Enhanced Uncertainty Management For Optical Communication Systems

Patent metadata

Field	Value
Publication number	US-2022343176-A1
Application number	US-202117236678-A
Country	US
Kind code	A1
Filing date	Apr 21, 2021
Priority date	Apr 21, 2021
Publication date	Oct 27, 2022
Grant date	—

Abstract

Margin hedging in optical communication systems allows for overhead within the optical system. A machine learning model can be trained using the output of a physics based simulation of the optical system as well as the features of the optical system. A trained machine learning model can adjust the results of a physics based simulation of an optical network to more accurately match the adjusted simulation results to the “true” performance of the optical network. The margins of the optical communication system can be more tailored to the true performance of a designed or planned optical system.

First claim

1 . A method for predicting link design margins, the method comprising:
obtaining a plurality of link features associated with an optical link; simulating the optical link with a first model based on the plurality of link features to produce a first value; obtaining empirically one or more performance metrics associated with the optical link; calculating a second value based on the one or more performance metrics; determining one or more prediction error values based on the first value and the second value; training or updating a machine learning model using the plurality of link features and at least one of the one or more prediction error values or the one or more performance metrics.

2 . The method of claim 1 further comprising providing a predicted link capacity margin for a new optical link based on one or more prediction error values.

3 . The method of claim 1 further comprises determining variability of each of the one or more prediction error values for each of the plurality of link features.

4 . The method of claim 1 wherein the one or more performance metric comprises a generalized signal to noise ratio.

5 . A method for predicting link capacity margins, the method comprising:
simulating the optical link with a first model based on a plurality of link features to produce a first simulated value; providing to a trained machine learning model the plurality of link features and the first simulated value; and outputting by the trained machine learning model an error range associated with the first simulated value.

6 . The method of claim 5 further comprising determining a margin based on the output of the trained machine learning model.

7 . The method of claim 5 wherein the error range is based on trained variability values.

8 . The method of claim 7 further comprising determining a margin for the optical link based on the predicted error range.

9 . The method claim 5 further comprising providing as an output a relationship between generalized signal to noise ratio and frequency for at least one set of link features.

10 . The method of claim 5 wherein the set of link features is one of a multiplexer, amplifier, or optical fiber.

11 . A non-transient computer readable medium containing program instructions, the instructions when executed perform the steps of:
simulating the optical link with a first model based on a plurality of link features to produce a first simulated value;
providing to a trained machine learning model the plurality of link features and the first simulated value; and
outputting by the trained machine learning model an error range associated with the first simulated value.

12 . The non-transient computer readable medium of claim 11 further comprising determining a margin based on the output of the trained machine learning model.

13 . The non-transient computer readable medium of claim 12 wherein the error range is based on trained variability values

14 . The non-transient computer readable medium of claim 11 further comprising providing as an output a relationship between generalized signal to noise ratio and frequency for at least one set of link features

15 . The non-transient computer readable medium of claim 11 further comprising providing as an output a relationship between generalized signal to noise ratio and frequency for at least one set of link features.

16 . The non-transient computer readable medium of claim 11 wherein the set of link features contains at least one of a multiplexer, amplifier, or optical fiber.

17 . The non-transient computer readable medium of claim 11 wherein the trained machine learning model is trained on a sets of data, each set of data associated with an optical network, each set of data comprising (i) a simulated value for the optical network, (ii) a set of link features, and (iii) empirical data from the optical network.

Assignees

• Google Llc

Inventors

• Schmogrow Rene Marcel
• Cantono Mattia

Classifications

• H04L43/50

  Testing arrangements · CPC title

• H04L41/0896

  Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities (flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76) · CPC title

• G06N20/00

  Machine learning · CPC title

• H04L43/0876

  Network utilisation, e.g. volume of load or congestion level · CPC title

• H04L41/142

  using statistical or mathematical methods · CPC title