With the advancement in evolving concepts of software-defined networks and elastic-optical-network, the number of design parameters is growing dramatically, making the lightpath (LP) deployment more complex. Typically, worst-case assumptions are utilized to calculate the quality-of-transmission (QoT) with the provisioning of high-margin requirements. To this aim, precise and advanced estimation of the QoT of the LP is essential for reducing this provisioning margin. In this investigation, we present convolutional-neural-networks (CNN) based architecture to accurately calculate QoT before the actual deployment of LP in an unseen network. The proposed model is trained on the data acquired from already established LP of a completely different network. The metric considered to evaluate the QoT of LP is the generalized signal-to-noise ratio (GSNR). The synthetic dataset is generated by utilizing well appraised GNPy simulation tool. Promising results are achieved, showing that the proposed CNN model considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin.

中文翻译：

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用于未建立光路传输质量预测的卷积神经网络

随着软件定义网络和弹性光网络概念的不断发展，设计参数的数量急剧增加，使得光路（LP）部署变得更加复杂。通常，最坏情况假设用于计算传输质量 (QoT)，并提供高利润率要求。为此，对 LP 的 QoT 进行精确和高级的估计对于减少此配置余量至关重要。在这项调查中，我们提出了基于卷积神经网络 (CNN) 的架构，以在未见网络中实际部署 LP 之前准确计算 QoT。所提出的模型是根据从完全不同网络的已建立的 LP 获取的数据进行训练的。用于评估 LP 的 QoT 的指标是广义信噪比 (GSNR)。合成数据集是通过使用经过充分评估的 GNPy 模拟工具生成的。取得了有希望的结果，表明所提出的 CNN 模型极大地减少了 GSNR 的不确定性，从而最大限度地减少了配置余量。