System and method for determining launch energy, power and efficiency for a smart channel in a dwdm network using machine learning

Abstract

Aspects of the subject disclosure may include, for example, a device, including: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: determining a network topology in a fiber optic network, wherein the network topology comprises a plurality of network elements joined by fiber optic links; selecting parameter values of a set of parameters for a channel between a first network element and a second network element in the plurality of network elements; applying the parameter values to create parameterized dense wavelength division multiplexing (DWDM) signals between the first network element and the second network element; responsive to the applying the parameter values, determining characteristics of the channel in the fiber optic network; repeating the selecting and applying of the parameter values to determine the characteristics of the channel using different selected parameter values; training a machine learning (ML) model using the set of the parameters and the characteristics of the channel in the fiber optic network; and predicting a target launch energy, power and efficiency using the ML model for the channel in the fiber optic network. Other embodiments are disclosed.