Abstract: A device may receive a first type of data identifying measurements associated with user devices and/or base stations of a mobile radio environment, and a second type of data identifying spatiotemporal behavior associated with the user devices. The device may train a first model, with the first type of data, to generate a trained first model that yields dimensionality-reduced spatiotemporal characteristics of the first type of data, and may train a second model, with the second type of data and the dimensionality-reduced spatiotemporal characteristics, to generate a trained second model. The device may receive particular data identifying measurements associated with a user device and/or base stations, and may process the particular data, with the trained first model, to generate a dimensionality-reduced spatiotemporal characteristic of the particular data.

Abstract: A device may receive mobile radio data identifying utilization of a mobile radio network that includes base stations and user devices in a geographical area. The device may process the mobile radio data, with a machine learning feature extraction model, to generate a behavioral representation, that is probabilistic in nature, of invariant aspects of spatiotemporal utilization of the mobile radio network. The device may generate one or more instances of the spatiotemporal utilization of the mobile radio network that reflects the probabilistic nature of a spatiotemporal predictable component of the behavioral representation. The device may utilize the one or more instances of the spatiotemporal utilization of the mobile radio network as a dataset for training or evaluating a system to manage performance of the mobile radio network.

Abstract: A device may receive a first type of data identifying measurements associated with user devices and/or base stations of a mobile radio environment, and a second type of data identifying spatiotemporal behavior associated with the user devices. The device may train a first model, with the first type of data, to generate a trained first model that yields dimensionality-reduced spatiotemporal characteristics of the first type of data, and may train a second model, with the second type of data and the dimensionality-reduced spatiotemporal characteristics, to generate a trained second model. The device may receive particular data identifying measurements associated with a user device and/or base stations, and may process the particular data, with the trained first model, to generate a dimensionality-reduced spatiotemporal characteristic of the particular data.

Abstract: A device may receive real mobile radio data identifying measurements of radio transmissions of base stations and user devices of a mobile radio environment in a geographical area, and may receive network topology data associated with the geographical area. The device may utilize, based on the network topology data, a machine learning feature extraction approach to generate a representation of invariant aspects of spatiotemporal predictable components of the real mobile radio data, and may generate, based on the representation of invariant aspects, stochastic data that includes a probability that a radio signal will be obstructed. The device may utilize the stochastic data to identify a realistic discoverable spatiotemporal signature, and may train or evaluate a system to manage performance of a mobile radio network based on the realistic discoverable spatiotemporal signature.

Abstract: A device may receive mobile radio data identifying utilization of a mobile radio network that includes base stations and user devices in a geographical area. The device may process the mobile radio data, with a machine learning feature extraction model, to generate a behavioral representation, that is probabilistic in nature, of invariant aspects of spatiotemporal utilization of the mobile radio network. The device may generate one or more instances of the spatiotemporal utilization of the mobile radio network that reflects the probabilistic nature of a spatiotemporal predictable component of the behavioral representation. The device may utilize the one or more instances of the spatiotemporal utilization of the mobile radio network as a dataset for training or evaluating a system to manage performance of the mobile radio network.

Abstract: A device may receive a geolocation estimation model and geolocation input data identifying geolocations. The device may process one or more sets of the geolocation input data, with a quality evaluator model, to generate one or more quality evaluation metrics. The device may modify the geolocation estimation model based on the one or more quality evaluation metrics.

Abstract: A device may determine a first resolution and aggregation for data collection from a network and may receive first PM data associated with the network, at the first resolution and aggregation. The device may calculate, based on the first PM data, a first parameter characteristic of a UE and may determine a trigger based on the first parameter characteristic and based on one or more of a root cause analysis , an application input, or a KPI. The device may identify a portion of the network that is associated with the trigger based on the first PM data and may determine a second resolution and aggregation for data collection from the portion of the network. The device may receive second PM data associated with the portion of the network, at the second resolution and aggregation and may calculate a second parameter characteristic of the UE based on the second PM data.

Abstract: A navigation platform may receive information identifying an origin location and a destination location associated with an autonomous vehicle. The navigation platform may identify one or more candidate route segments for a navigation route from the origin location to the destination location. The navigation platform may receive, based on transmitting a query to a geolocation server, wireless communication coverage information for the one or more candidate route segments. The navigation platform may select, based on the wireless communication coverage information, one or more route segments, from the one or more candidate route segments, for the navigation route. The navigation platform may transmit, to the autonomous vehicle, an instruction to navigate the autonomous vehicle along the navigation route.

