公开(公告)号：US20200313788A1

公开(公告)日：2020-10-01

申请号：US16902197

申请日：2020-06-15

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Martin Birk ,  David Frederick Lynch ,  Gaurav Thakur ,  Simon Tse

IPC: H04J14/02 ,  H04L12/751 ,  H04B10/079

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

公开(公告)号：US20210306086A1

公开(公告)日：2021-09-30

申请号：US17347383

申请日：2021-06-14

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Martin Birk ,  David Frederick Lynch ,  Gaurav Thakur ,  Simon Tse

IPC: H04J14/02 ,  H04L12/751 ,  H04B10/079

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

公开(公告)号：US11316607B2

公开(公告)日：2022-04-26

申请号：US17347383

申请日：2021-06-14

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Martin Birk ,  David Frederick Lynch ,  Gaurav Thakur ,  Simon Tse

IPC: H04B10/00 ,  H04J14/02 ,  H04L45/00 ,  H04B10/079 ,  H04J14/00

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

公开(公告)号：US20200092026A1

公开(公告)日：2020-03-19

申请号：US16135844

申请日：2018-09-19

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Martin Birk ,  David Frederick Lynch ,  Gaurav Thakur ,  Simon Tse

IPC: H04J14/02 ,  H04B10/079 ,  H04L12/751

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

公开(公告)号：US11038616B2

公开(公告)日：2021-06-15

申请号：US16902197

申请日：2020-06-15

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Martin Birk ,  David Frederick Lynch ,  Gaurav Thakur ,  Simon Tse

IPC: H04B10/00 ,  H04J14/02 ,  H04L12/751 ,  H04B10/079 ,  H04J14/00

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

公开(公告)号：US10686544B2

公开(公告)日：2020-06-16

申请号：US16135844

申请日：2018-09-19

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Martin Birk ,  David Frederick Lynch ,  Gaurav Thakur ,  Simon Tse

IPC: H04J14/00 ,  H04J14/02 ,  H04L12/751 ,  H04B10/079 ,  H04B10/00

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.