Journal of Optical Communications and Networking最新文献_第9页

Segmented protection scheme based on maximum bandwidth sharing in F5G 基于 F5G 最大带宽共享的分段保护方案

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-28 DOI: 10.1364/JOCN.529958

Wenhong Liu;Yongli Zhao;Yajie Li;Xin Li;Sabidur Rahman;Jie Zhang

{"title":"Segmented protection scheme based on maximum bandwidth sharing in F5G","authors":"Wenhong Liu;Yongli Zhao;Yajie Li;Xin Li;Sabidur Rahman;Jie Zhang","doi":"10.1364/JOCN.529958","DOIUrl":"https://doi.org/10.1364/JOCN.529958","url":null,"abstract":"As guaranteed reliable experience (GRE) is one of the features of fifth-generation fixed networks (F5G), high-reliability optical transport networks (OTNs) have become one of the key technologies supporting this feature. Unfortunately, current OTN protection methods often provide fixed bandwidth for protection of 1 Gbps or more, which leads to resource wastage. Fine grain OTN (fgOTN) is an extension of existing OTN, which supports hitless bandwidth adjustment and uses 10 Mbps time slot isolation. The application of fgOTN’s advantages to network protection can save resources. However, how much initial protection bandwidth is reserved for links to improve the service recovery success probability after faults is a key issue to be studied. If the initially reserved protection bandwidth is too much, that may waste precious bandwidth resources and fail to recover other services. If the initially reserved protection bandwidth is too small, the controller needs to adjust the bandwidth frequently to meet service requirements, which puts tremendous pressure on network management and control. This study proposes a maximum bandwidth segmented shared protection (MBSSP) scheme, which is based on optimized centralized and distributed collaboration network management architecture. The protection scheme includes two algorithms: (i) the resource reservation algorithm used before the fault occurs based on maximum bandwidth segmented shared protection and (ii) the protection switch algorithm used after the fault occurs based on bandwidth adjustment. Simulative results show that, in a 38-node topology, compared with minimum bandwidth dedicated protection (MBDP), MBSSP only sacrifices 0.8% of resource utilization but can reduce the bandwidth adjustment probability by 15.8% and improves the recovery success probability by 33.4%. Compared with end-to-end shared protection (E2ESP), MBSSP improves recovery success probability by 42.9% and saves resources by 16.7%, although it increases the bandwidth adjustment probability by 20%.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 11","pages":"1145-1158"},"PeriodicalIF":4.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Raman amplifier design and launch power optimization in multi-band optical systems 多波段光学系统中的拉曼放大器设计和发射功率优化

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-25 DOI: 10.1364/JOCN.534006

Andre Souza;Nelson Costa;Joao Pedro;Joao Pires

引用次数: 0

Disaggregation and virtualization for future access and metro networks [Invited Tutorial] 未来接入网和城域网的分解与虚拟化 [特邀教程]

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-24 DOI: 10.1364/JOCN.534303

Jun-ichi Kani;Takahiro Suzuki;Yasutaka Kimura;Shin Kaneko;Sang-Yuep Kim;Tomoaki Yoshida

引用次数: 0

Domain adversarial adaptation framework for few-shot QoT estimation in optical networks 用于光网络中少量 QoT 估测的域对抗自适应框架

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-24 DOI: 10.1364/JOCN.530915

Zhuojun Cai;Qihang Wang;Yubin Deng;Peng Zhang;Gai Zhou;Yang Li;Faisal Nadeem Khan

