Rakuten Mobile, Inc.

Petrit Nahi

Madhukiran Medithe

Manoj Kumar

Suvindu Chinnam

Sagiv Draznin

Sharad Sriwastawa

Neighbor relation conflict prediction is performed by operations including receiving, from a serving MCG of a terminal, a measurement report of the terminal including a plurality of signal measurements associated with an SCG represented by a PCI and an ARFCN, identifying an unlisted SCG among the plurality of signal measurements, identifying one or more nearby MCG within a threshold distance of the serving MCG, counting a number of SCG in the NRT of each nearby MCG having the PCI and the ARFCN of the unlisted SCG, applying a classification model to the counted number of SCG and an MCG-PCI-ARFCN identifier representing the serving MCG, the PCI, and the ARFCN to obtain a binary value indicating whether provisioning the unlisted SCG with the serving MCG and the plurality of nearby MCG will result in PCI conflict.

Neighbor relation conflict prediction

A method and apparatus for mapping a virtual machine in a cloud infrastructure to a network port. The method includes: obtaining, by a smart monitoring device distinct from the virtual machine and a network appliance, a first location of the virtual machine in the cloud infrastructure; mapping, by the smart monitoring device, the first location of the virtual machine to a first source port in the network appliance and a first destination port in the network appliance; based on a determination that the virtual machine is not at the first location, obtaining, by the smart monitoring device, a second location of the virtual machine in the cloud infrastructure; and based on the determination that the virtual machine is not at the first location, mapping, by the smart monitoring device, the second location of the virtual machine to a second source port in the network appliance and a second destination port in the network appliance.

Apparatus and method for port mapping of virtual machines in cloud infrastructure

Polarity classifications of writing samples are obtained by sentiment analysis operations including embedding each word of a writing sample into a word vector based on surrounding words, extracting one or more features of the writing sample, applying a feature learning function to the one or more features, estimating a polarity of the writing sample based on output from the word learning function and output from the feature learning function, and training the word learning function and the feature learning function based on a loss function relating the estimated polarity to the word vector to produce a model for writing sample polarity classification.

Sentiment analysis

Systems and methods for analyzing the impact of outage events in a wireless communications network are disclosed. In some embodiments, the method includes obtaining outage data that indicates that an outage event occurred at a base station. A first set of either expected or real time connected user devices prior to the outage event are identified. A first subset of the first set of user devices that are wirelessly connected to any of the plurality of base stations other than the first base station during the outage event are identified. Furthermore, a second subset of the first set of user devices that are not connected to any of the plurality of base stations during the outage event are identified. In this manner, the impact of the outage event can be analyzed to allow for service providers to reduce the negative effects of future outage events.

Computer device, method, and non-transitory computer readable medium for analyzing the impact of outage events in a wireless communications network

A system includes a processor. The system also includes a memory connected to the processor, where the memory is configured to store executable instructions that, when executed by the processor, facilitate performance of operations, including: detecting an outage associated with devices of a cellular antenna, where the outage defines a cellular coverage hole; determining, in response to detecting the outage, one or more neighboring cellular antennas to the cellular antenna with the outage that are configured to provide coverage of the cellular coverage hole based upon a first criteria; determining for the neighboring cellular antennas satisfying the first criteria whether a second criteria is satisfied; and issuing an instruction, based on neighboring cellular antennas satisfying the second criteria, to up-tilt at least one of the one or more neighboring cellular antennas satisfying the second criteria.

Cell outage compensation system

A method includes first determining among a plurality of cells a first set of cells that are anomalous, second determining among remaining cells a second set of cells that are anomalous, and identifying a resulting set of anomalous cells being a combination of the first set and the second set of anomalous cells. The first determining is based on input data of a plurality of first features of each cell among the plurality of cells, and a first machine learning operation. The second determining is based on the input data of the plurality of first features of each cell among the remaining cells, and a second machine learning operation different from the first machine learning operation.

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Tokyo, Japan

Petrit Nahi