Application of Machine Learning in Fiber Nonlinearity Modeling and Monitoring for Elastic Optical Networks

Zhuge, Qunbi; Zeng, Xiaobo; Lun, Huazhi; Cai, Meng; Liu, Xiaomin; Yi, Lilin; Hu, Weisheng

doi:10.1109/jlt.2019.2910143

Cited by 73 publications

(44 citation statements)

References 26 publications

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“…Network Topology 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]. In EON, there are some challenges for traditional analytical models.…”

Section: Monitoringmentioning

confidence: 99%

“…Therefore, some traditional methods with complex data processing and a long-time window may not be compatible with dynamic real-time applications. To solve this problem, advanced ML methods with forward propagation mechanisms [11], such as ANN, convolutional neural networks (CNN) and so on, can be employed to accomplish the feature extraction and estimate real-time status in a short time period [5,12,13]. These monitoring tools can be trained offline before deployment.…”

Section: Monitoringmentioning

confidence: 99%

“…In EON, during the phase of network planning, the accuracy of QoT and impairment models is influenced by various configurable parameters like modulation format, symbol rate and physical path in optical networks. If these parameters are not accurate, the estimations of QoT may have deviations compared with the real value [5,26,27]. In this case, due to the inaccuracy of planning tools, a large design margin [3] is needed and networks are overutilized to avoid network degradation until the EoL.…”

Section: Background and Challengesmentioning

confidence: 99%

“…In many scenarios, machine learning (ML) methods can obtain a higher accuracy and/or a lower complexity compared to analytical models. For instance, in [5], an artificial neural network (ANN) is adopted to estimate fiber nonlinear noise more accurately and efficiently compared to the original analytical model. The accuracy of this ANN-based nonlinear estimator is higher than the incoherent GN (IGN) model and the complexity is much lower than the SSFM.…”

Section: Introductionmentioning

confidence: 99%

“…High tolerance to parameter uncertainty: In a practical system, link parameters can be uncertain due to inaccurate measurements and other reasons. If the uncertainty of the model input exists, there might be a significant deviation between the real value and the model estimation [5]. Therefore, models that are less sensitive to parameter uncertainty are also desired.…”

mentioning

confidence: 99%

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

Liu

Lun

et al. 2020

Applied Sciences

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With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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Section: Monitoringmentioning

confidence: 99%

Section: Monitoringmentioning

confidence: 99%

Section: Background and Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

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Lun

et al. 2020

Applied Sciences

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Machine learning‐based regression models for predicting signal quality of dense wavelength division multiplexing (DWDM) optical communication network

Masih

Kaur

2023

Int J Communication

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Summary Over the years, optical communication systems have been a significant source of fast and secure communication. However, factors like noise and mitigation error can degrade the bit error rate (BER) and quality factor (Q factor) of optical communication systems. Predicting the optimal threshold, Q factor, and BER is usually a difficult task. Therefore, in this paper, machine learning‐based linear regression, least absolute shrinkage and selection operator (LASSO) regression, and Ridge regression have been used for a dense wavelength division multiplexing (DWDM)‐based optical communication network to predict the signal quality. These techniques have been used to predict the desired BER, Q factor, threshold, and eye height of the system. To demonstrate this research concept, a DWDM‐based optical communication network of 50 km length is designed and simulated using Optisystem‐14.0. After data preparation, regression models have been developed and validated through diagnostic plots. Results show that mean square error (MSE) has a significant decline with an increase in the number of epochs for all four models. LASSO and Ridge regression have effectively resolved the issue of overfitting, which occurred in the linear regression case. Furthermore, the mean MSE plot proved the significant reduction of mean MSE in the case of LASSO regression. Results show that min BER for LASSO regression came out to be −173,627.14, providing a robust and cost‐efficient process.

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Elevating optical networks: Machine learning approach for optimal resource scheduling and performance boost

S.S.,

Jain,

Bhatt

et al. 2024

Int J Communication

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SummaryThe increasing demand for massaging networks that are stable and quick needs reevaluations of standard optical networking administration strategies. To improve the efficacy of optical networks by integrating machine learning (ML) approach for the best resource scheduling, this research presents an innovative dynamic block widow optimized random forest (DBWO‐RF) strategy. To implement the DBWO‐driven resource allocation method in accordance with the categorization and clustering findings, the RF method is incorporated with the software defined optical to achieve channel quality assessment after successfully clustering employs the RF approach to achieve channel quality assessment after successfully clustering traffic patterns using the fuzzy C‐means (FCM) algorithm. To lessen the likelihood of blocking, the fragmentation‐function‐fit (FFF) algorithm was provided and the findings indicate that this approach possesses a reduced blocking risk. Using multiple approaches to modulation for various channel quality, the suggested resource allocation system leverages the DBWO approach to distribute the necessary resources based on various “traffic flow (TF)” clustering findings. The examination's outcomes demonstrate that, compared to other techniques under various given load levels, the present study has a reduced blocking risk, a sufficient complexity degree and greater effectiveness in the utilization of spectrum resources.

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Application of Machine Learning in Fiber Nonlinearity Modeling and Monitoring for Elastic Optical Networks

Cited by 73 publications

References 26 publications

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

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

Machine learning‐based regression models for predicting signal quality of dense wavelength division multiplexing (DWDM) optical communication network

Elevating optical networks: Machine learning approach for optimal resource scheduling and performance boost

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