In recent years, optical networks have become more complex due to traffic increase and service diversification, and it has become increasingly difficult for network operators to monitor large-scale networks and keep track of communication status at all times, as well as to control and operate the various services running on the networks. This issue is motivating the need for autonomous optical network diagnosis, and expectations are growing for the use of machine learning and deep learning. Another trend is the active movement toward reducing capital expenditure (CAPEX)/operational expenditure (OPEX) of optical transport equipment by employing whitebox hardware, open source software, and open interfaces. In this paper, we describe in detail the concept of a series of workflows for the whitebox transponder, including getting optical performance data from the coherent optical transceiver, diagnosing optical transmission line conditions by applying deep neural networks (DNNs) to the collected data, and notifying the remote network management system (NMS) of the diagnosis results. In addition, as one of the use cases, we demonstrate fiber bending detection based on the diagnosis workflow. Offline and online demonstrations show the deployed diagnosis system can identify the fiber bend with up to 99% accuracy in our evaluation environment.

中文翻译：

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使用深度神经网络进行监视和诊断技术以进行预测性光网络维护[已邀请]

近年来，由于通信量的增加和服务的多样化，光网络变得更加复杂，并且网络运营商越来越难以始终监控大型网络并随时跟踪通信状态以及控制和操作。网络上运行的各种服务。这个问题激发了对自动光网络诊断的需求，并且人们对使用机器学习和深度学习的期望也越来越高。另一个趋势是通过采用白盒硬件，开源软件和开放接口，积极朝着减少光传输设备的资本支出（CAPEX）/运营支出（OPEX）的方向发展。在本文中，我们详细描述了白盒应答器的一系列工作流程的概念，包括从相干光收发器获取光性能数据，通过对所收集的数据应用深度神经网络（DNN）诊断光传输线状况以及将诊断结果通知远程网络管理系统（NMS）。此外，作为用例之一，我们演示了基于诊断工作流程的纤维弯曲检测。离线和在线演示均显示，已部署的诊断系统可以在我们的评估环境中以高达99％的准确度识别光纤弯曲。我们将基于诊断工作流程演示纤维弯曲检测。离线和在线演示均显示，已部署的诊断系统可以在我们的评估环境中以高达99％的准确度识别光纤弯曲。我们将基于诊断工作流程演示纤维弯曲检测。离线和在线演示均显示，已部署的诊断系统可以在我们的评估环境中以高达99％的准确度识别光纤弯曲。