Techniques for clustering network elements of a telecommunication network are described herein. For instance, a machine learned model can cluster cells based on a combination of network data, user equipment data, location data, and map data. Clusters output from the machine learned model can be compared to each other to identify an underperforming cell, to generate recommendations that improve performance of a cell, and/or to determine performance benchmarks for cells or networks across different geographical regions.

Techniques for clustering network elements of a telecommunication network are described herein. For instance, a machine learned model can cluster cells based on a combination of network data, user equipment data, location data, and map data. Clusters output from the machine learned model can be compared to each other to identify an underperforming cell, to generate recommendations that improve performance of a cell, and/or to determine performance benchmarks for cells or networks across different geographical regions.

Aspects of the subject disclosure may include, for example, collecting and storing data about a communication environment including one or more communication networks wherein respective communication networks of the one or more communication networks employ respective communication technologies for communication with remote user devices in the communication environment, providing traffic forecasts of communication traffic, creating importance layers for potential build locations for equipment of the one or more communication networks, identifying desirable build locations among the potential build locations of the one or more communication networks based on the traffic forecasts of communication traffic and the importance layers, receiving user input defining planning parameters for the communication environment and determining respective target goal values for respective build options for the one or more communication networks based on the planning parameters, the identified desirable build locations and the traffic forecasts. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, collecting and storing data about a communication environment including one or more communication networks wherein respective communication networks of the one or more communication networks employ respective communication technologies for communication with remote user devices in the communication environment, providing traffic forecasts of communication traffic, creating importance layers for potential build locations for equipment of the one or more communication networks, identifying desirable build locations among the potential build locations of the one or more communication networks based on the traffic forecasts of communication traffic and the importance layers, receiving user input defining planning parameters for the communication environment and determining respective target goal values for respective build options for the one or more communication networks based on the planning parameters, the identified desirable build locations and the traffic forecasts. Other embodiments are disclosed.

System, methods, and embodiments described herein relate to controlling a vehicular micro cloud, which may include a plurality of vehicle members each having respective positions in a formation and in which two or more of the vehicle members collaboratively execute at least one micro cloud operation. Control may be implemented to monitor performance of the at least one micro cloud operation, monitor operational capabilities of the vehicle members, determine, based at least in part on the operational capabilities of the vehicle members, a change in formation position for at least one of the vehicle members, wherein the change in formation position improves the performance of the at least one micro cloud operation and transmit one or more commands to cause the vehicle members to execute the change in formation position.

System, methods, and embodiments described herein relate to controlling a vehicular micro cloud, which may include a plurality of vehicle members each having respective positions in a formation and in which two or more of the vehicle members collaboratively execute at least one micro cloud operation. Control may be implemented to monitor performance of the at least one micro cloud operation, monitor operational capabilities of the vehicle members, determine, based at least in part on the operational capabilities of the vehicle members, a change in formation position for at least one of the vehicle members, wherein the change in formation position improves the performance of the at least one micro cloud operation and transmit one or more commands to cause the vehicle members to execute the change in formation position.

A computer-implemented method for controlling a vehicle communication network comprising a plurality of nodes at a site, where at least some of the nodes are vehicles operating at the site. The method includes obtaining environment data indicative of a geometry of the site; obtaining position data indicative of respective positions of the nodes; modelling respective communication channels between interconnected nodes in the vehicle communication network based on the environment data and the position data; estimating respective signal quality metrics indicative of a communication link quality between the respective interconnected nodes based on the modelled communication channels; and controlling relative positions of the vehicles at the site based on the signal quality metrics and on a pre-determined signal quality acceptance criterion.

A computer-implemented method for controlling a vehicle communication network comprising a plurality of nodes at a site, where at least some of the nodes are vehicles operating at the site. The method includes obtaining environment data indicative of a geometry of the site; obtaining position data indicative of respective positions of the nodes; modelling respective communication channels between interconnected nodes in the vehicle communication network based on the environment data and the position data; estimating respective signal quality metrics indicative of a communication link quality between the respective interconnected nodes based on the modelled communication channels; and controlling relative positions of the vehicles at the site based on the signal quality metrics and on a pre-determined signal quality acceptance criterion.

A spectral mapping module may include processing circuitry configured to receive spectral activity information from one or more assets that have flown through an area. The spectral activity information may be associated with location information indicating a respective location at which each portion of the spectral activity information was obtained. The processing circuitry may be further configured to associate the spectral activity information with respective volumetric elements that are each associated with respective portions of the area, determine spectral activity indicators for each of the respective volumetric elements that has spectral activity information associated therewith, and generate display data including the spectral activity indicators.

A spectral mapping module may include processing circuitry configured to receive spectral activity information from one or more assets that have flown through an area. The spectral activity information may be associated with location information indicating a respective location at which each portion of the spectral activity information was obtained. The processing circuitry may be further configured to associate the spectral activity information with respective volumetric elements that are each associated with respective portions of the area, determine spectral activity indicators for each of the respective volumetric elements that has spectral activity information associated therewith, and generate display data including the spectral activity indicators.