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Life sciences · Preprint
arXiv · August 11, 2026
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This preprint presents a neural network-based digital twin method (LA-DT) for modeling signal degradation in optical networks, designed to reduce computational burden and adapt to new link configurations with limited data. Performance is reported only on simulated scenarios with no peer review, experimental validation, or real-world deployment evidence.
Preprint. Simulated ultra-wideband hybrid-amplified optical network links under inter-channel stimulated Raman scattering effects.. Intervention: Link-adaptive digital twin (LA-DT) with neural networks, linear modulation layers, and domain discriminators for GSNR modeling and few-shot adaptation. Compared with: Baseline method (unspecified in abstract).
LA-DT reduces RMSE for NLI prediction to 0.151 dBm, a 56.0% improvement over baseline LA-DT reduces RMSE for ASE prediction to 0.111 dBm, a 58.4% improvement over baseline LA-DT reduces RMSE for signal power prediction to 0.113 dBm, a 52.7% improvement over baseline
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This is a preprint describing a computational modeling method with simulated performance metrics across scenarios, lacking peer review, clinical validation, or real-world deployment evidence.
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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.
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