135 citations · 486 across the 26 of their papers we have counts for
26 papers
The QTF-Backbone: Proposal for a Nationwide Optical Fibre Backbone in Germany for Quantum Technology and Time and Frequency Metrology
Tara Cubel Liebisch, Peter Kaufmann, Harald Schnatz +166
The recent breakthroughs in the distribution of quantum information and high-precision time and frequency (T&F) signals over long-haul optical fibre networks have transformative po…
Soft-Demapping for Short Reach Optical Communication: A Comparison of Deep Neural Networks and Volterra Series
Maximilian Schaedler, Georg Böcherer, Stephan Pachnicke
In optical fiber communication, optical and electrical components introduce nonlinearities, which require effective compensation to attain highest data rates. In particular, in sho…
Experimental Investigation of Machine Learning based Soft-Failure Management using the Optical Spectrum
Lars E. Kruse, Sebastian Kühl, Annika Dochhan +1
The demand for high-speed data is exponentially growing. To conquer this, optical networks underwent significant changes getting more complex and versatile. The increasing complexi…
Fault Monitoring in Passive Optical Networks using Machine Learning Techniques
Khouloud Abdelli, Carsten Tropschug, Helmut Griesser +1
Passive optical network (PON) systems are vulnerable to a variety of failures, including fiber cuts and optical network unit (ONU) transmitter/receiver failures. Any service interr…
Faulty Branch Identification in Passive Optical Networks using Machine Learning
Khouloud Abdelli, Carsten Tropschug, Helmut Griesser +1
Passive optical networks (PONs) have become a promising broadband access network solution. To ensure a reliable transmission, and to meet service level agreements, PON systems have…
Branch Identification in Passive Optical Networks using Machine Learning
khouloud Abdelli, Carsten Tropschug, Helmut Griesser +2
A machine learning approach for improving monitoring in passive optical networks with almost equidistant branches is proposed and experimentally validated. It achieves a high diagn…
Probabilistic Shaping for High-Speed Unamplified IM/DD Systems with an O-Band EML
Md Sabbir-Bin Hossain, Georg Bocherer, Talha Rahman +4
Probabilistic constellation shaping has been used in long-haul optically amplified coherent systems for its capability to approach the Shannon limit and realize fine rate granulari…
Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder
Khouloud Abdelli, Helmut Griesser, Christian Neumeyr +2
Semiconductor lasers have been rapidly evolving to meet the demands of next-generation optical networks. This imposes much more stringent requirements on the laser reliability, whi…
A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication
Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke
Semiconductor lasers, one of the key components for optical communication systems, have been rapidly evolving to meet the requirements of next generation optical networks with resp…
Experimental Comparison of PAM-8 Probabilistic Shaping with Different Gaussian Orders at 200 Gb/s Net Rate in IM/DD System with O-Band TOSA
Md Sabbir-Bin Hossain, Georg Böcherer, Youxi Lin +10
For 200Gb/s net rates, cap probabilistic shaped PAM-8 with different Gaussian orders are experimentally compared against uniform PAM-8. In back-to-back and 5km measurements, cap-sh…
Experimental Comparison of Cap and Cup Probabilistically Shaped PAM for O-Band IM/DD Transmission System
Md Sabbir-Bin Hossain, Georg Boecherer, Talha Rahman +9
For 200Gbit/s net rates, uniform PAM-4, 6 and 8 are experimentally compared against probabilistic shaped PAM-8 cap and cup variants. In back-to-back and 20km measurements, cap shap…
Machine Learning-based Anomaly Detection in Optical Fiber Monitoring
Khouloud Abdelli, Joo Yeon Cho, Florian Azendorf +3
Secure and reliable data communication in optical networks is critical for high-speed Internet. However, optical fibers, serving as the data transmission medium providing connectiv…
Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory
Khouloud Abdelli, Helmut Griesser, Peter Ehrle +2
To reduce operation-and-maintenance expenses (OPEX) and to ensure optical network survivability, optical network operators need to detect and diagnose faults in a timely manner and…
Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals
Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke
Fast and accurate fault detection and localization in fiber optic cables is extremely important to ensure the optical network survivability and reliability. Hence there exists a cr…
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…
Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers
Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke
A new privacy-preserving federated learning framework allowing laser manufacturers to collaboratively build a robust ML-based laser lifetime prediction model, is proposed. It achie…
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…
Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks
Khouloud Abdelli, Florian Azendorf, Helmut Griesser +2
We propose a deep learning approach based on an autoencoder for identifying and localizing fiber faults in passive optical networks. The experimental results show that the proposed…
A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis
Khouloud Abdelli, Helmut Griesser, Carsten Tropschug +1
A novel multitask learning approach based on stacked bidirectional long short-term memory (BiLSTM) networks and convolutional neural networks (CNN) for detecting, locating, charact…
56 GBaud PAM-4 100 km Transmission System with Photonic Processing Schemes
Irene Estébanez, Shi Li, Janek Schwind +3
Analog photonic computing has been proposed and tested in recent years as an alternative approach for data recovery in fiber transmission systems. Photonic reservoir computing, per…
Complexity Reduction of Volterra Nonlinear Equalization for Optical Short-Reach IM/DD Systems
Tom Wettlin, Talha Rahman, Jinlong Wei +3
We investigate approaches to reduce the computational complexity of Volterra nonlinear equalizers (VNLEs) for short-reach optical transmission systems using intensity modulation an…