A survey on QoT prediction using machine learning in optical networks

Abstract

In optical networks, a connection (e.g., light-path and light-tree) is set up to carry data from its source to destination(s). When the optical signal transmits through the fiber links and optical devices, the quality of transmission (QoT) degrades due to various physical layer impairments (PLIs), including linear and nonlinear impairments. QoT is an important metric that determines the availability of a connection. Therefore, the QoT guarantee is the premise of successful connection establishment in optical networks. QoT prediction before connections establishment can provide guidance for the routing and resources allocation of connections. In order to receive the correct signal at the receiving end, during network planning design margins are introduced to compensate the inaccuracy of the QoT prediction model itself and its inputs. Improving the accuracy of prediction can make better use of network resources and reduce margins. With the help of strong computing power and data acquisition based on software defined optical network (SDON), machine learning (ML) based models are more suitable for QoT prediction than analytical models that are difficult to derive and computationally heavy. This paper provides an overview on the applications of ML technologies in QoT prediction. Firstly, we elaborate the QoT problem in optical networks, including main QoT influence factors, QoT metrics, and QoT prediction strategies. Then, suitable ML algorithms, the generation of sample data, ML frameworks and the construction of QoT prediction model, are briefly introduced. Next, three solutions of QoT prediction using various ML technologies in recent studies and their practical feasibility are reviewed and discussed in detail. Finally, based on the existing researches, we present some future research directions about the improvement of QoT prediction.