Viavi Solutions UK Limited

Viavi Solutions Inc.

Jds Uniphase Corporation

Christopher Michael Murphy

Howard John Thomas

Peter Kenington

Gareth James Smith

Michael Joseph Flanagan

Takai Eddine Kennouche

Stefan Ulrich Thiel

David Charles Padfield

Kexuan Sun

John Joseph Holden

Walter Featherstone

Agustin Pozuelo

Michael Joseph Flnagan

Baruch Friedman

Philip Geoffrey Claridge

Mohsen Zadeh-Koochak

Mihai Stanciuc

Peter Randall

Meng Wang

Dave Padfield

Matthew James W Brown

Payam Maveddat

Philip Geoffery Claridge

Nicolas G Cotanis

Frank Mamani

Robert Clark

A device may receive real mobile radio data identifying measurements of radio transmissions of base stations and user devices of a mobile radio environment in a geographical area, and may receive network topology data associated with the geographical area. The device may utilize, based on the network topology data, a machine learning feature extraction approach to generate a representation of invariant aspects of spatiotemporal predictable components of the real mobile radio data, and may generate, based on the representation of invariant aspects, stochastic data that includes a probability that a radio signal will be obstructed. The device may utilize the stochastic data to identify a realistic discoverable spatiotemporal signature, and may train or evaluate a system to manage performance of a mobile radio network based on the realistic discoverable spatiotemporal signature.

Utilizing invariant shadow fading data for training a machine learning model

A device may receive a first type of data identifying measurements associated with user devices and/or base stations of a mobile radio environment, and a second type of data identifying spatiotemporal behavior associated with the user devices. The device may train a first model, with the first type of data, to generate a trained first model that yields dimensionality-reduced spatiotemporal characteristics of the first type of data, and may train a second model, with the second type of data and the dimensionality-reduced spatiotemporal characteristics, to generate a trained second model. The device may receive particular data identifying measurements associated with a user device and/or base stations, and may process the particular data, with the trained first model, to generate a dimensionality-reduced spatiotemporal characteristic of the particular data. The device may process the dimensionality-reduced spatiotemporal characteristic, with the trained second model, to predict a spatiotemporal behavior of the user device.

Utilizing machine learning models to estimate user device spatiotemporal behavior

A device may receive mobile radio data identifying utilization of a mobile radio network that includes base stations and user devices in a geographical area. The device may process the mobile radio data, with a machine learning feature extraction model, to generate a behavioral representation, that is probabilistic in nature, of invariant aspects of spatiotemporal utilization of the mobile radio network. The device may generate one or more instances of the spatiotemporal utilization of the mobile radio network that reflects the probabilistic nature of a spatiotemporal predictable component of the behavioral representation. The device may utilize the one or more instances of the spatiotemporal utilization of the mobile radio network as a dataset for training or evaluating a system to manage performance of the mobile radio network.

Utilizing invariant user behavior data for training a machine learning model

A navigation platform may receive information identifying an origin location and a destination location associated with an autonomous vehicle. The navigation platform may identify one or more candidate route segments for a navigation route from the origin location to the destination location. The navigation platform may receive, based on transmitting a query to a geolocation server, wireless communication coverage information for the one or more candidate route segments. The navigation platform may select, based on the wireless communication coverage information, one or more route segments, from the one or more candidate route segments, for the navigation route. The navigation platform may transmit, to the autonomous vehicle, an instruction to navigate the autonomous vehicle along the navigation route.

Wireless communication coverage based vehicle routing

A device may perform an iteration of an optimization procedure. The device may apply a smoothing technique to a value relating to the optimization procedure after performing the iteration of the optimization procedure. The device may selectively terminate the optimization procedure based on applying the smoothing technique to the value relating to the optimization procedure. The device may provide information identifying a result of the optimization procedure based on selectively terminating the optimization procedure.

Automatic optimization procedure termination using a smoothing-based technique

A device may detect a service condition associated with a network. The device may determine a prioritization of cells of the network based on characteristics of one or more subscribers to the cells of the network. The characteristics may include at least one of: a service type characteristic, an application type characteristic, a mobility characteristic, a vector characteristic, an altitude characteristic, a position characteristic, a location type characteristic, a device type characteristic, a device capability characteristic, a connection characteristic, a subscription characteristic, a call type characteristic, a user profile characteristic, a terrain characteristic, or an operator relationship characteristic. The device may perform an alteration to one or more network parameters based on the prioritization of cells of the network to reduce an impact of the service condition.

