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标题: "Link-adaptive digital twin for robust physical-layer modeling in hybrid-amplified ultra-wideband optical networks"
原文链接: "https://arxiv.org/abs/2608.10517"
发布日期: "2026-08-11"
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## Computer Science > Networking and Internet Architecture

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

Authors:[Xiaoxuan Gao](https://arxiv.org/search/cs?searchtype=author&query=Gao,+X), [Rentao Gu](https://arxiv.org/search/cs?searchtype=author&query=Gu,+R), [Yingchun Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y), [Xinyi Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+X), [Junshi Gao](https://arxiv.org/search/cs?searchtype=author&query=Gao,+J), [Yuefeng Ji](https://arxiv.org/search/cs?searchtype=author&query=Ji,+Y)

[View PDF](https://arxiv.org/pdf/2608.10517) [HTML (experimental)](https://arxiv.org/html/2608.10517v1)

> 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.

| Subjects: | Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an); Optics (physics.optics) |
| --- | --- |
| MSC classes: | 68T07, 68T05, 94A12, 68M10 |
| ACM classes: | C.2.1; B.4.3; I.2.6; I.6.5 |
| Cite as: | [arXiv:2608.10517](https://arxiv.org/abs/2608.10517) \[cs.NI\] |
|  | (or [arXiv:2608.10517v1](https://arxiv.org/abs/2608.10517v1) \[cs.NI\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10517](https://doi.org/10.48550/arXiv.2608.10517) |
| Journal reference: | Xiaoxuan Gao, Rentao Gu, Yingchun Wang, Xinyi Liu, Junshi Gao, Yuefeng Ji, Journal of Optical Communications and Networking, Volume: 18, Issue: 6, June 2026 |
| Related DOI: | [https://doi.org/10.1364/JOCN.580631](https://doi.org/10.1364/JOCN.580631) |

## Submission history

From: Rentao Gu \[[view email](https://arxiv.org/show-email/65a25b95/2608.10517)\]  
**\[v1\]** Tue, 11 Aug 2026 05:41:16 UTC (1,036 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10517) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
