Machine-Learning Method for Quality of Transmission Prediction of Unestablished Lightpaths

Abstract

Predicting the Quality of Transmission (QoT) of a lightpath prior to its deployment is a step of capital importance for an optimized design of optical networks. Due to the continuous advances in optical transmission, the number of design parameters available to system engineers (say, e.g., modulation formats, baud rate, code rate, etc.) is growing dramatically, thus significantly increasing the alternative scenarios for lightpath deployment. As of today, existing (pre-deployment) estimation techniques for lightpath QoT belong to two categories: "exact" analytical models estimating physical layer impairments, which provide accurate results but incur heavy computational requirements, and margined formulas which are computationally faster, but typically introduce high link margins that lead to underutilization of network resources. In this paper we explore a third option, i.e., Machine Learning (ML), as ML techniques have been already successfully applied for optimization and performance prediction of complex systems where analytical models are hard to derive and/or numerical procedures impose high computational burden. We investigate a ML classifier that predicts whether the bit-error rate of unestablished lightpaths meets the required system threshold, based on traffic volume, desired route and modulation format. The classifier is trained and tested on synthetic data and its performance is assessed over different network topologies and for various combinations of classification features. Results in terms of classifier accuracy are promising and motivate further investigation over real field data.

An Overview on Application of Machine Learning Techniques in Optical Networks

“…The authors in [45] use three of the above-mentioned classifiers to associate QoT labels with a large set of lightpaths Fig. 7: The classification framework adopted in [41].…”

Machine learning for intelligent optical networks: A comprehensive survey

“…Barletta et al predict whether the Bit-Error-Rate (BER) of a lightpath meet the QoT requirement. This problem is modeled as a classification tasks, and random forest is employed as classifier [93]. A Case-based Reasoning (CBR) method is proposed to estimate the lightpath QoT and to classify the lightpaths into high-or low-quality categories in impairment-aware wavelength-routed optical networks [96].…”

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

“…A high margin can lead to the underutilization of spectrum resources. Therefore, to build a low margin optical network to increase network capacity, a more accurate planning tool is needed to estimate the QoT prior to link deployment or reconfiguration [4]. In this case, an accurate QoT model is essential and impairment models can improve the accuracy of the QoT model.…”

“…The basic architecture of the modeling and monitoring techniques is shown in Figure 1. For the modeling, some models are applied to judge whether one lightpath meets the requirement for establishment in terms of the QoT [4]. Some are applied to estimate the specific value of the QoT or impairments [5].…”