Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks

• URL: http://arxiv.org/abs/2203.11727v1
• Date: Sat, 19 Mar 2022 09:26:07 GMT
• Title: Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks
• Authors: Khouloud Abdelli, Florian Azendorf, Helmut Griesser, Carsten Tropschug, Stephan Pachnicke
• Abstract summary: We propose a deep learning approach for identifying and localizing fiber faults in passive optical networks. The experimental results show that the proposed method detects faults with 97% accuracy, pinpoints them with an RMSE of 0.18 m and outperforms conventional techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 method detects faults with 97% accuracy, pinpoints them with an RMSE of 0.18 m and outperforms conventional techniques.

Related papers

• Eavesdropper localization for quantum and classical channels via nonlinear scattering [58.720142291102135]
  Quantum key distribution (QKD) offers theoretical security based on the laws of physics. We present a novel approach to eavesdropper location that can be employed in quantum as well as classical channels. We demonstrate that our approach outperforms conventional OTDR in the task of localizing an evanescent outcoupling of 1% with cm precision inside standard optical fibers.
  arXiv  Detail & Related papers  (2023-06-25T21:06:27Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• Branch Identification in Passive Optical Networks using Machine Learning [0.0]
  A machine learning approach for improving monitoring in passive optical networks with almost equidistant branches is proposed and experimentally validated. It achieves a high diagnostic accuracy of 98.7% and an event localization error of 0.5m.
  arXiv  Detail & Related papers  (2023-04-01T10:26:16Z)
• Denoising Diffusion Autoencoders are Unified Self-supervised Learners [58.194184241363175]
  This paper shows that the networks in diffusion models, namely denoising diffusion autoencoders (DDAE), are unified self-supervised learners. DDAE has already learned strongly linear-separable representations within its intermediate layers without auxiliary encoders. Our diffusion-based approach achieves 95.9% and 50.0% linear evaluation accuracies on CIFAR-10 and Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-03-17T04:20:47Z)
• Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing [58.73924499067486]
  Blockage detection is achieved by classifying light detection and ranging (LIDAR) data through a physics-based graph neural network (GNN) For precoder design, a preliminary channel estimate is obtained by running ray tracing on a 3D surface obtained from LIDAR data. Numerical simulations show that blockage detection is successful with 95% accuracy.
  arXiv  Detail & Related papers  (2022-09-15T15:04:55Z)
• Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals [0.0]
  We propose a novel data driven approach based on convolutional neural networks (CNNs) to detect and characterize the fiber reflective faults. In our simulations, we achieved a higher detection capability with low false alarm rate and greater localization accuracy even for low SNR values.
  arXiv  Detail & Related papers  (2022-03-19T09:20:40Z)
• Machine Learning-based Anomaly Detection in Optical Fiber Monitoring [0.0]
  We propose a data driven approach to accurately and quickly detect, diagnose, and localize fiber anomalies including fiber cuts, and optical eavesdropping attacks. The proposed method combines an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm. The experimental results demonstrate that: (i) the autoencoder detects any fiber fault or anomaly with an F1 score of 96.86%; and (ii) the attention-based bidirectional gated recurrent unit algorithm identifies the the detected anomalies with an average accuracy of 98.2%.
  arXiv  Detail & Related papers  (2022-03-19T08:56:54Z)
• ML-based Anomaly Detection in Optical Fiber Monitoring [0.0]
  We propose a data driven approach for anomaly detection and faults identification in optical networks to diagnose physical attacks such as fiber breaks and optical tapping. We verify the efficiency of our methods by experiments under various attack scenarios using real operational data.
  arXiv  Detail & Related papers  (2022-02-23T19:43:37Z)
• Sensor-Guided Optical Flow [53.295332513139925]
  This paper proposes a framework to guide an optical flow network with external cues to achieve superior accuracy on known or unseen domains. We show how these can be obtained by combining depth measurements from active sensors with geometry and hand-crafted optical flow algorithms.
  arXiv  Detail & Related papers  (2021-09-30T17:59:57Z)
• Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders [0.0]
  We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, without targeting a specific kind of source.
  arXiv  Detail & Related papers  (2021-07-27T09:56:49Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.