Telefonaktiebolaget Lm Ericsson (publ)

Julien Forgeat

Meral Shirazipour

Eugen Feller

Richard Carlsson

Vladimir Katardjiev

Dmitri Krylov

Vincent Huang

Alvin Jude Hari Haran

Qingyan Liu

Muhammad Mudassar Nazar

Ming Xia

Jaeseong Jeong

Tomas Goldbeck-Löwe

Ricardo Souza

Sky Zhao

Michael Liljenstam

Tong Yu

Priya Krishnan Sundararajan

Gechuan Qiu

Per Karlsson

Hasan Farooq

Shruti Bothe

Siyan Zhao

A method generates synthetic data by a data collection system where the synthetic data meets a first threshold for accuracy and a second threshold for protecting sensitive data from recovery from the synthetic data. The method includes collecting data including sensitive data and non-sensitive data, executing a first machine learning model to generate the synthetic data from the collected data where the synthetic data meets the first threshold, executing a second machine learning model to update the synthetic data to meet the second threshold, checking whether the updated synthetic data meets the first threshold, releasing the updated synthetic data where the first threshold is met, and re-executing the first machine learning model and second machine learning model to update the synthetic data where the first threshold is not met during the checking.

Using generative adversarial networks (GANs) to enable sharing of sensitive data

According to some embodiments, a method performed by a network node for validating minimization of drive test (MDT) reports comprises: receiving a MDT report generated by a wireless device: determining to perform validation on the received MDT report: correlating the MDT report with MDT reports from one or more wireless devices in proximity to the wireless device to determine a first correlation value; determining a trust score for the MDT report based on one or more correlation values, the one or more correlation values at least comprising the first correlation value; determining whether the trust score is below a validation threshold; and upon determining the trust score is below the validation threshold, performing a corrective action with respect to the received MDT report.

Minimization of drive test report validation

A system, method and architecture for network-assisted scanning of a surrounding environment. In one example arrangement, the system is operative for receiving one or more pieces of environmental data from a plurality of collection agents, each collection agent operating with a plurality of sensors configured to sense data relating to a respective location area of an environment, wherein the one or more environmental data is received responsive to determining that a portion of the environmental data is necessary for updating a consumption point report. Based on the received data, an updated consumption point report may be generated, which may be transmitted to a data consumption point.

Network-assisted scanning of a surrounding environment

A system, method and architecture for providing driving recommendations to a vehicle based on network-assisted scanning of a surrounding environment. In one embodiment, the system is operative for receiving environmental data from one or more collection agents, each operating with a plurality of sensors configured to sense data relating to the environment. At least one collection agent is operative in association with the vehicle for providing vehicular condition data and vehicular navigation data. Road condition information and obstacle condition information relating to a road segment, the vehicle is traversing may be obtained. Responsive to the road condition/navigation data, vehicle type and positioning, etc., a routing path over the road segment may be determined. In one configuration, the routing path may be presented via a display (e.g., AR/MR) associated with the vehicle.

Driving assistance system and method

A scheme () for facilitating automated AR-based rendering with respect to expert guidance or assistance provided in a connected work environment based on contextualization is disclosed. In one aspect, a method comprises a worker () requiring assistance with respect to a given task generating () one or more suitable queries. Responsive thereto, real-time context data is gathered (), which may comprise data relating to the work environment. A remote expert () may generate () appropriate guidance relevant to the task and assistance query, which may be rendered as AR content () for worker consumption by using an AR rendering module (). The worker () consumes or uses the AR content for performing () an action with respect to the task. The performance of the worker () may be used as a measure to improve the AR rendering in automated fashion () using one or more machine learning modules ().

Automated augmented reality rendering platform for providing remote expert assistance

A system, method and architecture for network-assisted scanning of a surrounding environment. In one example arrangement, the system is operative for receiving real world object identification and spatial mapping data relative to a plurality of real world scenarios sensed by sensing points, each operating as a collection agent configured to collect environmental data in association with a plurality of sensors. The data may be prioritized responsive to at least one of a determination of RAN resource capacity available to the collection agent, e.g., compute resources, storage resources, radio transmission resources, cost of the transmission capacity available to the collection agent, etc. in view of urgency/relevance of the data. Responsive to prioritizing the environmental data, one or more pieces of the environmental data may be transmitted to a nearest edge compute location and/or a cloud-based datacenter network node.

A method performed by a first network node operating in a communications network is disclosed. The method is for handling a performance of a radio access network (RAN) including one or more radio network nodes. The first network node determines a configuration of one or more parameters in the RAN based on one or more machine-implemented reinforcement learning (RL) procedures to optimize the performance of the RAN based on the one or more parameters. The RL procedures are further based on at least one of: i) one or more physical characteristics of a deployment of the RAN, ii) one or more radio characteristics of the RAN, and iii) a location of users or traffic load in the RAN. The first network node then initiates providing one or more indicators of the determined configuration to a second network node.

