Ciena Corporation

Andrew D Shiner

Maurice M O'Sullivan

Michael Andrew Reimer

David W Boertjes

Shahab Oveis Gharan

Kim Bryon Roberts

Alex W MacKay

Michael Hubbard

Fangyuan Zhang

Andrzej Borowiec

Michael Y Frankel

Eric Maniloff

John K Oltman

Hamid Ebrahimzad

Yinqing Pei

Douglas W Charlton

James St Harley

Mahmoud Taherzadehboroujeni

Benoît Châtelain

Frederic Poulin

Mohammad Ebrahim Mousa Pasandi

Francisco Javier Vaquero-Caballero

Seb Savory

David Ives

Maurice O'sullivan

A system is configured to measure () a forward error correction (FEC) decoding property () associated with applying FEC decoding () to FEC-encoded bits or symbols at a receiver device () deployed in a communication network (). The system is configured to provide () an assessment of operating conditions of the receiver device based on the FEC decoding property. The FEC decoding property comprises, for example, a distribution of a number of iterations of a FEC decoding operation applied to a plurality of FEC blocks processed within a period of time. In some examples, the FEC decoding property comprises any one of heat, temperature, current, voltage, active clock cycles, idle clock cycles, activity of parallel engines, activity of pipeline stages, and input or output buffer fill level of the FEC decoding. The assessment is based, for example, on a comparison between the FEC decoding property and reference FEC data ().

Assessing operating conditions of a receiver in a communication network based on forward error correction decoding properties

Systems and methods of optimizing launch power for each span in an optical system with a plurality of spans are provided. In an embodiment, a method includes varying power on a span under test of the plurality of spans; observing performance measurements related to of one or more channels, at corresponding optical receivers; and one of setting launch power for the span under test and repeating the varying and observing, responsive to the observed measurements.

Localized span launch power control for network level optimization

Systems and methods include receiving (S) data for a plurality of elements associated with an optical network; determining (S) an incremental noise penalty for each element of the plurality of elements based on the received data; and storing (S) the incremental noise penalty for each element of the plurality of elements. The steps can further include determining (S) Signal-to-Noise Ratio (SNR) across an optical path in the optical network by concatenating associated incremental noise penalties for each element in the optical path along with corrections. The present disclosure includes a fast, nonlinear estimation process with improved accuracy for low loss spans compared to traditional closed-form GN models, as well as a method to determine the coherent nonlinear penalty in an arbitrary concatenation of mixed heterogeneous fibers which is not considered by existing fast nonlinear interference calculation methods.

Utilizing an incremental noise metric for rapid modeling of optical networks

A lazy graph construction with compression includes receiving a query related to a network; one of generating a site graph for the network and accessing the site graph already in memory; performing a search on the site graph based on the query; accessing data in a database and generating, in the memory, a plurality of sub-graphs using the data, for the query; and providing a solution to the query based on the search and a search of the plurality of sub-graphs. The site graph can be at a site level and includes network elements and associated connections in the network, and the plurality of sub-graphs can be at an intra-site level including any of component-to-component connections and intra-component connections.

Lazy graph construction with compression and a hybrid graph-relational model for representing a network topology

System and methods of measuring nonlinear interference (NLI) on a per-span basis in an optical system with a plurality of spans are provided. The method includes steps of varying power based on phase sensitive detection method on a span under test of the plurality of spans; observing total noise, at an optical receiver, from all of the plurality of spans; and isolating noise for the span under test from the total noise based on the varying power. The optical system can be in-service with one or more traffic-carrying channels, and the varying power is small enough on the span under test which does not impact the one or more traffic-carrying channels.

In-service characterization of nonlinear interference on a per-span basis

Systems and methods include monitoring users logging into a collaboration service for a corresponding online meeting; and assigning a plurality of the users to a virtual waiting room, separate from any corresponding online meeting, in advance of their corresponding online meeting to enable collaboration in the virtual waiting room. The systems and methods can further include notifying the plurality of users when their corresponding online meeting has started for the plurality of users to leave the virtual waiting room.

Virtual hallway conversations for remote collaboration

A communication system is configured to generate a perturbed signal by perturbing an amplitude of a spectrum of an original signal in one or more spectral regions, and to propagate the perturbed signal through components of the communication system. The communication system is further configured to obtain a measurement of the perturbed signal in a first spectral region of the one or more spectral regions following the propagation of the perturbed signal, and to calculate an estimate of an impairment associated with the communication system based on the measurement.

