GNPy: an open source application for physical layer aware open optical networks

TLDR

GNPy is an open‑source, disaggregated‑paradigm application that estimates quality of transmission for coherent WDM optical networks and is versatile across scenarios. The paper validates the GNPy application. GNPy is employed as a request‑for‑proposal engine, what‑if analysis tool, and network‑optimization engine, and its predictions were validated against experimental measurements across mixed‑fiber, Raman‑amplified, multivendor C‑band scenarios, testing distances up to 4000 km and various modulation formats and amplifiers. The validation demonstrates that GNPy predicts optical SNR and GSNR within 1 dB for over 90% of 500 samples and estimates transmitted power maximizing GSNR within 0.5 dB.

Abstract

In this paper, we describe the validation of GNPy. GNPy is an open source application that approaches the optical layer according to a disaggregated paradigm, and its core engine is a quality-of-transmission estimator for coherent wavelength division multiplexed optical networks. This software is versatile. It can be used to prepare a request for proposal/request for quotation, as an engine of a what-if analysis on the physical layer, to optimize the network configuration to maximize the channel capacity, and to investigate the capacity and performance of a deployed network. We validate GNPy by feeding it with data from the network controller and comparing the results to experimental measurements on mixed-fiber, Raman-amplified, multivendor scenarios over the full C-band. We then test transmission distances from 400 up to 4000 km, polarization-multiplexed (PM) quadrature phase shift keying, the PM-8 quadrature amplitude modulation (QAM) and PM-16QAM formats, erbium-doped fiber amplifier (EDFA) and mixed Raman–EDFA amplification, and different power levels. We show excellent accuracy in predicting both the optical signal-to-noise ratio and the generalized signal-to-noise ratio (GSNR), within 1 dB accuracy for more than 90% of the 500 experimental samples. We also demonstrate the ability to estimate the transmitted power maximizing the GSNR within 0.5 dB of accuracy.