{"title":"Domain adversarial adaptation framework for few-shot QoT estimation in optical networks","authors":"Zhuojun Cai;Qihang Wang;Yubin Deng;Peng Zhang;Gai Zhou;Yang Li;Faisal Nadeem Khan","doi":"10.1364/JOCN.530915","DOIUrl":"https://doi.org/10.1364/JOCN.530915","url":null,"abstract":"The increasing complexity and dynamicity of future optical networks will necessitate accurate, fast, and low-cost quality-of-transmission (QoT) estimation. Machine learning-based QoT estimation models have shown promise in ensuring the reliability and efficiency of optical networks. However, the data-driven nature of these models impedes their application in practical settings. To address the problem of limited data availability in the target domain, known as the few-shot learning problem, we propose a domain adversarial adaptation method that aligns the distributions of representations from different source and target domains by minimizing the domain discrepancy quantified by the approximate Wasserstein distance. We demonstrate the method’s effectiveness through a theoretical proof and two example adaptations, i.e., from simulation to experimental data and from experimental to real network data. Our method consistently outperforms commonly used artificial neural networks (ANNs) and more advanced transfer learning approaches for various target domain data sizes. More profoundly, we show two ways to further improve the prediction accuracy, i.e., incorporating unlabeled target domain data in the training stage and utilizing the learned representations after training to train a new ANN with a reweighting strategy. In the adaptation to actual field data, our model, trained with only eight labeled network data samples, outperforms an ANN trained with 300 samples, thus reducing the labeled target domain data burden by more than 97%. The proposed method’s adaptability and generalizability make it a promising solution for accurate QoT estimation with low data requirements in future intelligent optical networks.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 11","pages":"1133-1144"},"PeriodicalIF":4.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TEFNET24: reference packet optical network topology for edge to core transport TEFNET24：用于边缘到核心传输的参考分组光网络拓扑结构

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-24 DOI: 10.1364/JOCN.533131

Jose Manuel Rivas-Moscoso;Farhad Arpanaei;Gabriel Otero Perez;Jose David Martinez Jimenez;Juan Pedro Fernandez-Palacios;Oscar Gonzalez de Dios;Luis Miguel Contreras;Alfonso Sanchez-Macian;Jose Alberto Hernandez;David Larrabeiti;Jesus Folgueira

{"title":"TEFNET24: reference packet optical network topology for edge to core transport","authors":"Jose Manuel Rivas-Moscoso;Farhad Arpanaei;Gabriel Otero Perez;Jose David Martinez Jimenez;Juan Pedro Fernandez-Palacios;Oscar Gonzalez de Dios;Luis Miguel Contreras;Alfonso Sanchez-Macian;Jose Alberto Hernandez;David Larrabeiti;Jesus Folgueira","doi":"10.1364/JOCN.533131","DOIUrl":"https://doi.org/10.1364/JOCN.533131","url":null,"abstract":"In this paper, we introduce TEFNET24, a reference multi-layer hierarchical network topology that spans from access to core networks, specifically designed to meet the demands of beyond 5G and prepared for next-generation 6G communication systems. This topology, inspired by the actual network deployments of Telefónica in medium-sized countries (or large federal states) in Europe and America, integrates both IP and optical (DWDM) layers to provide a comprehensive framework for network design, optimization, and analysis. Our primary contribution is the development of an open-source benchmarking network, accessible to both researchers and industry professionals. This resource aims to facilitate the study and advancement of integrated IP and optical networks, allowing researchers to address key challenges such as traffic aggregation, latency reduction, cost efficiency, and support for advanced applications. We provide guidelines for utilizing this benchmark network, enabling users to evaluate and enhance their solutions for AI-driven network management, ultra-reliable low-latency communication, enhanced mobile broadband, and massive machine-type communication. By sharing this detailed and practical benchmarking network, we seek to foster innovation and collaboration within the optical network community, driving forward the capabilities and performance of future communication networks. A dataset with TEFNET24 details is provided.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 11","pages":"G28-G39"},"PeriodicalIF":4.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large language model-based optical network log analysis using LLaMA2 with instruction tuning 使用带有指令调整功能的 LLaMA2 进行基于大型语言模型的光网络日志分析

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-24 DOI: 10.1364/JOCN.527874

Yue Pang;Min Zhang;Yanli Liu;Xiangbin Li;Yidi Wang;Yahang Huan;Zhuo Liu;Jin Li;Danshi Wang

{"title":"Large language model-based optical network log analysis using LLaMA2 with instruction tuning","authors":"Yue Pang;Min Zhang;Yanli Liu;Xiangbin Li;Yidi Wang;Yahang Huan;Zhuo Liu;Jin Li;Danshi Wang","doi":"10.1364/JOCN.527874","DOIUrl":"https://doi.org/10.1364/JOCN.527874","url":null,"abstract":"The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 11","pages":"1116-1132"},"PeriodicalIF":4.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optical circuit switched three-stage twisted-folded Clos-network design model guaranteeing admissible blocking probability 保证可接受阻塞概率的光路交换式三级扭曲折叠 Clos 网络设计模型

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-22 DOI: 10.1364/JOCN.535282

