论文标题
准确的封闭形式的实时EGN模型公式在8500上利用机器学习，彻底随机完整的C波段系统
Accurate Closed-Form Real-Time EGN Model Formula Leveraging Machine-Learning over 8500 Thoroughly Randomized Full C-Band Systems
论文作者
论文摘要
我们得出了一个近似的非线性干扰（NLI）闭合格式模型（CFM），能够处理非常广泛的光学WDM系统方案。我们测试了8500多个随机C波段WDM系统的CFM，其中6250个已载荷，部分加载了2250。该系统具有高度多样化的通道格式，符号率，纤维以及其他参数。我们通过使用简单的机器学习因子来增强公式，通过利用系统测试集来提高CFM精度。我们通过添加一个术语来进一步改善CFM，该术语模拟了NLI具有很高自相关性的特殊情况。最后，我们获得了使用数值综合增强的GN模型（或Egn-Model）发现的结果非常好的匹配。我们还通过将其预测与300个随机系统的全C型波段拆分模拟进行比较来检查CFM的精度。 CFM的高精度和非常快速的计算时间（毫秒）的组合可能使其成为实时物理体现象的光学网络管理和控制的有效工具。
We derived an approximate non-linear interference (NLI) closed-form model (CFM), capable of handling a very broad range of optical WDM system scenarios. We tested the CFM over 8500 randomized C-band WDM systems, of which 6250 were fully-loaded and 2250 were partially loaded. The systems had highly diversified channel formats, symbol rates, fibers, as well as other parameters. We improved the CFM accuracy by augmenting the formula with simple machine-learning factors, optimized by leveraging the system test-set. We further improvedthe CFM by adding a term which models special situations where NLI has high self-coherence. In the end, we obtained a very good match with the results found using the numerically-integrated Enhanced GN-model (or EGN-model). We also checked the CFM accuracy by comparing its predictions with full-C-Band split-step simulations of 300 randomized systems. The combined high accuracy and very fast computation time (milliseconds) of the CFM potentially make it an effective tool for real-time physical-layer-aware optical network management and control.
