Task-aware meta equalizer for multi-scenario generalization in coherent DWDM systems.

Area of Science:

• Optical Communications
• Machine Learning
• Signal Processing

Background:

• Dense Wavelength Division Multiplexing (DWDM) systems face challenges with linear and nonlinear channel distortions.
• Current neural network equalizers (NNE) lack generalization across varying modulation formats, transmission rates, and optical signal-to-noise ratio (OSNR).
• Retraining NNE models for each new scenario is resource-intensive and impractical for elastic optical networks.

Purpose of the Study:

• To develop a task-aware neural network equalizer that overcomes the generalization limitations of traditional NNE.
• To enable rapid adaptation and efficient fine-tuning for diverse DWDM channel configurations.
• To improve the practicality of neural network equalization in dynamic elastic optical network environments.

Main Methods:

• Proposed a task-aware neural network equalizer utilizing Meta-SGD within a multi-task learning framework.
• Implemented a two-stage training strategy: meta-learning for fast adaptation and transfer learning for fine-tuning with limited data.
• Conducted experiments on a large-scale DWDM dataset covering S, C, and L bands, 263 channels, 200 km, and diverse parameters.

Main Results:

• The Meta-SGD approach significantly outperformed NNE methods on both seen and unseen tasks.
• Achieved higher Q factor with only 20% data after 10 adaptation steps.
• Converged within 10 epochs (seen) and 20 epochs (unseen), demonstrating superior data efficiency compared to NNE.

Conclusions:

• The proposed Meta-SGD-based task-aware equalizer offers superior data efficiency and generalization capabilities.
• It effectively addresses the limitations of traditional NNE in dynamic DWDM systems.
• Enables practical and robust equalization for elastic optical networks with varying configurations.