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Artificial Intelligence in Optical Communication Networks
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Artificial Intelligence in Optical Communication Networks

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI in Autonomous Systems".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 1615

Special Issue Editor

Dr. Yibeltal Chanie Manie

E-Mail Website
Guest Editor
Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: AI; optical wireless communication; optical sensors

Special Issue Information

Dear Colleagues,

This Special Issue is titled “Artificial Intelligence in Optical Communication Networks”. AI is concerned with intelligent behaviour in artifacts. Deep learning and generative AI are two of the most recent innovations in artificial intelligence. AI has a wide range of applications in communication networks. AI technologies can significantly enhance the performance, efficiency, and reliability of optical networks by optimizing network operations, predicting and preventing failures, processing signals, and intelligently managing resources. AI-driven approaches enable more efficient data routing, wavelength assignment, and adaptive network control mechanisms, which are crucial for handling the exponential growth in data traffic and ensuring high-speed reliable communication.

This Special Issue calls for papers on AI in optical communication networks. It welcomes research articles that present novel theories, algorithms, systems, and applications surrounding AI in optical networks and encourages submissions from multiple disciplines, including artificial intelligence, optical networks, sensors, electrical engineering, computer sciences, information systems, and telecommunications. Topics of interest include, but are not limited to, the following:

  • AI-based optical network design and optimization;
  • Predictive maintenance in optical networks;
  • Fault detection, identification, localization, and recovery in optical networks;
  • AI for advanced modulation formats and signal processing in optical communications;
  • Machine learning for resource allocation and routing in optical networks;
  • AI for optical sensors and networks;
  • Intelligent control mechanisms;
  • Machine learning for optical performance monitoring and optimization;
  • Machine learning applications in optical networks;
  • Optical signal processing and performance enhancement.

Dr. Yibeltal Chanie Manie
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • optical communications
  • optical sensors
  • machine learning
  • deep learning
  • network optimization
  • predictive maintenance
  • fault detection and localization
  • resource allocation
  • modulation formats
  • intelligent control mechanisms
  • optical networking
  • signal processing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

21 pages, 3139 KiB  
Article
Resilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring
by Gulmina Malik, Imran Chowdhury Dipto, Muhammad Umar Masood, Mashboob Cheruvakkadu Mohamed, Stefano Straullu, Sai Kishore Bhyri, Gabriele Maria Galimberti, Antonio Napoli, João Pedro, Walid Wakim and Vittorio Curri
AI 2025, 6(7), 131; https://doi.org/10.3390/ai6070131 - 20 Jun 2025
Viewed by 755
Abstract
We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber [...] Read more.
We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks. Full article
(This article belongs to the Special Issue Artificial Intelligence in Optical Communication Networks)
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