Abstract: A device may perform an iteration of an optimization procedure. The device may apply a smoothing technique to a value relating to the optimization procedure after performing the iteration of the optimization procedure. The device may selectively terminate the optimization procedure based on applying the smoothing technique to the value relating to the optimization procedure. The device may provide information identifying a result of the optimization procedure based on selectively terminating the optimization procedure.

Abstract: A device may detect a service condition associated with a network. The device may determine a prioritization of cells of the network based on characteristics of one or more subscribers to the cells of the network. The characteristics may include at least one of: a service type characteristic, an application type characteristic, a mobility characteristic, a vector characteristic, an altitude characteristic, a position characteristic, a location type characteristic, a device type characteristic, a device capability characteristic, a connection characteristic, a subscription characteristic, a call type characteristic, a user profile characteristic, a terrain characteristic, or an operator relationship characteristic. The device may perform an alteration to one or more network parameters based on the prioritization of cells of the network to reduce an impact of the service condition.

Abstract: Techniques for mobile network geolocation are disclosed. In one particular exemplary embodiment, the techniques may be realized as a system for mobile network geolocation. The system may comprise one or more processors communicatively coupled to a mobile communications network. The one or more processors may be configured to determine that a mobile device is on a travel path. The one or more processors may also be configured to determine one or more candidate locations of the mobile device based on data associated with the travel path. The one or more processors may further be configured to determine at least one candidate route associated with the mobile device along the travel path comprised from one or more candidate locations.

Abstract: A device may obtain network topology information that identifies a set of nodes of a network and one or more physical links between nodes of the set of nodes. The set of nodes may include one or more base stations, one or more mobile devices, and one or more network resources. The device may determine enhancement information based on location information identifying a location of one or more of the set of nodes, and subscriber information identifying one or more subscribers associated with one or more of the set of nodes. The device may determine network performance information based on the enhancement information. The network performance information may identify an impairment in performance of the set of nodes, and may identify a modification to a network parameter to improve performance of the set of nodes. The device may provide the network performance information to cause a change in the network.

Abstract: Techniques for mobile network geolocation are disclosed. In one particular exemplary embodiment, the techniques may be realized as a system for mobile network geolocation. The system may comprise one or more processors communicatively coupled to a mobile communications network. The one or more processors may be configured to determine that a mobile device is on a travel path. The one or more processors may also be configured to determine one or more candidate locations of the mobile device based on data associated with the travel path. The one or more processors may further be configured to determine at least one candidate route associated with the mobile device along the travel path comprised from one or more candidate locations.

Abstract: A device may include one or more processors. The device may receive, via a plurality of data streams of a front haul link, a set of packets including information for transmission via an air interface. The set of packets may be associated with a data prioritization relating to transmission via the air interface. The set of packets may include information of a time-frequency resource element array. The device may reconstruct a set of time-frequency resource elements of the time-frequency resource element array based on the data prioritization of the set of packets. The device may transmit, via the air interface, the time-frequency resource element array.

Abstract: Techniques for dynamic network optimization using geolocation and network modeling are disclosed. In one particular exemplary embodiment, the techniques may be realized as a system for network optimization. The system may comprise a data collection system configured to collect geolocated subscriber records from a plurality of mobile devices. The system may also comprise a SON optimization system communicatively coupled to the data collection system via a network. The SON optimization system may comprise at least one pre-processor configured to receive and process geolocated subscriber records from the data collection system. The SON optimization system may further comprise a network simulator configured to perform network simulation analysis based on the processed geolocated subscriber records, and provide a new network configuration based on the network simulation analysis, wherein the new network configuration is estimated to improve network performance.

Abstract: Techniques for multiple pass geolocation are disclosed. In one particular exemplary embodiment, the techniques may be realized as a system for multiple pass geolocation. The system may comprise one or more processors communicatively coupled to a mobile communications network. The one or more processors may be configured to conduct a first geolocation pass to identify one or more geolocation estimates under consideration. The one or more processors may also be configured to conduct at least one additional geolocation pass to refine one or more geolocation estimates under consideration. The one or more processors may further be configured to determine an approximate location of one or more mobile devices within an estimated coverage area of a network based on at least the first geolocation pass and the at least one additional geolocation pass.