混合放大超宽带光网络中用于鲁棒物理层建模的链路自适应数字孪生
Link-adaptive digital twin for robust physical-layer modeling in hybrid-amplified ultra-wideband optical networks
- Beijing University of Posts and Telecommunications（北京邮电大学）
- China Mobile Group Design Institute Co., Ltd.（中国移动集团设计研究院有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对混合放大超宽带光网络提出链路自适应数字孪生（LA-DT），通过分解GSNR建模任务、开发专用DT模型、引入域判别器微调等方法，在多场景下实现精准鲁棒的物理层建模与GSNR估计。
AI中文摘要：
准确的物理层建模对于超宽带可靠运行和容量优化愈发关键，尤其在增强的信道间受激拉曼散射（ISRS）效应下。本文针对混合放大超宽带链路提出链路自适应数字孪生（LA-DT），以克服现有方法的泛化性与速度限制，实现跨不同链路的精准建模及鲁棒广义信噪比（GSNR）估计。首先，为解决掺铒光纤放大器（EDFA）的异质性，将GSNR建模任务分解为三个关键功率预测：EDFA前的放大自发辐射（ASE）、非线性干扰（NLI）及信号功率。其次，为增强跨场景泛化性，采用含线性调制层（LMLs）的新型神经架构开发三个专用数字孪生（DT）模型。第三，针对有限数据下对未见场景的快速适应，引入三个域判别器引导LMLs的少样本微调。第四，LA-DT明确考虑拉曼放大器（RA）的插入损耗，提升实际部署可靠性。在35个场景上的结果显示，LA-DT将NLI、ASE及信号功率预测的均方根误差（RMSE）分别降至0.151、0.111、0.113 dBm，较基线分别提升56.0%、58.4%、52.7%，且GSNR估计平均RMSE达0.114 dBm（提升55.8%）；在12个未见场景中，LA-DT通过每个场景仅20个样本的少样本微调保持高准确率，GSNR估计平均RMSE为0.159 dB，展现出强适应性与鲁棒性。
英文摘要：
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.
