Abstract: A method of a collaboration server for identifying cells of a mobile network for machine learning collaboration is provided. The mobile network includes a plurality of cells. The method includes managing collection of features for the plurality of cells to generate at least one feature vector for each of the plurality of cells, determining a cluster of cells within the plurality of cells based on similarity in feature vectors between at least two cells in the plurality of cells, sending cluster information to each cell of the cluster, receiving cluster pre-check information from each cell of the cluster, and determining a first cell and a second cell in the cluster to collaborate for machine learning based on the received pre-check information.

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 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: A route prediction system implements a method that utilizes real-time mobile communication network data including receiving real-time mobile communication network data. The method further includes aggregating the real-time mobile communication network data with historical data to form a training set, filtering the training data set for relevance to servicing route predictions for connected vehicles, training a plurality of offline models using the filtered training data set, and selecting a best performing propagation model from the plurality of offline models.

Abstract: A method of a route prediction system utilizing real-time mobile communication network data. The method includes receiving a route request originating from a connected vehicle, the route request identifying a route, determining segments for the route, localizing mobile communication network resources to the segments, determining key performance indicators for the segments based on at least a current offline model, and sending predicted service level indicators (SLIs) for the segments to the connected vehicle.

Abstract: A method provides route information to a connected vehicle in a brokered information prediction system. The method includes determining available mobile communication networks that can provide real-time mobile communication network data, querying a first available mobile communication network to provide key performance indicators for a route for a connected vehicle, determining service level indicators for the route based on received key performance indicators for the route where the key performance indicators meet defined thresholds, and sending predicted service level indicators for the route to the connected vehicle.