At&t Communications Services India Private Limited

At&t Intellectual Property I, LP

At&t Global Network Services (u.k.) B.v.

Mritunjay Pandey

Subhash Kapoor

Lynn Rivera

Donald Charles Jeffery

Lalena Aria

Saurav Paira

Karunakar Revuri

Somnath Mallick

Carl Thomas

Aspects of the subject disclosure may include, for example, predicting dense wavelength division multiplexing (DWDM) path outage prediction and recommendation of alternate communication paths. Parameterized DWDM signals are launched, and resulting network characteristics are determined. The parameters and characteristics are used to train one or more machine learning models subsequently used to predict outages and recommend alternate paths. Other embodiments are disclosed.

DWDM path outage prediction and alternative path recommendation

Aspects of the subject disclosure may include, for example, a device, including: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: 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. Other embodiments are disclosed.

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

Aspects of the subject disclosure may include, for example, identifying network elements of a wavelength division multiplexing (WDM) domain of an optical waveguide communication system that includes a group of optical-add-drop multiplexor (OADM) devices. Operations are observed for the WDM domain configured to deliver communication services configured for simultaneously transporting independent signals across a network of single optical waveguides. A demand for optical waveguide communication services is determined and the WDM network is configured according to the optical fiber communication link requirement and according to the observations. The configured WDM network includes at least one OADM device of the group of OADM devices configured to provide a WDM cross-frequency network path of the configured WDM network. Other embodiments are disclosed.

Dynamic cross-frequency path configuration for wavelength division multiplexing networks

Devices, computer-readable media, and methods for automatically configuring network elements in multi-vendor and multi-domain topologies. In one example, a method includes determining a need of a communications network, where a topology of the communications network includes a plurality of network functions from at least two different vendors, predicting a subset of the plurality of network functions and respective configuration parameter values for network functions in the subset, that are expected to support the need of the communications network, and modifying the configurations of the network functions in the subset to reflect the respective configuration parameter values.

Automatically configuring network elements in multi-vendor and multi-domain topologies

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Bengaluru, India

Mritunjay Pandey