Machine learning techniques for selecting paths in multi-vendor reconfigurable optical add/drop multiplexer networks

US10686544B2 · kind B2 · utility

Assignee

• AT&T Intellectual Property I, L.P. · US

Inventors

• Martin Birk · Holmdel, US
• David Frederick Lynch · Freehold, US
• Gaurav Thakur · Matawan, US
• Simon Tse · Holmdel, US

Key dates

• Filing date: Sep 19, 2018
• Grant date: Jun 16, 2020
• Expiry date: Sep 19, 2038

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04L45/123
• WIPO field: Telecommunications
• WIPO sector: Electrical engineering

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.