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.

中文翻译：

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GNPy：物理层感知开放光网络的开源应用程序

在本文中，我们描述了GNPy的验证。GNPy是一种开放源应用程序，它按照分解的范式接近光学层，其核心引擎是用于相干波分复用光网络的传输质量估计器。该软件是多功能的。它可以用来准备提议/报价请求，作为对物理层进行假设分析的引擎，以优化网络配置以最大化信道容量，并调查部署的容量和性能。网络。我们通过向GNPy提供来自网络控制器的数据，并将结果与​​在整个C波段上混合光纤，拉曼放大，多厂商方案的实验测量结果进行比较，来验证GNPy。然后，我们测试从400到4000 km的传输距离，偏振复用（PM）正交相移键控，PM-8正交幅度调制（QAM）和PM-16QAM格式，掺ped光纤放大器（EDFA）和拉曼-EDFA混合放大，以及不同的功率水平。我们在预测光学信噪比和广义信噪比（GSNR）方面均显示出卓越的精度，对于500个实验样品中的90％以上，其精度在1 dB以内。我们还展示了估计传输功率的能力，该传输功率可在0.5 dB的精度范围内最大化GSNR。我们在预测光学信噪比和广义信噪比（GSNR）方面均显示出卓越的精度，对于500个实验样品中的90％以上，其精度在1 dB以内。我们还展示了估计传输功率的能力，该传输功率可在0.5 dB的精度范围内最大化GSNR。我们在预测光学信噪比和广义信噪比（GSNR）方面均显示出卓越的精度，对于500个实验样品中的90％以上，其精度在1 dB以内。我们还展示了估计传输功率的能力，该传输功率可在0.5 dB的精度范围内最大化GSNR。