Abstract: A method and system for a training manager for generating diagnosis models for mobile networks. The method including selecting automatically a set of parameters for an action to be simulated in a simulated network where the simulated network replicates a target network for a diagnosis model, executing a simulation of an operation of a network based on the set of parameters of the action to generate an output of the simulation including a set of network performance metrics, transforming output of the simulation into training data for the diagnosis model, training the diagnosis model with the training data, and outputting the diagnosis model for the target network, in response to the diagnosis model meeting a designated quality threshold.

Abstract: 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.

Abstract: A method of a node in a radio access network that optimizes radio access network operations. The method of the node includes determining a topology of the radio access network, exchanging optimization information and network metric information with neighbor nodes in the topology, determining an updated local configuration for the node based on a negotiated optimization with the neighbor nodes, and updating an optimization function based on collected updated network metric information of the node executing the updated local configuration.

Abstract: 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.

Abstract: A scheme (300) 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 (102) requiring assistance with respect to a given task generating (302) one or more suitable queries. Responsive thereto, real-time context data is gathered (304), which may comprise data relating to the work environment. A remote expert (118) may generate (306) appropriate guidance relevant to the task and assistance query, which may be rendered as AR content (308) for worker consumption by using an AR rendering module (112). The worker (102) consumes or uses the AR content for performing (310) an action with respect to the task. The performance of the worker (102) may be used as a measure to improve the AR rendering in automated fashion (312, 314) using one or more machine learning modules (114).

Abstract: A method by one or more network devices for determining parameter values for a base station of a cellular network operating in a wireless band that is shared with one or more wireless access points. The parameter values are determined to optimize network performance in a manner that allows for fair coexistence between the base station and the one or more wireless access points. The method includes determining, using a machine learning system, parameter values for the base station based on proximity information and activity information for the one or more wireless access points, causing the base station to be configured with the parameter values, determining a measure of how configuring the base station with the parameter values affected network performance based on comparing the level of network performance before and after the base station was configured with the parameter values, and training the machine learning system using the measure.

Abstract: An apparatus and method are described herein for training a similarity model that is used to predict similarity between item pairs. In one example, the apparatus is configured to train (build) the similarity model by using machine learning (e.g., a multivariate multiple liner regression process) that utilizes an independent variable including metadata (e.g., title, genre, writer, plot keywords) associated with training items, and two dependent variables including user contributed similarity scores for training item pairs, and collaborative filtering similarity scores for the training item pairs. Then, the apparatus uses the trained model to predict similarity between items.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method determines outlier inputs for a machine learning system. The method includes receiving a classification and activation values of a trained classifier or a first input processed by the trained classifier, determining whether an entropy score derived from the first input is below a threshold entropy-based distance metric, and changing the classification in response to the entropy score not being below the threshold.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A mobile device automatically determines which location of a plurality of locations of a vehicle a user of the mobile device is located at without knowing assistance of the user. The mobile device determines that the mobile device is likely in a moving vehicle and that the user is actively interacting with the mobile device. The mobile device captures an image via an optical sensor of the mobile device without assistance from the user for the capture. The mobile device determines a location of the user within the vehicle based upon an analysis of the image. The analysis includes an execution of a model that utilizes at least the captured image as an input to determine the location. The mobile device, after determining the location, performs one or more actions.

Abstract: A scheme (300) 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 (102) requiring assistance with respect to a given task generating (302) one or more suitable queries. Responsive thereto, real-time context data is gathered (304), which may comprise data relating to the work environment. A remote expert (118) may generate (306) appropriate guidance relevant to the task and assistance query, which may be rendered as AR content (308) for worker consumption by using an AR rendering module (112). The worker (102) consumes or uses the AR content for performing (310) an action with respect to the task. The performance of the worker (102) may be used as a measure to improve the AR rendering in automated fashion (312, 314) using one or more machine learning modules (114).

Abstract: A method by one or more network devices for determining parameter values for a base station of a cellular network operating in a wireless band that is shared with one or more wireless access points. The parameter values are determined to optimize network performance in a manner that allows for fair coexistence between the base station and the one or more wireless access points. The method includes determining, using a machine learning system, parameter values for the base station based on proximity information and activity information for the one or more wireless access points, causing the base station to be configured with the parameter values, determining a measure of how configuring the base station with the parameter values affected network performance based on comparing the level of network performance before and after the base station was configured with the parameter values, and training the machine learning system using the measure.

Abstract: 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.

Abstract: A method is implemented by a network device for determining parameter values for a base station of a cellular network. The base station operates in a wireless band that is shared with one or more wireless access points. The parameter values are determined to optimize network performance while ensuring fair coexistence between the base station and the one or more wireless access points. The method includes obtaining proximity information for the one or more wireless access points relative to the base station, obtaining activity information for the one or more wireless access points, and determining parameter values for the base station based on the proximity information and the activity information.