Machine learning regression for QoT estimation of unestablished lightpaths
Machine learning regression for QoT estimation of unestablished lightpaths
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用于未建立光路 QoT 估计的机器学习回归
DOI：
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发表时间：
2021
期刊：
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通讯作者：
M. Tornatore
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作者：
Memedhe Ibrahimi;Hatef Abdollahi;C. Rottondi;A. Giusti;Alessio Ferrari;V. Curri;M. Tornatore
在候选光路建立之前对其传输质量（QoT）进行估计对于光网络资源分配的有效决策至关重要。最近的几项研究调查了机器学习（ML）方法，以准确预测未来光路的配置是否满足QoT指标（如广义信噪比（GSNR）或误码率）的给定阈值。给定一组特征，给定光路配置的GSNR可能仍然表现出变化，因为它取决于所考虑的特征未捕获的其他几个因素。由此可见，与光路配置相关的GSNR可以建模为随机变量，因此可以用概率分布函数来表征。然而，大多数现有的方法都试图直接回答“给定光路配置（例如，给定调制格式）在特定路径上可行吗？”这个问题，但没有考虑估计观测下度量的整个统计分布所能提供的额外好处。因此，在本文中，我们研究了如何使用ML回归方法来估计未建立光路的接收GSNR分布。特别地，我们通过利用通过两种不同的数据生成工具获得的合成数据来讨论和评估三种回归方法的性能。我们根据均方根误差和R2分数评估了这三种方法在现实网络拓扑上的性能，并将它们与简单预测GSNR平均值的基线方法进行了比较。此外，我们通过对错误部署决策的惩罚进行了成本分析，并强调了从网络运营商的角度利用所提出的估计方法的好处，这使得我们可以根据最先进的QoT分类技术，对光路部署做出更明智的决策。
Estimating the quality of transmission (QoT) of a candidate lightpath prior to its establishment is of pivotal importance for effective decision making in resource allocation for optical networks. Several recent studies investigated machine learning (ML) methods to accurately predict whether the configuration of a prospective lightpath satisfies a given threshold on a QoT metric such as the generalized signal-to-noise ratio (GSNR) or the bit error rate. Given a set of features, the GSNR for a given lightpath configuration may still exhibit variations, as it depends on several other factors not captured by the features considered. It follows that the GSNR associated with a lightpath configuration can be modeled as a random variable and thus be characterized by a probability distribution function. However, most of the existing approaches attempt to directly answer the question “is a given lightpath configuration (e.g., with a given modulation format) feasible on a certain path?” but do not consider the additional benefit that estimating the entire statistical distribution of the metric under observation can provide. Hence, in this paper, we investigate how to employ ML regression approaches to estimate the distribution of the received GSNR of unestablished lightpaths. In particular, we discuss and assess the performance of three regression approaches by leveraging synthetic data obtained by means of two different data generation tools. We evaluate the performance of the three proposed approaches on a realistic network topology in terms of root mean squared error and R2 score and compare them against a baseline approach that simply predicts the GSNR mean value. Moreover, we provide a cost analysis by attributing penalties to incorrect deployment decisions and emphasize the benefits of leveraging the proposed estimation approaches from the point of view of a network operator, which is allowed to make more informed decisions about lightpath deployment with respect to state-of-the-art QoT classification techniques.