Determination of optical performance of distributed Raman amplifiers using deep neural networks
AssigneeCiena Corporation
InventorsJeff Gar Don Wong, Connie Ruth Sutherland, Petar Djukic
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Abstract
Systems and methods include receiving inputs comprising a Raman gain target for a Raman amplifier, fiber parameters, and fiber characteristics, each for an optical span; analyzing the inputs with a deep neural network (DNN) having a plurality of layers; providing a plurality of outputs from the DNN; and utilizing the plurality of outputs for a link budget calculation on the optical span. The Raman amplifier includes a plurality of pumps each at a fixed wavelength.
Claims18
A non-transitory computer-readable medium comprising instructions that, when executed, cause one or more processors to perform steps of:
- receiving inputs comprising a Raman gain target for a Raman amplifier, fiber parameters including any of fiber attenuation coefficient, fiber span loss, and fiber loss margin, and fiber characteristics including any of fiber effective area, zero chromatic dispersion wavelength, fiber attenuation at Raman pump frequencies, and a peak Raman gain coefficient value, each for an optical span;
- analyzing the inputs with a deep neural network (DNN) having a plurality of layers;
- providing a plurality of outputs from the DNN, the outputs including any of a calculated Raman gain spectrum, an effective noise figure, an incremental multi-path interference (ΔMPI), and an Optical Service Channel (OSC) gain;
- and
- utilizing the plurality of outputs for a link budget calculation including determination of amplifier settings, launch powers, channel occupancy, spacing, and performance margin for the optical span.
A method comprising steps of:
- receiving inputs comprising a Raman gain target for a Raman amplifier, fiber parameters including any of fiber attenuation coefficient, fiber span loss, and fiber loss margin, and fiber characteristics including any of fiber effective area, zero chromatic dispersion wavelength, fiber attenuation at Raman pump frequencies, and a peak Raman gain coefficient value, each for an optical span;
- analyzing the inputs with a deep neural network (DNN) having a plurality of layers;
- providing a plurality of outputs from the DNN, the outputs including any of a calculated Raman gain spectrum, an effective noise figure, an incremental multi-path interference (ΔMPI), and an Optical Service Channel (OSC) gain;
- and
- utilizing the plurality of outputs for a link budget calculation including determination of amplifier settings, launch powers, channel occupancy, spacing, and performance margin for the optical span.
Dependencies are inferred from the claim text.