Monitoring and diagnostic technologies using deep neural networks for predictive optical network maintenance [Invited]

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. (C) 2021 Optical Society of America

Automated summary

In recent years, optical networks have become more complex, requiring autonomous optical network diagnosis and the support of machine learning. Another trend is to reduce capital expenditure/operational expenditure of optical transport equipment through whitebox hardware, open source software, and open interfaces. The paper describes in detail the concept of workflows for the whitebox transponder and demonstrates successful fiber bending detection as a use case.