Digital Residual Spectrum-Based Generalized Soft Failure Detection and Identification in Optical Networks

Sun, Kaixuan; Yu, Zhenming; Shu, Liang; Wan, Zhiquan; Huang, Hongyu; Lei, Yi; Xu, Kun

doi:10.1109/tcomm.2022.3222519

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…In 2022, Sun et al [23] have offered a detection model and SFD according to the digital residual spectrum, which resisted the "Support Vector Machine (SVM) and Auto-Encoder (AE)". The features of the model were low cost, easy training, and high generalization.…”

Section: Related Workmentioning

confidence: 99%

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WITHDRAWN: Hybrid BSO+BMO: An Optimal Routing of Adaptive Deep Learning Approach with Attention Mechanism for Link Failure Detection in Elastic Optical Network

Sahana¹,

Sairam²

2023

Preprint

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Because of the enhancement in the data center services, the “Elastic Optical Network (EON)” is a very successive framework to interlink the information centers. The EON can elastically provide a spectrum tailored for multiple needs of bandwidths. In the link failure case, confirming the high stage “Quality of Service (QoS)” for candidate requests after the fault leads to an experiment focus. With the help of the modern digital signal processing approaches and developments in the integrated circuits and the coherent receivers in EON is able to estimate the link failures in the present time. The high-speed network survivability is highly important. When the sizes of the network get enhanced, the likelihood of the node and link impairment is also enhanced. Therefore, to predict the link impairment in EON, an adaptive technique is necessary. To accomplish this objective, a novel methodology is proposed using hybrid heuristic improvement. In the first stage, the required data is gathered and fed into the link failure detection model. The novel method is named an Atrous Spatial Pyramid Pooling – 1 Dimensional Convolution Neural Network with Attention mechanism (ASPP-1DCNN-AM), in which some of the hyper-parameters are tuned by proposing the hybrid algorithm as Iteration-aided Position of Beetle and Barnacles Mating (IPBBM). After forecasting the failure link, the model is in need of finding the optimal routing for better communication. Here, the optimal path is identified by using the IPBBM algorithm. Finally, the validation is done using divergent measurements and in contrast with traditional models. Hence, the designed system demonstrates that it achieves the higher detection results to make the data transmission effectively.

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Section: Related Workmentioning

confidence: 99%

“…It is not applicable to the real-time applications. AE [23] minimizes the noise of the given data. It eliminates the complexity of the datasets.…”

Section: Research Gaps and Challengesmentioning

confidence: 99%

WITHDRAWN: Hybrid BSO+BMO: An Optimal Routing of Adaptive Deep Learning Approach with Attention Mechanism for Link Failure Detection in Elastic Optical Network

Sahana¹,

Sairam²

2023

Preprint

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Measurement Informed Models and Digital Twins for Optical Fiber Communication Systems

Faruk,

Savory

2024

J. Lightwave Technol.

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Expertise-Enhanced Machine Learning for Failure Detection on Field-Deployed Optical Modules

Zhang,

Sun,

Yang

et al. 2025

J. Lightwave Technol.

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Digital Residual Spectrum-Based Generalized Soft Failure Detection and Identification in Optical Networks

Cited by 4 publications

References 33 publications

WITHDRAWN: Hybrid BSO+BMO: An Optimal Routing of Adaptive Deep Learning Approach with Attention Mechanism for Link Failure Detection in Elastic Optical Network

WITHDRAWN: Hybrid BSO+BMO: An Optimal Routing of Adaptive Deep Learning Approach with Attention Mechanism for Link Failure Detection in Elastic Optical Network

Measurement Informed Models and Digital Twins for Optical Fiber Communication Systems

Expertise-Enhanced Machine Learning for Failure Detection on Field-Deployed Optical Modules

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