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

Josh William Nevin, Sam Nallaperuma, Seb Savory

Keywords: Gaussian processes, history matching, optical communication networks

TL;DR: We present a novel, flexible method for estimation of the parameters describing the physics of optical communication network components, which allows for the calibration of any physics-based network model from readily available measurements.

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.