Advancements and Future Perspectives in All-Optical Detection and Reliability Improvement Technologies

A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Optical Interaction Science".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 205

Special Issue Editor


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Guest Editor
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: optical network survivability; optical network routing and resource allocation; all-optical fast matching

Special Issue Information

Dear Colleagues,

With the rapid development of bandwidth-intensive applications, optical networks have experienced a substantial increase in traffic. Theseoptical networks are highly vulnerable to security threats such as eavesdropping and attacks. Photonic firewalls, which can perform data identification and intrusion detection directly at the optical layer, are emerging as high-speed, low-energy network security tools. A core part of photonic firewalls is the all-optical matching system, which comprises all-optical logic gates such as the AND gate, NOT gate, XOR gate, etc. However, we continue to face the following challenges: First, SOA-based matching structures are unable to achieve all-optical matching at 40G and higher rates. In addition, there is a lack of matching structures for high-order modulation formats and existing structures that are suitable for amplitude signals. Furthermore, there is a lack of matching models for arbitrary modulation formats, where each modulation format corresponds to a set of matching structures. Lastly, the existing matching structures cannot handle the impact of system noise on matching, limiting the application of the current structures. These challenges highlight the need for further in-depth research to develop more effective solutions.

Dr. Xin Li
Guest Editor

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Keywords

  • optical network security
  • all-optical fast matching
  • all-optical logic gates
  • optical network reliability
  • reliability evaluation

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Published Papers (1 paper)

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Research

16 pages, 3161 KiB  
Article
Multi-Link Fragmentation-Aware Deep Reinforcement Learning RSA Algorithm in Elastic Optical Network
by Jing Jiang, Yushu Su, Jingchi Cheng and Tao Shang
Photonics 2025, 12(7), 634; https://doi.org/10.3390/photonics12070634 - 22 Jun 2025
Viewed by 88
Abstract
Deep reinforcement learning has been extensively applied for resource allocation in elastic optical networks. However, many studies focus on link-level state analysis and rarely discuss the influence between links, which may affect the performance of allocation algorithms. In this paper, we propose a [...] Read more.
Deep reinforcement learning has been extensively applied for resource allocation in elastic optical networks. However, many studies focus on link-level state analysis and rarely discuss the influence between links, which may affect the performance of allocation algorithms. In this paper, we propose a multi-link fragmentation deep reinforcement learning-based routing and spectrum allocation algorithm (MFDRL-RSA). We number the links using a breadth-first numbering algorithm. Based on the numbering results, high-frequency links are selected to construct the network state matrix that reflects the resource distribution. According to the state matrix, we calculate a multi-link fragmentation degree, quantifying resource fragmentation within a representative subset of network. The MFDRL-RSA algorithm enhances the accuracy of the agent’s decision-making by incorporating it into the reward function, thereby improving its performance in routing decisions, which contributes to the overall allocation performance. Simulation results show that MFDRL-RSA achieves lower blocking rates compared to the reference algorithms, with reductions of 16.34%, 13.01%, and 7.42% in the NSFNET network and 19.33%, 15.17%, and 9.95% in the Cost-239 network. It also improves spectrum utilization by 12.28%, 9.83%, and 6.32% in NSFNET and by 13.92%, 11.55%, and 8.26% in Cost-239. Full article
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