论文标题
高斯流程驱动的历史记录与光纤通信网络中物理层参数估计的匹配
Gaussian Process-Driven History Matching for Physical Layer Parameter Estimation in Optical Fiber Communication Networks
论文作者
论文摘要
我们提供了一种通过历史匹配来估计从信号到噪声比的光网物理层参数的方法。昂贵的网络链接模拟器由高斯流程替代模型模拟，该模型用于从模拟地面真实数据估算一组物理层参数。假定的先验知识由从典型网络组件的文献和规范表获得的广泛参数范围组成，以及基于物理的模拟模型。仅使用3个模拟测量值，证明了物理层参数的准确估计，以信号与噪声比为1〜 dB或更高的噪声比惩罚。所提出的方法是高度灵活的，可以从广泛的先验边界校准任何未知的模拟器输入。讨论了该方法在改进光网模型中的作用。
We present a methodology for the estimation of optical network physical layer parameters from signal to noise ratio via history matching. An expensive network link simulator is emulated by a Gaussian process surrogate model, which is used to estimate a set of physical layer parameters from simulated ground truth data. The a priori knowledge assumed consists of broad parameter bounds obtained from the literature and specification sheets of typical network components, and the physics-based model of the simulator. Accurate estimation of the physical layer parameters is demonstrated with a signal to noise ratio penalty of 1~dB or greater, using only 3 simulated measurements. The proposed approach is highly flexible, allowing for the calibration of any unknown simulator input from broad a priori bounds. The role of this method in the improvement of optical network modeling is discussed.
