353 citations
- Christian-Albrechts-Universität zu KielDE12 papers
- Technical University of DenmarkDK5 papers
- Technical University of MunichDE4 papers
- Centre Tecnologic de Telecomunicacions de CatalunyaES2 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE2 papers
- Mitsubishi Electric (United States)US2 papers
- Ørsted (Denmark)DK2 papers
- Vertilas (Germany)DE2 papers
- Consorzio Nazionale Interuniversitario per le TelecomunicazioniIT1 paper
- Deutsche Telekom (Germany)DE1 paper
- Deutsche Telekom (United Kingdom)GB1 paper
- Fraunhofer Institute for Telecommunications, Heinrich Hertz InstituteDE1 paper
8 papers · 2 filters
Reconfigurable Optical Networks with Self-Tunable Transceivers: Implementation Options and Control
Michael H. Eiselt
This paper reviews methods for autonomous tuning of optical transceivers, based on an overhead management channel between the modules on both sides of the link. Different implement…
Lifetime Prediction of 1550 nm DFB Laser using Machine learning Techniques
Khouloud Abdelli, Danish Rafique, Helmut Griesser +1
A novel approach based on an artificial neural network (ANN) for lifetime prediction of 1.55 um InGaAsP MQW-DFB laser diodes is presented. It outperforms the conventional lifetime…
Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory
Khouloud Abdelli, Helmut Griesser, Carsten Tropschug +1
Optical time-domain reflectometry (OTDR) has been widely used for characterizing fiber optical links and for detecting and locating fiber faults. OTDR traces are prone to be distor…
A Hybrid CNN-LSTM Approach for Laser Remaining Useful Life Prediction
Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke
A hybrid prognostic model based on convolutional neural networks (CNN) and long short-term memory (LSTM) is proposed to predict the laser remaining useful life (RUL). The experimen…
Machine Learning based Laser Failure Mode Detection
Khouloud Abdelli, Danish Rafique, Stephan Pachnicke
Laser degradation analysis is a crucial process for the enhancement of laser reliability. Here, we propose a data-driven fault detection approach based on Long Short-Term Memory (L…
Machine Learning based Data Driven Diagnostic and Prognostic Approach for Laser Reliability Enhancement
Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke
In this paper, a data-driven diagnostic and prognostic approach based on machine learning is proposed to detect laser failure modes and to predict the remaining useful life (RUL) o…