Next-Generation Optical Networks for 5G, 6G, and Beyond

A Special Issue of Photonics (ISSN 2304-6732) belonging to the section "Optical Communication and Network".

Deadline for manuscript submissions: closed (10 August 2026) | Viewed by 1857

Editors


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Guest Editor
Principal Investigator and Researcher at Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Barcelona, Spain
Interests: 5G and beyond; 6G; SDN/NFV; cloud-native; passive optical networking; integration of NTN to TN; MANO; edge computing; 5G core
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Guest Editor
Centre Tecnològic de Telecomunicacions de Catalunya, Parc Mediterrani de la Tecnologia—Building B4, Av. Carl Friedrich Gauss 7, 08860 Castelldefels, Barcelona, Spain
Interests: 6G; 5G/B5G technologies; optimization solutions in networking; algorithm design; cloud-native frameworks; advanced network architectures

Special Issue Information

Dear Colleagues,

The rapid deployment of 5G networks has accelerated the demand for more advanced network solutions that can handle the increasing requirements for bandwidth, ultra-low latency, and energy efficiency. As we look beyond 5G to 6G, optical networks will play a pivotal role in enabling these next-generation communication systems. With applications such as immersive media, autonomous vehicles, and the Internet of Everything (IoE) on the horizon, innovations in optical networking are essential. Key technologies include dynamic bandwidth allocation, fiber-to-everything (FTTx), and the integration of optical and wireless networks, which will be crucial in meeting the evolving demands of 5G, 6G, and beyond.

This Special Issue seeks to highlight cutting-edge research and advancements in the design, development, and implementation of optical networks for 5G, 6G, and future communication systems. We encourage contributions that explore new algorithms, architectures, and technologies that enhance the capabilities, performance, and efficiency of optical networks to support the complex requirements of these next-generation systems.

The topics of interest include, but are not limited to, the following:

  • Dynamic bandwidth allocation algorithms for optical networks in 5G/6G;
  • Convergence of optical and wireless networks for 5G and 6G;
  • Fiber-optic technologies for ultra-low-latency communication;
  • Network slicing and orchestration in 5G/6G optical networks;
  • Energy-efficient optical network design for 5G/6G;
  • Optical networks supporting IoT, smart cities, and autonomous systems;
  • Security and privacy in optical communication systems for 5G/6G;
  • AI and machine learning applications in optical network management;
  • Photonic technologies for 5G/6G applications;
  • Performance evaluation and scalability of next-generation optical networks.

Dr. Hamzeh Khalili
Dr. Fatemeh Tabatabimehr
Guest Editors

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Keywords

  • 5G and beyond
  • 6G
  • dynamic bandwidth allocation
  • network slicing
  • fiber-to-everything (FTTx)
  • optical fronthaul
  • backhaul
  • energy-efficient networks
  • photonic technologies
  • AI-driven network management

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Published Papers (1 paper)

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Research

19 pages, 3356 KB  
Article
Analysis of Machine Learning Models for Predicting the Quality Factor and Received Power in Free Space Optical Communication Systems
by Mansoor Qadir, Muhammad Umar, Muhammad Ismail Mohmand, Waqas A. Imtiaz and Sajjad Aleem
Photonics 2026, 13(8), 751; https://doi.org/10.3390/photonics13080751 - 10 Aug 2026
Viewed by 836
Abstract
The study examines the application of machine learning algorithms (MLAs) to enhance free space optical (FSO) communication performance by predicting quality-factor (QF) from key system parameters. FSO technology has developed as a promising solution for front-haul links in 5G, beyond the 5G (B5G), [...] Read more.
The study examines the application of machine learning algorithms (MLAs) to enhance free space optical (FSO) communication performance by predicting quality-factor (QF) from key system parameters. FSO technology has developed as a promising solution for front-haul links in 5G, beyond the 5G (B5G), and 6G transmission networks. Nevertheless, performance of an FSO communication link is limited by environmental challenges like weather conditions, attenuation and turbulence that can degrade signal quality and affect QF at the receiving end. Using the data collected through simulation analysis in OptiSystem, we trained several MLAs in order to analyze performance of this system through accurate prediction of the QF. Analysis shows that received power serves as an important parameter in translating the overall QF of the received signal. Furthermore, it is shown that the prediction accuracy of the tree-based approach such as random forest and gradient boosting ranges above 97% are reliant on channel conditions and the predicted parameter type. Full article
(This article belongs to the Special Issue Next-Generation Optical Networks for 5G, 6G, and Beyond)
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