Enhanced Accuracy in Optical Fiber Bending Detection with Machine Learning against Accumulated ASE Noise
Author:
Affiliation:
1. Tokyo Denki University,Information and Communication Engineering,Tokyo,Japan,120-8551
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9849706/9849712/09850105.pdf?arnumber=9850105
Reference8 articles.
1. Field Demonstration of Real-time Optical Network Diagnosis Using Deep Neural Network and Telemetry
2. Autonomous network diagnosis from carrier perspective;hirano;OSA J Optical Networking,2020
3. Intelligent Monitoring of Optical Fiber Bend Using Artificial Neural Networks Trained With Constellation Data
4. Open whitebox architecture for smart integration of optical networking and data center technology [Invited]
5. Monitoring and diagnostic technologies using deep neural networks for predictive optical network maintenance [Invited]
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1. Real-Time Detection of Optical Cross-Talk for Autonomous Network Diagnosis;2024 International Conference on Computing, Networking and Communications (ICNC);2024-02-19
2. Real-Time Detection of Fiber Bending and/or Optical Filter Shift by Machine-Learning of Tapped Raw Digital Coherent Optical Signals;IEICE Transactions on Communications;2023-11-01
3. A Data Representation and Resource Allocation Approach for Intelligent Network Architecture;2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta);2022-12
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