Neural Networks in Optical Communications and Optical Computing: Implementation, Applications, and Prospects

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1297

Special Issue Editors

The State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: intelligent optical network

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Guest Editor
College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
Interests: photonics; optical networks; optical communications; failure management; network optimization; network sensor; machine learning

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Guest Editor
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Interests: communication network; network security; machine learning system
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Special Issue Information

Dear Colleagues,

In recent years, the convergence of artificial intelligence and photonic technologies has opened new frontiers in the design, optimization, and control of optical networks. Neural networks are increasingly being leveraged to enhance network performance and automate management. Meanwhile, photonic neural networks, built upon the inherent advantages of ultrafast and energy-efficient photonic computing, are emerging as a promising paradigm for real-time signal processing and intelligent decision-making directly in the optical domain. This Special Issue aims to provide a platform for researchers to share their latest advances, experimental demonstrations, and theoretical developments in the integration of neural networks and photonic technologies for optical networking. Contributions addressing innovative architectures, algorithms, devices, and applications are particularly encouraged. Topics of interest include, but are not limited to, the following:

  • AI-driven optical network control, optimization, and management;
  • Neural network-based fault diagnosis and performance prediction;
  • Photonic neural networks for high-speed signal processing;
  • Optical computing and machine learning accelerators;
  • Hybrid electronic–photonic architectures for intelligent communications;
  • Learning-based optical network design and resource allocation;
  • Hardware implementations and experimental demonstrations;
  • Emerging trends in intelligent, self-adaptive optical networks.

Articles, perspectives, and reviews are all welcome.

Dr. Zhiqun Gu
Dr. Ruikun Wang
Dr. Qiaolun Zhang
Guest Editors

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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.

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Keywords

  • neural networks
  • photonic neural networks
  • optical networks
  • artificial intelligence
  • photonics
  • optical signal processing
  • intelligent optical communications
  • optical computing
  • optical fiber systems
  • machine learning in photonics

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Published Papers (2 papers)

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Research

24 pages, 2013 KB  
Article
Capacity-Enhanced Li-Fi Transmission Using Autoencoder-Based Latent Representation: Performance Analysis Under Practical Optical Links
by Serin Kim, Yong-Yuk Won and Jiwon Park
Photonics 2026, 13(4), 356; https://doi.org/10.3390/photonics13040356 - 8 Apr 2026
Viewed by 229
Abstract
Visible light communication (VLC)-based Li-Fi systems suffer from limitations in transmission capacity expansion due to the restricted modulation bandwidth of LEDs. In this study, a latent representation-based NRZ-OOK Li-Fi transmission framework that exploits the statistical feature distribution of the latent space is proposed [...] Read more.
Visible light communication (VLC)-based Li-Fi systems suffer from limitations in transmission capacity expansion due to the restricted modulation bandwidth of LEDs. In this study, a latent representation-based NRZ-OOK Li-Fi transmission framework that exploits the statistical feature distribution of the latent space is proposed to improve transmission efficiency without expanding the physical bandwidth. An autoencoder is employed to transform input images into low-dimensional latent vectors, which are then quantized and modulated for transmission. At the receiver, hard decision and inverse quantization are performed, and the image is reconstructed through a trained decoder by leveraging the distribution characteristics of the latent representation. The effective transmission capacity gain Gcap is defined to quantify the amount of representable information relative to the original data under the same physical link resources according to the latent dimension, achieving up to a 49-fold data representation efficiency. The experimental results over practical optical links (0.5–1.5 m) showed that, in short-range conditions, larger latent dimensions maintained higher reconstruction PSNR, whereas under channel degradation conditions, smaller latent dimensions exhibited higher robustness, demonstrating a performance inversion phenomenon. Furthermore, it was confirmed that the dominant factor governing reconstruction performance shifts from the representational capability of the data to error accumulation characteristics depending on the channel condition. These results suggest that the latent representation-based transmission framework is an effective Li-Fi strategy that can simultaneously consider transmission efficiency and channel robustness through information representation optimization in bandwidth-limited environments. Full article
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19 pages, 2330 KB  
Article
Mercury: Accelerating 3D Parallel Training with an AWGR-WSS-Based All-Optical Reconfigurable Network
by Shi Feng, Jiawei Zhang, Huitao Zhou, Xingde Li and Yuefeng Ji
Photonics 2026, 13(3), 286; https://doi.org/10.3390/photonics13030286 - 16 Mar 2026
Viewed by 357
Abstract
The network traffic of 3D parallel training in large-scale deep learning, featuring burstiness, hot-spots, and periodic large-bandwidth patterns, severely challenges network efficiency, necessitating a high-performance and flexible optical network solution. To address this, this paper proposes Mercury, a hybrid optical network based on [...] Read more.
The network traffic of 3D parallel training in large-scale deep learning, featuring burstiness, hot-spots, and periodic large-bandwidth patterns, severely challenges network efficiency, necessitating a high-performance and flexible optical network solution. To address this, this paper proposes Mercury, a hybrid optical network based on physical optical components: its optical timeslot switching (OTS) subnet uses an arrayed waveguide grating router (AWGR) and tunable lasers for dynamic traffic, while the optical circuit switching (OCS) subnet relies on wavelength selective switches (WSSs) for low-latency high-bandwidth transmission, which is coordinated by selective valiant load balancing (S-VLB) and most efficient path configuration (MEPC) mechanisms. Validated via simulations and FPGA-based testbed experiments, Mercury outperforms the Sirius network by reducing epoch training time (e.g., 179s with five jobs) and relieving OTS congestion through offloading large flows to OCS. This work demonstrates that Mercury provides a flexible, high-performance physical optical solution for 3D parallel training of large-scale deep learning models. Full article
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