Abstract:
A software-defined network multi-layer controller (SDN-MLC) may communicate with multiple layers of a telecommunication network. The SDN-MLC may have an optimization algorithm that helps manage, in near real-time, the multiple layers of the telecommunication network.
Abstract:
Concepts and technologies for multi-lane optical transport network recovery are provided herein. In an embodiment, a system includes a multi-lane optical transceiver. The multi-lane optical transceiver can include a transmitter optical sub-assembly, a receiver optical sub-assembly, and a controller that includes a processor and a memory that stores computer-executable instructions that, in response to execution by the processor, cause the processor to perform operations. The operations can include detecting an optical interruption event corresponding to an optical lane within a multi-lane optical path. The operations can further include instantiating an optical protocol alarm based on the optical interruption event. The operations can further include generating an optical protocol message based on the optical protocol alarm. The operations can further include instructing a peer multi-lane optical transceiver to alter optical transmission along the multi-lane optical path based on the optical protocol message.
Abstract:
In accordance with one or more embodiments, a system can include a plurality of uninsulated conductors that is stranded together. The plurality of uninsulated conductors can form a hollow pathway that is bounded by internal conductive surfaces of at least three of the plurality of uninsulated conductors. The system can further include a communication device coupled to a first plurality of external conductive surfaces of the plurality of uninsulated conductors, where the communication device facilitates generating transmission signals at the first plurality of external conductive surfaces, and where the transmission signals induce electromagnetic waves that propagate along the hollow pathway without requiring an electrical return path. Other embodiments are disclosed.
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.
Abstract:
In accordance with one or more embodiments, a communication system, includes at least one launcher configured to generate first guided electromagnetic waves in response to a first communication signal conveying first data, wherein the first guided electromagnetic waves are guided by a structure within a cable and propagate within the cable via a plurality of guided wave modes without requiring an electrical return path; wherein the cable comprises a plurality of uninsulated conductors that are stranded together, wherein the plurality of uninsulated conductors form a plurality of interstitial areas that are bounded by conductive surfaces of at least three of the plurality of uninsulated conductors, and wherein the structure comprises one of the plurality of interstitial areas.
Abstract:
Aspects of the subject disclosure may include, for example, a system for exchanging electrical signals and guided electromagnetic waves between customer premises equipment and service provider equipment to provide uplink and/or downlink communication services. Other embodiments are disclosed.
Abstract:
An amplifier receives an optical signal including a number of labeled channels via a fiber. The amplifier determines a count of the labeled channels and a spectral distribution of the labeled channels. The amplifier adjusts a parameter of the amplifier based on the count of the labeled channels and the spectral distribution of the labeled channels. The amplifier amplifies the optical signal at an adjusted output gain resulting from adjusting the parameter of the amplifier.
Abstract:
Devices, computer-readable media and methods are disclosed for verifying that an optical transmit/receive device is correctly installed. For example, a processing system including at least one processor may activate a first light source of an optical transmit/receive device of a telecommunication network and detect a receiving of a light from the first light source at a port of an optical add/drop multiplexer of the telecommunication network. The processing system may then verify the optical transmit/receive device and the port of the optical add/drop multiplexer match a network provisioning order, when the receiving of the light from the first light source is detected, and may generate an indication that the optical transmit/receive device is correctly installed, when the optical transmit/receive device and the port of the optical add/drop multiplexer match the network provisioning order.
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.
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.