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  Subjects -> ELECTRONICS (Total: 207 journals)
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IEEE/OSA Journal of Optical Communications and Networking
Journal Prestige (SJR): 0.504
Citation Impact (citeScore): 3
Number of Followers: 19  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1943-0620 - ISSN (Online) 1943-0639
Published by IEEE Homepage  [228 journals]
  • Faulty branch identification in passive optical networks using machine

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      Authors: Khouloud Abdelli;Carsten Tropschug;Helmut Griesser;Stephan Pachnicke;
      Pages: 187 - 196
      Abstract: Passive optical networks (PONs) have become a promising broadband access network solution thanks to their wide bandwidth, low-cost deployment and maintenance, and scalability. To ensure a reliable transmission, and to meet service level agreements, PON systems have to be monitored constantly in order to quickly identify and localize network faults and thus reduce maintenance costs, minimize downtime, and enhance quality of service. Typically, a service disruption in a PON system is mainly due to fiber cuts and optical network unit (ONU) transmitter/receiver failures. When the ONUs are located at different distances from the optical line terminal, the faulty ONU or branch can be identified by analyzing the recorded optical time domain reflectometry (OTDR) traces. OTDR is a technique commonly used for monitoring of fiber optic links. However, faulty branch isolation becomes very challenging when the reflections originate from two or more branches with similar length overlap, which makes it very hard to discriminate the faulty branches given the global backscattered signal. Recently, machine learning (ML)-based approaches have shown great potential for managing optical faults in PON systems. Such techniques perform well when trained and tested with data derived from the same PON system. But their performance may severely degrade if the PON system (adopted for the generation of the training data) has changed, e.g., by adding more branches or varying the length difference between two neighboring branches, etc. A re-training of the ML models has to be conducted for each network change, which can be time consuming. In this paper, to overcome the aforementioned issues, we propose a generic ML approach trained independently of the network architecture for identifying the faulty branch in PON systems given OTDR signals for the cases of branches with close lengths. Such an approach can be applied to an arbitrary PON system without requiring to be re-trained for each change of-the network. The proposed approach is validated using experimental data derived from the PON system.
      PubDate: April 2023
      Issue No: Vol. 15, No. 4 (2023)
  • Random-blockage model and adaptive feedback strategy of CSI for an indoor
           VLC network

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      Authors: Guiyu Gong;Chaoqin Gan;Yong Fang;Yifan Zhu;Qiuyue Hu;
      Pages: 197 - 208
      Abstract: This paper investigates the feedback period of channel state information (CSI) considering random blockage for an indoor visible light communication (VLC) network. First, a random-blockage model (RBM) for the indoor VLC network was built. Through the RBM, a closed expression for the dynamic blockage and self-blockage probability were obtained. Based on the statistical method and RBM, a coherence distance model (CDM) under random blockage was established. Through the CDM, the weighted coherence distance of channel gain for user equipment at any position can be obtained under a specific distribution density of blockers. Based on the CDM, an adaptive feedback strategy for CSI was proposed, thus realizing the timely feedback of CSI under random blockage. Finally, the effectiveness of the above RBM and CDM was verified via simulation. The value obtained by Monte Carlo simulation is consistent with the theoretical value obtained by the RBM. Compared with the fixed-period feedback strategy, the adaptive feedback strategy was able to achieve compromise between improving channel reliability and reducing feedback overhead. The average interrupted time ratio and the average mean square error between the recovered channel gain and the actual channel gain were significantly reduced.
      PubDate: April 2023
      Issue No: Vol. 15, No. 4 (2023)
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