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arXiv · 2202.11700

Gaussian Process-Driven History Matching for Physical Layer Parameter Estimation in Optical Fiber Communication Networks

Josh W. Nevin·Sam Nallaperuma·Seb J. Savory
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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.

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Josh W. Nevin, Sam Nallaperuma, Seb J. Savory. 2022-02-16. Gaussian Process-Driven History Matching for Physical Layer Parameter Estimation in Optical Fiber Communication Networks. https://arxiv.org/abs/2202.11700

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