Nonlinear compensation for ultra-high symbol rate PDM-WDM systems based on second-order perturbation theory

Area of Science:

• Optical Communications
• Nonlinear Optics
• Machine Learning in Communications

Background:

• High-speed, long-haul optical transmission systems face severe performance degradation due to Kerr nonlinearity.
• Existing first-order perturbation methods struggle with higher-order nonlinear interference, limiting compensation effectiveness.
• Polarization division multiplexing wavelength division multiplexing (PDM-WDM) systems are increasingly affected by intra- and inter-channel nonlinear impairments.

Purpose of the Study:

• To propose a novel second-order perturbation theory model for PDM-WDM systems.
• To develop an advanced machine learning approach for compensating higher-order nonlinear impairments.
• To reduce the complexity and data requirements for nonlinear compensation in optical systems.

Main Methods:

• Developed a second-order perturbation theory model to characterize joint nonlinear impairments.
• Utilized triplets and quintuplets to model nonlinear crosstalk, especially for ultra-high symbol rates.
• Employed a Convolutional Bidirectional Long Short-Term Memory (CNN-BiLSTM) neural network with knowledge distillation and transfer learning.
• Fed characterized nonlinear features into the CNN-BiLSTM for compensation.

Main Results:

• Achieved significant reduction in implementation complexity and training data requirements (down to 10%).
• Improved Q-factor by up to 0.71 dB in experimental and simulation PDM-WDM systems compared to linear and DBP-40StPS schemes.
• Reduced computational complexity by up to 68.6% compared to the DBP-40StPS scheme.
• Demonstrated effectiveness for 16QAM and 64QAM modulation formats over various distances (400km to 1600km).

Conclusions:

• The proposed second-order perturbation model and CNN-BiLSTM approach effectively compensate for nonlinear impairments in PDM-WDM systems.
• This method offers superior performance and reduced computational complexity compared to existing techniques.
• The approach is suitable for high-speed, large-capacity, and long-haul optical transmission scenarios.