Building a digital twin for large-scale and dynamic C+L-band optical networks
Building a digital twin for large-scale and dynamic C+L-band optical networks
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DOI：
10.1364/jocn.503265
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发表时间：
2023-11
影响因子：
5
通讯作者：
Yao Zhang;Min Zhang;Yuchen Song;Yan Shi;Chunyu Zhang;Cheng Ju;B. Guo;Shanguo Huang;Danshi Wang
中科院分区：
文献类型：
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作者：
Yao Zhang;Min Zhang;Yuchen Song;Yan Shi;Chunyu Zhang;Cheng Ju;B. Guo;Shanguo Huang;Danshi Wang
数字孪生（DT）利用数据、模型和算法实现全面连接，弥合了真实的世界和虚拟世界之间的差距。近年来，光网络中DT的研究有所增加;然而，光网络正朝着宽带能力、高度动态状态和不断增加的规模发展，这带来了巨大的挑战，包括高复杂性、长计算时间和DT建模精度有限。在这项研究中，基于高斯噪声（GN）模型和深度神经网络（DNN）开发了DT模型，以在大规模C+ L波段光网络中执行高效准确的传输质量估计，从而促进数字平台的有效管理和控制。基于DNN的模型在大规模网络仿真中获得了0.2 dB以内的估计广义信噪比绝对误差，特别是77节点网络拓扑。此外，与基于GN的模型相比，基于DNN的模型测试时间从几十分钟大幅缩短至110 ms。此外，基于DT模型，研究了多个潜在应用场景，以确保高可靠运行和高效管理，包括物理层设备的优化和控制，劣化告警和链路故障的实时响应，以及网络重新路由和资源重新分配。构建的DT框架集成了实用的分析和推导功能，运算速度快，计算准确，逐步推动光网络的高效设计。
Bridging the gap between the real and virtual worlds, a digital twin (DT) leverages data, models, and algorithms for comprehensive connectivity. The research on DTs in optical networks has increased in recent years; however, optical networks are evolving toward wideband capabilities, highly dynamic states, and ever-increasing scales, posing huge challenges, including high complexity, extensive computational duration, and limited accuracy for DT modeling. In this study, the DT models are developed based on the Gaussian noise (GN) model and a deep neural network (DNN) to perform efficient and accurate quality of transmission estimations in large-scale C+L-band optical networks, facilitating effective management and control in the digital platform. The DNN-based model obtained the estimated generalized signal-to-noise absolute errors within 0.2 dB in large-scale network simulation, specifically a 77-node network topology. Additionally, compared to the GN-based model, the testing time by using the DNN-based model has been significantly reduced from tens of minutes to 110 ms. Moreover, based on the DT models, multiple potential application scenarios are studied to ensure high-reliability operation and high-efficiency management, including optimization and control of physical layer devices, real-time responses to deterioration alarms and link faults, and network rerouting and resource reallocation. The constructed DT framework integrates practical analysis and deduction functions, with fast operation and accurate calculation to gradually promote the efficient design of optical networks.