Gaussian Process-Driven History Matching for Physical Layer Parameter Estimation in Optical Fiber Communication Networks
Abstract
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.
Cite
@article{arxiv.2202.11700,
title = {Gaussian Process-Driven History Matching for Physical Layer Parameter Estimation in Optical Fiber Communication Networks},
author = {Josh W. Nevin and Sam Nallaperuma and Seb J. Savory},
journal= {arXiv preprint arXiv:2202.11700},
year = {2022}
}
Comments
4 pages with 2 additional pages for references and appendix. 2 figures. To be presented as a workshop paper at the AAAI ADAM workshop 2022