Network optimization based on characteristic information regarding a network

A device may determine a set of orientations or relative displacements at a set of locations. The set of locations may include at least a first location and a second location. The device may determine a set of sensor measurements relating to one or more measurable parameters at the set of locations. The device may determine, based on the set of orientations or relative displacements at the set of locations and the set of sensor measurements relating to the one or more measurable parameters at the set of locations, a path relating to the set of locations. The device may perform an action based on determining the path.

Sensor-based geolocation of a user device

Techniques for mobile network geolocation are disclosed. In one particular exemplary embodiment, the techniques may be realized as a system for mobile network geolocation. The system may comprise one or more processors communicatively coupled to a mobile communications network. The one or more processors may be configured to determine that a mobile device is on a travel path. The one or more processors may also be configured to determine one or more candidate locations of the mobile device based on data associated with the travel path. The one or more processors may further be configured to determine at least one candidate route associated with the mobile device along the travel path comprised from one or more candidate locations.

Techniques for mobile network geolocation

A device may include one or more processors. The device may receive, via a plurality of data streams of a front haul link, a set of packets including information for transmission via an air interface. The set of packets may be associated with a data prioritization relating to transmission via the air interface. The set of packets may include information of a time-frequency resource element array. The device may reconstruct a set of time-frequency resource elements of the time-frequency resource element array based on the data prioritization of the set of packets. The device may transmit, via the air interface, the time-frequency resource element array.

Techniques for providing front-haul data awareness

A method and apparatus for generating reference signatures for use in geolocation in a cellular wireless communication system is provided. A first signature for a mobile communication unit comprises location information, a timestamp, and radio frequency measurement information obtained by a mobile communication unit at a location, but does not contain identification information for the mobile communication unit. The first signature is compared to signatures in a database, to identify a second signature that has a timestamp and radio frequency measurement information that correspond to those of the first signature. A reference signature is created by combining at least a part of the first signature with at least a part of the second signature. Repetition of the comparison and combination steps creates a database of reference signatures, for use in geolocating other received call signatures.

Mobile geolocation

Techniques for multiple pass geolocation are disclosed. In one particular exemplary embodiment, the techniques may be realized as a system for multiple pass geolocation. The system may comprise one or more processors communicatively coupled to a mobile communications network. The one or more processors may be configured to conduct a first geolocation pass to identify one or more geolocation estimates under consideration. The one or more processors may also be configured to conduct at least one additional geolocation pass to refine one or more geolocation estimates under consideration. The one or more processors may further be configured to determine an approximate location of one or more mobile devices within an estimated coverage area of a network based on at least the first geolocation pass and the at least one additional geolocation pass.

Techniques for multiple pass geolocation

A cellular wireless communications system and method of generating reference signatures for use in geolocation are provided. The cellular wireless communications system identifies a sub-area within the coverage area of a cellular wireless communications system, the sub-area having a density of radio frequency measurements that is below a threshold value. A user of a subscriber mobile communication unit is influenced to enter the sub-area, the subscriber mobile communication unit being a subscriber to the cellular wireless communications system. The cellular wireless communications system obtains radio frequency measurements from the subscriber mobile communication unit in the sub-area, and generates reference signatures and/or location estimates from the radio frequency measurements. The reference signatures may be used to geolocate connections made by other mobile communication units within the sub-areas.

Mobile communication system

A method and an apparatus for generating reference signatures in a cellular wireless communications system are provided. A first signature is obtained for a mobile communication unit at a first location. The first signature comprises first location information, a first timestamp, and radio frequency measurement information, but lacks identification information. The first signature is compared to signatures in a database, to identify a second signature having location information and a timestamp corresponding to the first location information and first timestamp. A reference signature is created by combining at least part of the first signature, such as the radio frequency information, with the second signature. The first signature is from an anonymized call. The second signature may be from an application operational on the communication unit, or from a social media website, and may comprise identification information for the communication unit or user.

Growing community of inventors

Bath, United Kingdom

Christopher Michael Murphy