135 citations · 608 across the 39 of their papers we have counts for
39 papers
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…
QoT estimation using EGN-assisted machine learning for multi-period network planning
Jasper Müller, Sai Kireet Patri, Tobias Fehenberger +3
The rapidly growing traffic demands in fiber-optical networks require flexibility and accuracy in configuring lightpaths, for which fast and accurate quality of transmission (QoT)…
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…
Comparison of Single-Wavelength and Multi-Wavelength Transponders in a Physical-layer-aware Network Planning Study
Jasper Müller, Ognjen Jovanovic, Carmen Mas-Machuca +3
Based on suitable system architectures and realistic specifications, transmit OSNR penalties and spectral constraints of multi-wavelength transponders are identified and analyzed i…
Employing Channel Probing to Derive End-of-Life Service Margins for Optical Spectrum Services. To appear in OPTICA Journal of Optical Communications and Networking
K. Kaeval, F. Slyne, S. Troia +8
Optical Spectrum as a Service (OSaaS) spanning over multiple transparent optical network domains, can significantly reduce the investment and operational costs of the end-to-end se…
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…
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 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…
A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications
Jasper Müller, Sai Kireet Patri, Tobias Fehenberger +3
An ML model based on precomputed per-channel SCI is proposed. Due to its superior accuracy over closed-form GN, an average SNR gain of 1.1 dB in an end-to-end link optimization and…
Estimating Quality of Transmission in a Live Production Network using Machine Learning
Jasper Müller, Tobias Fehenberger, Sai Kireet Patri +4
We demonstrate QoT estimation in a live network utilizing neural networks trained on synthetic data spanning a large parameter space. The ML-model predicts the measured lightpath p…
Black-Box Assessment of Optical Spectrum Services
Kaida Kaeval, Jörg-Peter Elbers, Klaus Grobe +4
A spectral sweep process is introduced to discover performance issues in optical spectrum services. We detect filtering penalty, spectral ripple/tilt and channel crosstalk in field…
Exploring Channel Probing to Determine Coherent Optical Transponder Configurations in a Long-Haul Network
Kaida Kaeval, Danish Rafique, Kamil Blawat +6
We use channel probing to determine the best transponder configurations for spectral services in a long-haul production network. An estimation accuracy better than +/- 0,7dB in GSN…
QoT Assessment of the Optical Spectrum as a Service in Disaggregated Network Scenarios
Kaida Kaeval, Tobias Fehenberger, Jim Zou +6
The potential to operate third-party terminals over multi-domain transparent optical networks attracts operators and customers to implement Optical Spectrum as a Service (OSaaS). A…
Channel Performance Estimations with Extended Channel Probing
Kaida Kaeval, Helmut Griesser, Klaus Grobe +3
We test the concept of extended channel probing in an Optical Spectrum as a Service scenario in coherent optimized flex-grid long-haul and 10Gbit/s OOK optimized 100-GHz fixed-grid…