Deep neural network based OSNR and availability predictions for multicast light-trees in optical WDM networks

The quality of transmission (QoT) of a light-tree is influenced by a variety of physical impairments including attenuation, dispersion, amplified spontaneous emission (ASE), nonlinear effect, light-splitting, etc. Moreover, a light-tree has multiple destinations that have different distances away from the source node so that the QoT of the received optical signal at each destination is different from each other. Since the optical network is a living network, the real-time network state is difficult to obtain. Therefore, it is difficult to accurately and rapidly determine the QoT or availability of a light-tree. However, the QoT or availability of a light-tree obtained in advance not only guarantees the quality of service (QoS) but also helps to network optimization design. This paper studies the problems of the optical signal-to-noise ratio (OSNR) and availability predictions for multicast light-trees while leveraging deep neural network (DNN) in optical WDM networks. The DNN based OSNR and availability prediction methods are developed and implemented. Numerical results show that the DNN based OSNR prediction method reaches an accuracy of about 95%. And the DNN based availability prediction method reaches a high accuracy greater than 98%. These two methods provide a fast decision approach for light-tree construction.