135 citations · 377 across the 21 of their papers we have counts for
21 papers
Threat Classification on Deployed Optical Networks Using MIMO Digital Fiber Sensing, Wavelets, and Machine Learning
Khouloud Abdelli, Henrique Pavani, Christian Dorize +4
We demonstrate mechanical threats classification including jackhammers and excavators, leveraging wavelet transform of MIMO-DFS output data across a 57-km operational network link.…
Weather-Adaptive Multi-Step Forecasting of State of Polarization Changes in Aerial Fibers Using Wavelet Neural Networks
Khouloud Abdelli, Matteo Lonardi, Jurgen Gripp +2
We introduce a novel weather-adaptive approach for multi-step forecasting of multi-scale SOP changes in aerial fiber links. By harnessing the discrete wavelet transform and incorpo…
Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks
Khouloud Abdelli, Matteo Lonardi, Jurgen Gripp +3
We propose a novel unsupervised anomaly detection approach using generative adversarial networks and SOP-derived spectrograms. Demonstrating remarkable efficacy, our method achieve…
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…
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…
DeepALM: Holistic Optical Network Monitoring based on Machine Learning
Joo Yeon Cho, Jose-Juan Pedreno-Manresa, Sai Kireet Patri +4
We demonstrate a machine learning-based optical network monitoring system which can integrate fiber monitoring, predictive maintenance of optical hardware, and security information…
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…
ML-based Anomaly Detection in Optical Fiber Monitoring
Khouloud Abdelli, Joo Yeon Cho, Carsten Tropschug
Secure and reliable data communication in optical networks is critical for high-speed internet. We propose a data driven approach for the anomaly detection and faults identificatio…
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…