Machine learning regression for QoT estimation of unestablished lightpaths

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.