English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a methodology for the estimation of optical network physical layerparameters from signal to noise ratio via history matching. An expensivenetwork link simulator is emulated by a Gaussian process surrogate model, whichis used to estimate a set of physical layer parameters from simulated groundtruth data. The a priori knowledge assumed consists of broad parameter boundsobtained from the literature and specification sheets of typical networkcomponents, and the physics-based model of the simulator. Accurate estimationof the physical layer parameters is demonstrated with a signal to noise ratiopenalty of 1~dB or greater, using only 3 simulated measurements. The proposedapproach is highly flexible, allowing for the calibration of any unknownsimulator input from broad a priori bounds. The role of this method in theimprovement of optical network modeling is discussed.

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