Photonics

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Special Issue Editors

Dr. Shuangyi Yan

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Guest Editor

Smart Internet Lab, University of Bristol, Bristol BS8 1TL, UK
Interests: machine learning; dynamic optical networks; 5G and beyond; multi-dimensional networks; software-defined networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yongli Zhao

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Guest Editor

State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: cloud and edge networking; NFV; optical network security; F5G

Dr. Xiaoliang Chen

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Guest Editor

University of California Davis, Davis, CA 95616, USA
Interests: optical networks; software-defined networking; network resilience; optimization; machine learning for networking

Special Issue Information

Dear Colleagues,

The developing concepts in networks, such as software-defined networking (SDN), virtual network embodiments, network function virtualization (NFV), and end-to-end orchestration, have increased the complexity of network management significantly, to a level that reaches far beyond what the current network management tools can offer. These challenges are more apparent in 5G and 5G bearer networks. On the other hand, computing capabilities have become critical in 5G architectures to enable low-latency communications and edge computing. Beyond network applications, network functions, such as RU, DU, and CU in the ORAN-based 5G-RAN solution, will run on the available computing resources without the limits of the dedicated hardware. Therefore, the deep integration of networking and computation has become a common goal to support emerging internet applications and functions. The latest advancements in machine-learning (ML) technologies provide a plethora of tools to drive the innovation of networks in terms of network management and network operations. It is believed that more advanced machine-learning technologies will be deployed in networks to enable revolutions in network management and operations, with the available computation resources. In this Special Issue, we will focus on ML-enabled networking and computation and intend to explore novel networking and computation resource provision, dynamic network operations, and self-managed networks to pave the way toward autonomous networks.

Dr. Shuangyi Yan
Prof. Dr. Yongli Zhao
Dr. Xiaoliang Chen
Guest Editors

Manuscript Submission Information

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Keywords

ML-aided network performance monitoring, telemetry services
Novel data plane technologies, network infrastructure designs
Network survivability, availability-aware service provisioning
Resource virtualization in cloud and network integration
Service orchestration and control architecture
SDN controller embedded with AI engine
Cloud computing and edge computing cooperation
AI assistant network operation method
AI-based failure diagnosis algorithm
Autonomous routing crossing multi-domain networks
Interoperability and optimization between computing resources and transport resources
Security in cloud and network integration.

Published Papers (2 papers)

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Research

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15 pages, 3386 KiB

Open AccessArticle

TDTS: Three-Dimensional Traffic Scheduling in Optical Fronthaul Networks with Conv-LSTM

by Bowen Bao, Zhen Xu, Chao Li, Zhengjie Sun, Sheng Liu and Yunbo Li

Photonics 2021, 8(10), 451; https://doi.org/10.3390/photonics8100451 - 18 Oct 2021

Cited by 2 | Viewed by 1612

Abstract

Given the more intensive deployments of emerging Internet of Things applications with beyond-fifth-generation communication, the access network becomes bandwidth-hungry to support more kinds of services, requiring higher resource utilization of the optical fronthaul network. To enhance resource utilization, this study novelly proposed a three-dimensional traffic scheduling (TDTS) scheme in the optical fronthaul network. Specifically, large and mixed traffic data with multiple different requirements were firstly divided according to three-dimensions parameters of traffic requests, i.e., arriving time, transmission tolerance delay, and bandwidth requirements, forming eight types of traffic model. Then, historical traffic data with division results were put into convolutional-long short-term memory (Conv-LSTM) strategy for traffic prediction, obtaining a clear traffic pattern. Next, the traffic processing order was supported by a priority evaluation factor that was measured by traffic status of the link and network characteristics comprehensively. Finally, following the priority, the proposed TDTS scheme assigned the resource to traffic requests according to their results of traffic division, prediction, and processing order with the shortest path routing and first-fit spectrum allocation policies. Simulation results demonstrated that the proposed TDTS scheme, on the premise of accurate traffic prediction, could outperform conventional resource-allocation schemes in terms of blocking probability and resource utilization. Full article

(This article belongs to the Special Issue Machine Learning in the Era of Computing and Network Integration)

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Review

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9 pages, 849 KiB

Open AccessReview

Modeling EDFA Gain: Approaches and Challenges

by Yichen Liu, Xiaomin Liu, Lei Liu, Yihao Zhang, Meng Cai, Lilin Yi, Weisheng Hu and Qunbi Zhuge

Photonics 2021, 8(10), 417; https://doi.org/10.3390/photonics8100417 - 29 Sep 2021

Cited by 9 | Viewed by 3838

Abstract

With the rapid development of virtual/augmented reality and cloud services, the capacity demand for optical communication systems is ever-increasing. To further increase system capacity, current researches focus on efficient and reliable system management, in which the transmission performance should be accurately estimated. The wavelength-dependent gain effects of erbium-doped fiber amplifiers (EDFAs) have a great impact on transmission performance, and therefore a precise EDFA gain model is required. In this paper, we firstly summarize the underlying principles and structures of EDFA, and introduce the gain performance and challenges in modeling. Then, we review the EDFA gain modeling methods. We categorize these researches into analytical modeling methods and machine learning (ML)-based modeling methods, and discuss their feasibilities and performances. In addition, we discuss the remaining problems for applying the models in a system and the possible directions for future investigations. Full article

(This article belongs to the Special Issue Machine Learning in the Era of Computing and Network Integration)

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