Ciena Corporation

Maryam Amiri

Petar Djukic

Todd D Morris

Yinqing Pei

John Wade Cherrington

Firouzeh Golaghazadeh

Ivan Radovic

Systems, methods, and computer-readable medium for forecasting a time-series are provided. In one implementation, a method is configured to include a step of providing a time-series to a neural network including one or more branches for processing one or more portions of the time-series. In each of the one or more branches, the method includes separating the respective portion of the time-series into individual portions and applying each portion to a respective sub-branch of a plurality of sub-branches of the one or more branches. The method also includes generating forecasting coefficients for each output time point in each of the respective sub-branches and providing a forecast of the time-series based at least on the forecasting coefficients.

Forecasting time-series data in a network environment

Systems and methods are provided for utilizing polarization parameters obtained from an optical network to determine vibrations in optical fibers using coherent optics equipment and machine learning techniques. A method, according to one implementation, includes the step of obtaining a time-series dataset that includes measurements of polarization characteristics of light traversing an optical fiber of an optical network. The method also includes the step of detecting vibration characteristics of the optical fiber based on the time-series dataset. In some implementations, the time-series dataset may be a multi-variate dataset and the polarization characteristics may be related to transients in a State of Polarization (SOP). The SOP, for example, may be represented by an amplitude and a phase of an electric field vector and may be defined as having one of a linear polarization, elliptical polarization, and circular polarization.

Utilizing polarization characteristics to detect vibrations in optical fibers

Deep Neural Networks (DNNs) for forecasting future data are provided. In one embodiment, a non-transitory computer-readable medium is configured to store computer logic having instructions that, when executed, cause one or more processing devices to receive, at each of a plurality of Deep Neural Network (DNN) forecasters, an input corresponding to a time-series dataset of a plurality of input time-series datasets. The instructions further cause the one or more processing devices to produce, from each of the plurality of DNN forecasters, a forecast output and provide the forecast output from each of the plurality of DNN forecasters to a DNN mixer for combining the forecast outputs to produce one or more output time-series datasets.

Forecasting routines utilizing a mixer to combine deep neural network (DNN) forecasts of multi-variate time-series datasets

Systems and methods include receiving network topology information of a network including a plurality of routers; receiving link measurements defining bandwidth on links in the network; determining routes in the network based on the network topology information; and utilizing the routes and the link measurements to determine an estimate of an initial traffic matrix that includes the bandwidth between origin routers and destination routers.

Estimating a traffic matrix of a communication network using network topology features

An optical network element includes a connection to an optical fiber in an optical line system including a coherent receiver; a microphone configured to detect sound; and circuitry connected to the microphone and configured to cause transmission of information related to sounds detected by the microphone to a receiver at an end of the optical line system, wherein the transmission is over the optical fiber in the optical line system to the coherent receiver. The optical network element can include a polarization controlling device connected to the circuitry and configured to modulate a state-of-polarization (SOP) envelope for the transmission.

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Ottawa, Canada

Maryam Amiri