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

A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:

• 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, wherein the characteristics of the fiber optic network include spectral efficiency, asymptotic power efficiency, average energy per bit, stimulated Brillouin scattering, stimulated Raman scattering, Rayleigh scattering, or any combination thereof.

A method, comprising:

• determining, by a processing system including a processor, a network topology in a fiber optic network, wherein the network topology comprises a plurality of network elements joined by fiber optic links;
• selecting, by the processing system, 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, by the processing system, 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, by the processing system, characteristics of the channel in the fiber optic network;
• repeating, by the processing system, the selecting and applying of the parameter values to determine the characteristics of the channel using different selected parameter values;
• repeating, by the processing system, the selecting and applying of the parameter values to determine the characteristics of each channel between every pair of network elements in the plurality of network elements;
• training, by the processing system, a machine learning (ML) model using the set of the parameters and the characteristics of each channel in the fiber optic network;
• and
• predicting, by the processing system, a target launch energy, power and efficiency using the ML model for each channel in the fiber optic network, wherein the characteristics of the fiber optic network include spectral efficiency, asymptotic power efficiency, average energy per bit, stimulated Brillouin scattering, stimulated Raman scattering, Rayleigh scattering, or any combination thereof.