Abstract: A predictive modeling approach to managing cellular network infrastructure is disclosed. In an embodiment, a method can include receiving raw data from a plurality of data sources populated while operating a cellular network. The method can then generate per-logical cell site data by normalizing the raw data based on a set of LCSs in the cellular network to generate per-LCS data. The method can then generate an example from the per-LCS data and generate a predicted energy consumption value for the given LCS by inputting the example into a predictive model (e.g., a decision tree-based model, such as an XGBoost model). From this output, the method can determine if the predicted energy consumption value is higher than an expected energy consumption value (e.g., a historical range of consumption). If so, the method can then label the given LCS as an outlier.

Abstract: Techniques for determining a network health score for a network are disclosed. In one embodiment, a method is disclosed comprising obtaining network data associated with one or more user equipments (UEs) connected to a network, generating UE data for the one or more UEs using the network data, the generating comprising assigning each UE to a UE category from a set of UE categories, determining a set of UE category weights corresponding to the set of UE categories using the network data, determining at least one network health score for the one or more UEs using the network data, the generated UE data and the set of UE category weights and causing performance or at least one action in accordance with the determined at least one network health score.

Abstract: Techniques for determining a network health score for a network are disclosed. In one embodiment, a method is disclosed comprising obtaining network data associated with one or more user equipments (UEs) connected to a network, generating UE data for the one or more UEs using the network data, the generating comprising assigning each UE to a UE category from a set of UE categories, determining a set of UE category weights corresponding to the set of UE categories using the network data, determining at least one network health score for the one or more UEs using the network data, the generated UE data and the set of UE category weights and causing performance or at least one action in accordance with the determined at least one network health score.

Abstract: Techniques for determining a network health score for a network are disclosed. In one embodiment, a method is disclosed comprising obtaining network data associated with one or more user equipments (UEs) connected to a network, generating UE data for the one or more UEs using the network data, the generating comprising assigning each UE to a UE category from a set of UE categories, determining a set of UE category weights corresponding to the set of UE categories using the network data, determining at least one network health score for the one or more UEs using the network data, the generated UE data and the set of UE category weights and causing performance or at least one action in accordance with the determined at least one network health score.

Abstract: In some implementations, a service qualification system may receive, from a user device, a set of images that depict a window. The service qualification system may perform an image-based analysis of the set of images to determine a reflectivity score associated with the window. The reflectivity score may be indicative of a quality associated with a signal of the service being received through the window. The service qualification system may determine, based on the reflectivity score, a service qualification metric that is indicative of a capability of receiving the service within the unit. The service qualification system may perform an action associated with the service qualification metric.

Abstract: In some implementations, a service qualification system may receive, from a user device, a set of images that depict a window. The service qualification system may perform an image-based analysis of the set of images to determine a reflectivity score associated with the window. The reflectivity score may be indicative of a quality associated with a signal of the service being received through the window. The service qualification system may determine, based on the reflectivity score, a service qualification metric that is indicative of a capability of receiving the service within the unit. The service qualification system may perform an action associated with the service qualification metric.

Abstract: In some implementations, a service qualification system may receive, from a user device, a set of images that depict a window. The service qualification system may perform an image-based analysis of the set of images to determine a reflectivity score associated with the window. The reflectivity score may be indicative of a quality associated with a signal of the service being received through the window. The service qualification system may determine, based on the reflectivity score, a service qualification metric that is indicative of a capability of receiving the service within the unit. The service qualification system may perform an action associated with the service qualification metric.