The rapidly growing traffic demands in fiber-optical networks require flexibility and accuracy in configuring lightpaths, for which fast and accurate quality of transmission (QoT) estimation is of pivotal importance. This paper introduces a machine learning (ML)-based QoT estimation approach that meets these requirements. The proposed gradient-boosting ML model uses precomputed per-channel self-channel-interference values as representative and condensed features to estimate non-linear interference in a flexible-grid network. With an enhanced Gaussian noise (GN) model simulation as the baseline, the ML model achieves a mean absolute signal-to-noise ratio error of approximately 0.1 dB, which is an improvement over the GN model. For three different network topologies and network planning approaches of varying complexities, a multi-period network planning study is performed in which ML and GN are compared as path computation elements (PCEs). The results show that the ML PCE is capable of matching or slightly improving the performance of the GN PCE on all topologies while reducing significantly the computation time of network planning by up to 70%.

翻译：光纤网络中快速增长的业务需求要求配置光路时具备灵活性和准确性，因此快速准确的传输质量（QoT）估计至关重要。本文介绍了一种基于机器学习（ML）的QoT估计方法，可满足这些需求。所提出的梯度提升ML模型利用预计算的每信道自信道干扰值作为代表性且紧凑的特征，来估计灵活栅格网络中的非线性干扰。以增强型高斯噪声（GN）模型仿真为基准，该ML模型实现了约0.1 dB的平均信噪比绝对误差，优于GN模型。针对三种不同的网络拓扑及不同复杂度的网络规划方法，开展了一项多周期网络规划研究，将ML和GN作为路径计算单元（PCE）进行比较。结果表明，ML PCE在所有拓扑上均能达到或略微优于GN PCE的性能，同时将网络规划的计算时间大幅降低高达70%。