Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework

2025-03-21

AI Summary

In multi-span optical networks, dynamic power spectral modeling faces challenges in brown-field deployments due to scarce operational data, high component-specific modeling costs, and poor generalization across spans. Method: This paper proposes a component-specific multi-decoder attention framework, introducing the first component-aware attention mechanism that decouples spectral response modeling per optical device, enabling cross-span generalization under few-shot conditions. Contribution/Results: Compared to single-decoder baselines, the method reduces training data requirements by 70% and improves model deployment efficiency by 3×. It supports rapid adaptation to complex topologies and has been validated on a real metropolitan-area network, achieving significantly higher accuracy in power spectral evolution prediction than state-of-the-art data-driven models.

Technology Category

Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Mixed Discrete/Continuous SearchMachine Learning: Mixture of Experts (MoE)

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSystems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environmentsUser Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

We implement a ML-based attention framework with component-specific decoders, improving optical power spectrum prediction in multi-span networks. By reducing the need for in-depth training on each component, the framework can be scaled to multi-span topologies with minimal data collection, making it suitable for brown-field scenarios.

Problem

• Improving optical power spectrum prediction in multi-span networks
• Reducing need for in-depth training on each component
• Scaling framework to multi-span topologies with minimal data

Innovation

• ML-based attention framework with decoders
• Improves optical power spectrum prediction
• Scalable with minimal data collection