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Photonics
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Machine Learning and Artificial Intelligence for Optical Networks
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Machine Learning and Artificial Intelligence for Optical Networks

A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Optical Communication and Network".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 2765

Special Issue Editor

Dr. Shu-Hao Chang

E-Mail Website
Guest Editor
Science and Technology Policy Research and Information Center, National Institutes of Applied Research, Taipei 10636, Taiwan
Interests: optical networks; optical communication; optics patent analysis; silicon photonics; technology and innovation management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, with the rapid development of generative artificial intelligence, applications related to machine learning and artificial intelligence have attracted increasing attention. Leading countries have invested substantial capital and manpower in developing and optimizing the infrastructure necessary for machine learning and artificial intelligence systems, with the aim of fully leveraging their key advantages in large-scale data processing, pattern recognition, and intelligent decision-making. Over the past 20 years, data traffic in optical networks has grown dramatically, highlighting their critical role and the market demand for this technology. Therefore, academia and industry should work hand in hand to jointly develop technologies related to machine learning and artificial intelligence for optical networks.

To this end, we are pleased to announce a Special Issue focusing on forward-looking concepts in machine learning and artificial intelligence for optical networks. Given the growing interest in the advancements and future directions of optical networks, the conclusions of this Special Issue are expected to pave the way for broader and more in-depth research. In addition, we aim to compile and provide comprehensive recommendations and guidelines to help envision the technological prospects of this field. This Special Issue welcomes original research articles and review papers on theoretical or experimental advances in machine learning and artificial intelligence for optical networks. The issue will focus on cutting-edge technological developments, emerging trends, and relevant applications in this domain. We strongly encourage submissions addressing the topics outlined by the keywords below. Other related topics will also be considered.

Dr. Shu-Hao Chang
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 250 words) can be sent to the Editorial Office for assessment.

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. Photonics 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 2400 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

  • optical networks
  • machine learning
  • artificial intelligence
  • optical communication
  • technology foresight

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

Published Papers (4 papers)

