF) with 64 GBaud polarization-division-multiplexed (PDM) 16-ary quadrature amplitude modulation (16-QAM) signals. The proposed GDP-KAN requires only 300 real multiplications per bit (RMPB), outperforming 1 step per span DBP by 0.63 dB Q2 factor gain with merely 12% of its complexity, while reducing the RMPB by 68.75% compared to the multilayer perceptron (MLP) based equalizer without performance degradation.
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