Estimation of communication system impairments using spectrally selective signal perturbations

Systems and methods include determining a current state of a network; determining a new state for the network having an improved cost relative to the current state; determining a defragmentation plan to move the network from the current state to the new state, the defragmentation plan including a sequence of steps; and, responsive to an event that presents an opportunity, implementing one or more steps of the sequence of steps. The implementing is conditioned on occurrence of the opportunity.

Opportunistic network defragmentation and optimization

Systems and methods include obtaining a network state of a network having a plurality of nodes interconnected by a plurality of links and with services configured between the plurality of nodes on the plurality of links; utilizing a reinforcement learning engine to analyze the services and the network state to determine modifications to one or more candidate services of the services to increase a value of the network state; and, responsive to implementation of the modification to the one or more candidate services, updating the network state based thereon. The modifications can include changes to any of routing, modulation, and spectral assignment to the one or more candidate services.

Reinforcement learning for optical network re-grooming

A system configured to perform a method for estimating external noise in a communication channel between a transmitter and a receiver is described. The method comprises obtaining a measurement of effective noise on decoded symbols at the receiver, the decoded symbols comprising noisy versions of symbols conveyed by a communication signal transmitted over the communication channel. The method further comprises storing a representation of a relationship between the effective noise, external noise in the communication channel, and one or more variable parameters. The method further comprises storing applicable values of the variable parameters, wherein each applicable value is associated with current properties of the transmitter or current properties of the receiver or both. The method further comprises calculating an estimate of the external noise in the communication channel using the effective noise, the applicable values of the variable parameters, and the representation of the relationship.

Estimation of external noise in a communication channel

A system is configured to measure a forward error correction (FEC) decoding property associated with applying FEC decoding to FEC-encoded bits or symbols at a receiver device deployed in a communication network. The system is further configured to provide an assessment of operating conditions of the receiver device based on the FEC decoding property. In one example, the FEC decoding property comprises a distribution of a number of iterations of a FEC decoding operation applied to a plurality of FEC blocks processed within a period of time. In some examples, the FEC decoding property comprises any one of heat, temperature, current, voltage, active clock cycles, idle clock cycles, activity of parallel engines, activity of pipeline stages, and input or output buffer fill level of the FEC decoding. In some examples, the assessment is based on a comparison between the FEC decoding property and reference FEC data.

Systems and methods include determining a defragmentation plan that changes a configuration of a network to move the network from a current state of operation to a new state of operation; updating any of the defragmentation plan and the new state of operation in response to an opportunity presented during operation of the network; and implementing one or more steps in the defragmentation plan based on the opportunity. The opportunity can be anticipated to occur at a future time and implementation of the one or more steps is conditioned on occurrence of the opportunity. For example, the opportunity can include any of a maintenance window, a network equipment change, a capacity change in the network, a change to demand in the network, an external event including weather, and changes in links in the network. Also, the opportunity can include a fault.

A receiver is configured to generate a digital signal representative of data conveyed by a communication signal detected at the receiver, and to apply digital signal processing to the digital signal, thereby generating a processed signal. The receiver is further configured to determine a relative noise estimate for the processed signal, and to load an amount of digital noise into the digital signal processing based on a difference between the relative noise estimate and a target. As a result of the digital noise loading, improved stability of at least one control loop in the receiver may be achieved.

Receiver noise loading for control loop stability

An optical transmitter () is operable to generate an optical signal () by modulating a number N of frequency divisional multiplexing (FDM) subcarriers using transformed digital signals which are determined by applying a pseudo FDM (pFDM) transformation to preliminary digital signals representative of multi-bit symbols. Rather than experiencing the effects of the number N of FDM channels, the optical signal experiences the effects of a different number M of pFDM channels, where M&#x2260;N. In some examples, the number M of pFDM channels is less than the number N of FDM channels, and frequency-dependent degradations may be averaged across different symbol streams. In other examples, the number M of pFDM channels is greater than the number N of FDM channels, and different symbol streams may experience different frequency-dependent degradations. An optical receiver () is operable to apply an inverse pFDM transformation to recover estimates of the multi-bit symbols.

Pseudo frequency division multiplexing

Systems and method include a detector communicatively coupled to a fiber span; and processing circuitry connected to the detector and configured to digitally sample and process an output of the detector, detect phase changes in the output, and identify an electrostrictive response of the fiber span based on the detected phase changes and based on a dependence of the detected phase changes with frequency. A property of the fiber span can be determined on the electrostrictive response. The property of the optical fiber can include one or more of optical fiber material type, optical fiber material property, optical fiber area, optical fiber geometry, optical fiber condition, optical fiber stress and strain, optical fiber temperature. and optical fiber radiation exposure.

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Ottawa, Canada

Andrew D Shiner