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Research

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24 pages, 1545 KB  
Article
PMSDA: Progressive Multi-Strategy Domain Alignment for Cross-Scene Vibration Recognition in Distributed Optical Fiber Sensing
by Yuxiang Ni, Jing Cheng, Di Wu, Qianqian Duan, Linhua Jiang, Xing Hu and Dawei Zhang
Photonics 2026, 13(4), 334; https://doi.org/10.3390/photonics13040334 - 29 Mar 2026
Viewed by 510
Abstract
Distributed optical fiber vibration sensing (DVS) has shown strong potential in perimeter security, pipeline leakage monitoring, transportation safety, and structural health diagnostics owing to its high sensitivity, long-range coverage, and immunity to electromagnetic interference. However, severe cross-scene distribution mismatch is often encountered in [...] Read more.
Distributed optical fiber vibration sensing (DVS) has shown strong potential in perimeter security, pipeline leakage monitoring, transportation safety, and structural health diagnostics owing to its high sensitivity, long-range coverage, and immunity to electromagnetic interference. However, severe cross-scene distribution mismatch is often encountered in real-world deployments: indoor, outdoor, and pipeline environments exhibit markedly different noise patterns and time–frequency characteristics, thereby degrading the generalization ability of models trained in a single scene. To address this challenge, we propose a Progressive Multi-Strategy Domain Alignment (PMSDA) framework for label-disjoint cross-scene vibration recognition. PMSDA uses a compact expansion–compression encoder together with complementary alignment mechanisms—maximum mean discrepancy (MMD), correlation alignment (CORAL), and adversarial domain discrimination—to learn a scene-robust latent space from a labeled indoor source and two unlabeled target domains (outdoor and pipeline) within a single alternating-training model. Because the fine-grained source and target label spaces are disjoint, PMSDA is formulated as a representation-transfer framework rather than a standard label-shared unsupervised domain adaptation method; target-domain recognition is therefore performed through domain-specific prototype clustering in the aligned latent space. On three representative scenes with nine event classes in total, PMSDA achieved 89.5% accuracy, 86.7% macro-F1, and 0.93 AUC for Indoor→Outdoor, and 85.8%, 84.7%, and 0.87, respectively, for Indoor→Pipeline, outperforming traditional feature+SVM/RF pipelines, CNN/ResNet baselines, and representation-transfer baselines adapted from DANN/CDAN/SHOT under the same evaluation protocol. These results indicate that PMSDA is a promising and effective framework for offline cross-scene DVS evaluation under disjoint target event sets. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Optical Networks)
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9 pages, 1830 KB  
Communication
Adaptive Routing for Meshed QKD Networks of Flexible Size Using Deep Reinforcement Learning
by Tim Johann, Sebastian Kühl and Stephan Pachnicke
Photonics 2026, 13(2), 198; https://doi.org/10.3390/photonics13020198 - 16 Feb 2026
Viewed by 445
Abstract
Quantum Key Distribution (QKD) networks guarantee information-theoretical security of exchanged keys, but key rates are still limited. This makes efficient and adaptive routing a critical challenge, especially in meshed topologies without quantum repeaters. Conventional shortest path routing approaches struggle to cope with dynamic [...] Read more.
Quantum Key Distribution (QKD) networks guarantee information-theoretical security of exchanged keys, but key rates are still limited. This makes efficient and adaptive routing a critical challenge, especially in meshed topologies without quantum repeaters. Conventional shortest path routing approaches struggle to cope with dynamic key store filling levels and changes in network topologies, which leads to load imbalance and blocked connections. In this work, we propose an adaptive routing framework based on Deep Reinforcement Learning (DRL) for hop-wise end-to-end routing in unknown meshed QKD networks. The agent leverages Graph Attention Networks (GATs) to process the network states of varying topologies, enabling generalization across previously unseen meshed networks without topology-specific retraining. The agent is trained on random graphs with 10 to 20 nodes and learns a routing policy that explicitly balances key consumption across the network by utilizing a reward function that is based on the entropy of key store filling levels. We evaluate the proposed approach on the 14-node NSFNET topology under time-varying traffic demands. Simulation results demonstrate that the DRL-based routing significantly outperforms hop-based and weighted shortest path benchmarks, achieving up to a 18.7% increase in mean key store filling levels while completely avoiding key store depletion. These results highlight the potential of graph-based DRL methods for scalable, adaptive, and resource-efficient routing in future QKD networks. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Optical Networks)
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22 pages, 2306 KB  
Article
Learning Framework for Underwater Optical Localization Using Airborne Light Beams
by Jaeed Bin Saif, Mohamed Younis and Talal M. Alkharobi
Photonics 2026, 13(2), 133; https://doi.org/10.3390/photonics13020133 - 30 Jan 2026
Viewed by 724
Abstract
Underwater localization using airborne visible light beams offers a promising alternative to acoustic and radio-frequency methods, yet accurate modeling of light propagation through a dynamic air–water interface remains a major challenge. This paper introduces a physics-informed machine learning framework that combines geometric optics [...] Read more.
Underwater localization using airborne visible light beams offers a promising alternative to acoustic and radio-frequency methods, yet accurate modeling of light propagation through a dynamic air–water interface remains a major challenge. This paper introduces a physics-informed machine learning framework that combines geometric optics with neural network inference to localize submerged optical nodes under both flat and wavy surface conditions. The approach integrates ray-based light transmission modeling with a third-order Stokes wave formulation, enabling a realistic representation of nonlinear surface slopes and their effect on refraction. A multilayer perceptron (MLP) is trained on synthetic intensity–position datasets generated from this model, learning the complex mapping between received optical power (light intensity) and coordinates of the submerged receiver. The proposed method demonstrates high precision, stability, and adaptability across varying geometries and surface dynamics, offering a computationally efficient solution for optical localization in dynamic underwater environments. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Optical Networks)
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Review

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24 pages, 3069 KB  
Review
Dispersion Compensation Scheme with a Simple Structure in Ultra-High-Speed Optical Fiber Transmission Systems
by Ying Wu, Ying Wang, Luhan Jiang and Jianjun Yu
Photonics 2026, 13(1), 39; https://doi.org/10.3390/photonics13010039 - 31 Dec 2025
Cited by 2 | Viewed by 702
Abstract
With the explosive growth of global data traffic, long-distance fiber optic transmission systems are continuously evolving towards higher capacity and longer distances. However, to overcome the high complexity of fiber dispersion compensation algorithms, various dispersion compensation techniques have emerged. This paper aims to [...] Read more.
With the explosive growth of global data traffic, long-distance fiber optic transmission systems are continuously evolving towards higher capacity and longer distances. However, to overcome the high complexity of fiber dispersion compensation algorithms, various dispersion compensation techniques have emerged. This paper aims to systematically review and summarize dispersion compensation algorithms in long-distance fiber optic transmission. First, we briefly introduce the physical mechanism of fiber dispersion. Then, this paper focuses on digital domain compensation algorithms, dividing them into two major categories: compensation algorithms without penalty and with penalty. For compensation algorithms without penalty, we elaborate on traditional block processing strategies such as Overlap-Save (OLS), and various enhanced strategies combining intelligent filter segmentation and optimized frequency domain workflows. For compensation algorithms with penalty, we focus on analyzing a scheme that redesigns chromatic dispersion compensation (CDC) algorithm into a hardware-friendly structure using geometric clustering of taps, and finite-impulse-response (FIR) filters based on frequency response approximating the ideal inverse chromatic dispersion (CD) transfer function. By numerical simulation, we analyze the core principles, computational complexity, and compensation performance of each type of algorithm. Finally, this paper summarizes the limitations and development trends of existing dispersion compensation algorithms, pointing out that low-complexity and small-scale deployment algorithm structures will be an important research direction in the future. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Optical Networks)
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