Application of machine learning methods in provisioning of DWDM channels

Complexity and size of modern optic-fiber networks start to challenge the traditional methods of managing them and yet majority of telecommunication companies still report rapid growth of their optical networks. One of essential problems in managing optic-fiber networks is calculating the Quality of Transmission (QoT) of given path in network. The unit responsible for this task is Optical Performance Unit (OPU) which communicates with Network Management System (NMS). OPU's task is to determine whether it is possible to transmit signal through a given path. Modern OPUs are still operating based on traditional algorithms e.g. these systems take into consideration known physics rules and information about the network parameters, calculating transmission losses for each path. Main parameter that determines the OPUs result is Optical Signal to Noise Ratio (OSNR). However, measuring its value from NMS level is often not practical. An alternative solution to this problem might prove the application of Machine Learning (ML) algorithms for the estimation of OSNR. In this contribution an application of Artificial Neural Network (ANN) to an evaluation of OSNR in an optical Dense Wavelength Division Multiplexing (DWDM) network is investigated.