Ryotaro Taniguchi;Takeru Inoue;Kazuya Anazawa;Eiji Oki

{"title":"Optical circuit switched three-stage twisted-folded Clos-network design model guaranteeing admissible blocking probability","authors":"Ryotaro Taniguchi;Takeru Inoue;Kazuya Anazawa;Eiji Oki","doi":"10.1364/JOCN.535282","DOIUrl":"https://doi.org/10.1364/JOCN.535282","url":null,"abstract":"Some data center networks have already started to use optical circuit switching (OCS) with potential performance benefits, including high capacity, low latency, and energy efficiency. This paper addresses a switching network design to maximize the network radix, i.e., the number of terminals connected to the network under the condition that a specified number of identical switches with the size \u0000<tex>$N times N$</tex>\u0000 and the maximum admissible blocking probability are given. Previous work presented a two-stage twisted and folded Clos network (TF-Clos) with a blocking probability guarantee for OCS, which has a larger network radix than TF-Clos with a strict-sense non-blocking condition. Expanding the number of stages allows for enhancing the network radix. This paper proposes a model designing an OCS three-stage TF-Clos structure with a blocking probability guarantee to increase the network radix compared to the two-stage TF-Clos. We formulate the problem of obtaining the network configuration that maximizes the network radix as an optimization problem. We conduct an algorithm based on an exhaustive search to obtain a feasible solution satisfying the constraints of the optimization problem. This algorithm identifies the structure with the largest network radix in non-increasing order to avoid unnecessary searches. Numerical results show that the proposed model achieves a larger network radix than the two-stage model.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 11","pages":"1104-1115"},"PeriodicalIF":4.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-complexity end-to-end deep learning framework for 100G-PON 面向 100G-PON 的低复杂度端到端深度学习框架

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-18 DOI: 10.1364/JOCN.532742

Yongxin Xu;Xiaokai Guan;Wenqing Jiang;Xudong Wang;Weisheng Hu;Lilin Yi

{"title":"Low-complexity end-to-end deep learning framework for 100G-PON","authors":"Yongxin Xu;Xiaokai Guan;Wenqing Jiang;Xudong Wang;Weisheng Hu;Lilin Yi","doi":"10.1364/JOCN.532742","DOIUrl":"https://doi.org/10.1364/JOCN.532742","url":null,"abstract":"End-to-end learning allows communication systems to achieve optimal performance compared with conventional blockwise structure design. By modeling the channel with neural networks and training the transmitter and receiver on this differentiable channel, the whole system can be jointly optimized. However, in existing schemes, channel modeling methods, such as the generative adversarial network and long short-term memory network, have complex architectures and cannot track channel changes, leading to less effective end-to-end learning. Meanwhile, the complexity of neural networks deployed at the transmitter and receiver is too high for practical applications. In this work, we propose an efficient and low-complexity end-to-end deep learning framework and experimentally validate it on a 100G passive optical network. It uses a noise adaptation network to model channel response and noise distribution and employs offline pretraining and online tracking training to improve the efficiency and accuracy of channel modeling. For the transmitter, it consists of a pattern-dependent look-up table (PDLUT) based on a neural network (NN-PDLUT) with a single convolutional layer. Further, the receiver is also an NN with a single convolutional layer; thus, the end-to-end signal processing is extremely simple. The experimental results show that end-to-end learning improves the receiver sensitivity by 0.85 and 1.59 dB compared with receiver-only equalization based on Volterra nonlinear equalization (VNLE) and joint equalization based on a PDLUT and a feed-forward equalizer, respectively. Moreover, the number of multiply–accumulate operations consumed by the transmitter and receiver in the end-to-end learning scheme is reduced by 75.7% compared with VNLE-based receiver-only equalization.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 11","pages":"1093-1103"},"PeriodicalIF":4.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optical networking that exploits massive wavelength/spectrum and spatial parallelisms 利用大规模波长/频谱和空间并行性的光网络技术

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-16 DOI: 10.1364/JOCN.532594

Hiroshi Hasegawa

引用次数: 0

Benchmarking framework for resource allocation algorithms in multicore fiber elastic optical networks 多核光纤弹性光网络资源分配算法基准框架

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2024-10-15 DOI: 10.1364/JOCN.534257

Juan Pinto-Rios;Barbara Dumas Feris;Christofer Vasquez;Gabriel Saavedra;Danilo Borquez-Paredes;Nicolas Jara;Ricardo Olivares;Saquib Amjad;Ariel Leiva;Carmen Mas-Machuca

引用次数: 0