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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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Efficient nonlinear equalization across S+C+L bands using online knowledge distillation in high-capacity DWDM

Tianqian Zhang, Qingyu He, Ming Luo

|June 14, 2025

Summary

We developed an efficient online knowledge distillation method for high-speed optical communication systems. This technique significantly boosts data rates and reduces computational complexity in dense wavelength division multiplexing (DWDM) systems.

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Area of Science:

Optical Communications
Signal Processing
Machine Learning

Background:

High-symbol-rate dense wavelength division multiplexing (DWDM) systems face nonlinear equalization challenges.
Existing methods struggle with the complexity of triple-band (S+C+L) equalization.

Purpose of the Study:

To propose an online knowledge distillation method using a teacher-student model for nonlinear equalization.
To achieve efficient triple-band equalization in a high-symbol-rate DWDM system.

Main Methods:

Implemented a teacher-student model for knowledge distillation.
Trained teacher models on representative C-band channels and distilled them to lightweight student models.
Applied the method to a system with 195 WDM channels, 98 GBaud PDM-PCS 256-QAM signals over 150 km SSMF.

Main Results:

Improved net data-rate from 201.5 Tb/s to 231.6 Tb/s.
Reduced computational complexity by up to 97.1% in the C band and 80% in the S and L bands.
Student models achieved performance comparable to teacher models.

Conclusions:

Online knowledge distillation is effective for nonlinear equalization in high-symbol-rate DWDM systems.
The proposed method offers significant improvements in data rate and computational efficiency.
This approach enables efficient triple-band equalization.