Knowledge Distillation-Based Compression Model for QoT Estimation of an Unestablished Lightpaths

A precise Quality-of-transmission (QoT) estimation of a Lightpath (LP) before its deployment is a key step in effective network design and resource utilization. Deep neural network-based methods have recently shown promising results for QoT estimation tasks. However, these methods contain a large number of parameters and require heavy computational resources for accurate predictions. To this end, we propose a novel Knowledge distillation (KD) based compression method to obtain a compact and more accurate model for QoT estimation. Our simulation results demonstrate that the model trained using KD significantly improves accuracy with reduced parameters and computational complexity. To the best of our knowledge, this is the first time that the knowledge distillation technique has been used to estimate the QoT of an unestablished LP

QoT- Estimation Assisted by Transfer learning in Extended C-band Network Operating on 400ZR

We propose a transfer learning-based technique that assists in estimating the Quality-of-transmission (QoT) of the lightpaths in an extended C-band network on 400ZR. The proposed scheme develops the cognition using the traditional C-band operating network knowledge

Query Based Iterative Learning Approach for Lightpath Deployment in Optical Networks

Predicting the Quality of Transmission (QoT) of a Lightpath (LP) before its actual deployment is important for efficient resource utilization. Conventionally, analytical models using closed-loop formulation estimate QoT, which imposes substantial margins to avoid network outages. Recently, data-driven techniques have been shown as a potential alternative with excellent precision and real-time applicability. However, data-driven techniques require sufficient training data, which might be challenging to acquire during real network operations. In this context, we proposed a novel unsupervised Iterative learning (IL) framework developed on top of the Random forest (RF) classifier for QoT estimation of LP before deployment. We considered the Generalized signal-to-noise ratio (GSNR) as a characterizing parameter for QoT estimation of LP. Our simulation results illustrate that, by employing the proposed iterative learning approach, we can obtain 99% classification accuracy with a reduced number of training samples compared to the traditional supervised learning approach

Iterative Transfer Learning Approach for QoT Prediction of Lightpath in Optical Networks

Machine learning (ML) has been widely used in optical networks for accurate Quality-of-transmission (QoT) estimation of Lightpaths (LPs). However, this domain has two main issues: ML-based models require a sufficiently large amount of data for training, and once the model is trained on one type of configuration, it cannot be used for another configuration. This paper focuses on these two issues and proposes an Active Transfer Learning (ATL) based solution. In ATL, Active learning (AL) helps in reducing the dataset’s size while not compromising the model’s performance, while the Transfer learning (TL) concept enables the transfer of knowledge from a source domain to the target domain with improved accuracy. This combined approach of ATL delivers promising results with minimum data samples and enhanced performance

Convolutional neural network for quality of transmission prediction of unestablished lightpaths

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

Machine Learning Aided Control of Ultra-Wideband Indium Phosphide IQ Mach-Zehnder Modulators

A digital model of a dual-polarization IQ ultra-wideband indium phosphide Mach-Zehnder modulator is obtained through machine learning techniques. The model is used to test optimization algorithms that automatically set the modulator control voltages under different operative conditions finding the optimum bias point

Performance Analysis of Transfer-learning Approaches for QoT Estimation of Network Operating with 400ZR

In the last decade, internet traffic has increased exponentially due to the expansion of bandwidth-intensive applications and the evolution of the concept of the internet of things. To sustain this growth in internet traffic, network operators insist on maximizing the utilization of already deployed network infrastructure to its maximum capacity to maximize the CAPEX. In this context, an accurate and earlier calculation of the Quality of transmission (QoT) of the lightpaths (LPs) is essential for minimizing the required margins that result from the uncertainty of the working point of network elements. This article presents a novel QoT-Estimation (QoT-E) framework assisted by Transfer-learning (TL). The main focus of this study is to present a detailed analysis of two major TL approaches, i.e., the Transfer-learning feature extraction (TLFE) approach and the Transfer-learning fine-tuning (TLFT) method, and demonstrate their effectiveness in minimizing the uncertainties in QoT-E in comparison with standard baseline models like Artificial neural network (ANN) and Convolutional-neural network (CNN). The Generalized signal-to-noise ratio (GSNR) is considered a char-acterizing parameter for the QoT of LP. The dataset utilized in this analysis is generated synthetically using the GNPy platform. Promising results are achieved by reducing the overall required margin and extracting the residual network capacity

Evaluating Cross- feature Trained Machine Learning Models for Estimating QoT of Unestablished Lightpaths

The rapid increase in bandwidth-driven applications has resulted in exponential internet traffic growth, especially in the backbone networks. To address this growth of internet traffic, operators always demand the total capacity utilization of underlying infrastructure. In this perspective, precise estimation of the quality of transmission (QoT) of the lightpaths (LPs) is vital for reducing the margins provisioned by uncertainty in network equipment's working point. This article proposes and compares several data-driven Machine learning (ML) based models to estimate QoT of unestablished LP before its deployment in the future deploying network. The proposed models are cross-trained on the data acquired from an already established LP of an entirely different in-service network. The metric considered to evaluate the QoT of LP is the Generalized Signal-to-Noise Ratio (GSNR). The dataset is generated synthetically using well tested GNPy simulation tool. Promising results are achieved to reduce the GSNR uncertainty and, consequently, the provisioning margin

Cross-feature trained machine learning models for QoT-estimation in optical networks

The ever-increasing demand for global internet traffic, together with evolving concepts of software-defined networks and elastic-optical-networks, demand not only the total capacity utilization of underlying infrastructure but also a dynamic, flexible, and transparent optical network. In general, worst-case assumptions are utilized to calculate the quality of transmission (QoT) with provisioning of high-margin requirements. Thus, precise estimation of the QoT for the lightpath (LP) establishment is crucial for reducing the provisioning margins. We propose and compare several data-driven machine learning (ML) models to make an accurate calculation of the QoT before the actual establishment of the LP in an unseen network. The proposed models are trained on the data acquired from an already established LP of a completely different network. The metric considered to evaluate the QoT of the LP is the generalized signal-to-noise ratio (GSNR), which accumulates the impact of both nonlinear interference and amplified spontaneous emission noise. The dataset is generated synthetically using a well-tested GNPy simulation tool. Promising results are achieved, showing that the proposed neural network considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin. Furthermore, we also analyze the impact of cross-features and relevant features training on the proposed ML models’ performance

Iterative supervised learning approach using transceiver bit-error-rate measurements for optical line system optimization

Defining the working points of optical amplifiers is a key factor when managing optical networks, particularly for the quality of transmission (QoT) of deployed connections. However, given the lack of knowledge of physical layer parameters, in many cases operators use these infrastructures sub-optimally. In this work, a methodology is presented that optimizes the QoT of an optical line system (OLS) by setting the working points of the erbium-doped fiber amplifiers (EDFAs), by analysis of simulations that use synthetic data derived from experimental characterization of commercial devices. The procedure is divided into three phases: a physical layer characterization, a design process, and an iterative supervised learning approach. Within the first phase, a novel amplifier physical layer characterization is used, exploiting a simple EDFA model that allows an efficient estimation of the OLS behaviour, knowing only the setting operative ranges of the devices. The results show that the satisfactory outcome produced during the design phase is further improved by the iterative supervised learning approach. The latter approach is implemented for single OLSs between couples of adjacent re-configurable optical add & drop multiplexers (ROADMs), each equipped with a certain set of transceivers (TRXs), enabling the QoT estimation of the specific OLS