Link-adaptive digital twin for robust physical-layer modeling in hybrid-amplified ultra-wideband optical networks

2026-08-11

AI Summary

This work addresses the limited generalization and low modeling efficiency of existing physical-layer approaches in ultra-wideband optical networks, which struggle to accurately estimate the generalized signal-to-noise ratio (GSNR) under stimulated Raman scattering. The authors propose a Link-Adaptive Digital Twin (LA-DT) framework that decomposes GSNR modeling into amplified spontaneous emission (ASE), nonlinear interference (NLI), and signal power components. Innovatively integrating a neural network architecture with a linear modulation layer and a domain discriminator, the method leverages domain-adversarial training and few-shot fine-tuning to explicitly model Raman amplifier insertion loss within the digital twin for the first time. Experiments demonstrate substantial improvements: across 35 scenarios, prediction errors are significantly reduced (RMSE of 0.151, 0.111, and 0.113 dBm for NLI, ASE, and signal power, respectively), outperforming baselines by over 52%. Moreover, on 12 unseen scenarios, it achieves a GSNR RMSE of 0.159 dB with only 20 samples, highlighting exceptional generalization and rapid adaptability.

Abstract

Accurate physical-layer modeling is increasingly essential for reliable ultra-wideband operation and capacity optimization, especially under the intensified inter-channel stimulated Raman scattering (ISRS) effect. This paper proposes the link-adaptive digital twin (LA-DT) for hybrid-amplified ultra-wideband links to overcome the generalization and speed limitations of existing methods, achieving accurate modeling and robust generalized signal-to-noise ratio (GSNR) estimation across diverse links. First, to address EDFA heterogeneity, the GSNR modeling task is decomposed into three key power predictions: ASE, NLI, and signal powers before EDFA entry. Second, to enhance cross-scenario generalization, three dedicated DT models are developed using a novel neural architecture with linear modulation layers (LMLs). Third, for rapid adaptation to unseen scenarios with limited data, three domain discriminators guide few-shot fine-tuning of the LMLs. Fourth, the LA-DT explicitly accounts for Raman amplifier (RA) insertion loss, improving practical deployment reliability. Results across 35 scenarios show that LA-DT reduces RMSE for NLI, ASE, and signal power predictions to 0.151, 0.111, and 0.113 dBm with improvements of 56.0%, 58.4%, and 52.7% over the baseline,and achieves an average GSNR estimation RMSE of 0.114 dBm (55.8% improvement). For 12 unseen scenarios, the LA-DT maintains high accuracy through few-shot fine-tuning with only 20 samples per scenario, achieving an average GSNR RMSE of 0.159 dB and demonstrating strong adaptability and robustness.

Problem

• physical-layer modeling
• ultra-wideband optical networks
• inter-channel stimulated Raman scattering
• generalization
• signal-to-noise ratio estimation

Innovation

• link-adaptive digital twin
• linear modulation layers
• few-shot fine-tuning
• hybrid-amplified ultra-wideband
• generalized SNR estimation