First network node, third network node, and methods performed thereby, for handling a performance of a radio access network

A system, method and architecture for providing highly granular driving recommendations to a vehicle based on network-assisted scanning of a surrounding environment. In one embodiment, the system is operative for receiving environmental data from one or more collection agents, each operating with a plurality of sensors configured to sense data relating to the environment. At least one collection agent is operative in association with the vehicle for providing vehicular condition data and vehicular navigation data. The system is configured to calibrate the vehicle to identify a vehicle type responsive to the vehicular condition/navigation data. Road condition information and obstacle condition information relating to a road segment, the vehicle is traversing may also be obtained. Responsive to the road condition/navigation data, vehicle type and positioning, etc., an optimal racing line path over the road segment may be determined. In one configuration, the optimal racing line path may be presented via a display (e.g., AR/MR) associated with the vehicle.

System and method for providing precise driving recommendations based on network-assisted scanning of a surrounding environment

A system and method are described herein for improving content-based (CB) filtering to find other items similar to a particular item. For example, the system and method can operate to (1) collect ground truth from users, experts or non-experts for a subset of items in a library; (2) build similarity model(s) against the collected ground truth and evaluate this/these similarity model(s) for generalizability to all items in the library, and (3) build recommendations for users based on knowledge elicited from the users themselves, combined with the similarity scores in the built similarity model(s).

System and method for recommending semantically similar items

Exemplary techniques for analytics-driven hybrid concurrency control in clouds are disclosed that include a hybrid resource allocation module that can concurrently utilize an optimistic allocation scheme alongside a pessimistic allocation scheme. Machine learning techniques utilizing previous activity history of applications can be used to train a cluster model that is integrated by a hybrid resource allocation module to classify applications in either a pessimistic cluster or an optimistic cluster that identifies under which scheme requests from the applications will be processed.

Techniques for analytics-driven hybrid concurrency control in clouds

A sensor data system, method and computer program configured to handle sensor data are disclosed. The system includes a reception unit adapted to receive sensor data, a storing unit adapted to store at least some of the received sensor data in an observations database, a recording unit adapted to record usage of the sensor data, and a relevance determination unit adapted to determine the relevance of the sensor data based on the recorded usage. A decision unit is adapted to remove excess sensor data with determined relevance below a limit from the observations database, and to retain relevant sensor data with determined relevance above the limit in the sensor database. Sensor data in an observations database may thereby be limited to a desired size, by removal of excess sensor data from the database.

Method, computer program, computer program product and system for handling sensor data

According to one embodiment, a method in a server end station of a cloud for determining whether a service level agreement (SLA) violation has occurred or is expected to occur is described. The method includes receiving one or more insight models from an insight model builder, wherein each insight model is a based on one or more metrics previously collected from a virtualized infrastructure, and wherein each insight model models a particular behavior in the virtualized infrastructure and receiving real time metrics from the virtualized infrastructure. The method further includes for each of the one or more insight models, determining based on the received real time metrics that one or more services on the virtualized infrastructure is in an abnormal state or is expected to enter the abnormal state, wherein the abnormal state occurs when the insight model indicates that the associated modeled behavior violates a predetermined indicator.

System and method for analytics-driven SLA management and insight generation in clouds

Methods and apparatus are provided for collecting and detecting service area (SA) swapping noise in a wireless network including a plurality of SAs serving a plurality of devices. By receiving SA updates from the stationary devices of the plurality of devices changes in SA identifiers of the SA updates associated with each stationary device are detected. For each stationary device, if two or more SAs are detected to have changed for the stationary device, then these SAs are identified as contributing to SA swapping noise. With this in mind, SA transitions between the identified SAs for a non-stationary device of the plurality of devices are not interpreted as movement of the non-stationary device. However, SA transitions for the non-stationary device from these SAs to other SAs not contributing to SA swapping noise are interpreted as a change of location or movement of the non-stationary device.

Identification of service area swapping noise

A method in a server end station is described. The method includes performing an iteration of a rebalancing computation by selecting a set of one or more service sets for rebalancing, wherein the selecting the set of one or more service sets is based on service set constraints and host constraints; generating candidate solutions, wherein each candidate solution includes a randomized one-to-one mapping of each of the service sets to one of the hosts; performing one or more crossover operations on the candidate solutions; performing one or more mutation operations on the additional candidate solutions; selecting as a solution one of the candidate solutions that has a best fitness score, wherein a fitness score for a candidate solution is calculated based on the distribution of resources resulting from and number of migrations needed for the candidate solution; and repeating the iteration of the rebalancing computation an additional number of times.

Growing community of inventors

San Jose, CA, United States of America

Julien Forgeat