At&t Intellectual Property I, L.p.

Ye Ge

Wenjie Zhao

Jie Chen

Abraham George

Huahui Wang

Yuning Yang

The described technology is generally directed towards spammer location detection, and in particular, to locating a spammer that makes multiple calls from a given location via a cellular communications network. In some examples, network equipment can obtain call trace records associated with the multiple calls, identify a group of call trace records based on a shared call trace feature, aggregate data from call trace records within the group, and determine an estimated location based on the aggregated data.

Spammer location detection

Locations and azimuths of cells of a communication network can be estimated, determined, and validated. Cell attribute management component (CAMC) can estimate, determine, and/or validate cell locations based on analysis of timing advance (TA) measurement data and/or location data associated with devices associated with base stations associated with cells. CAMC can estimate azimuth of a cell associated with a base station based on analysis of a validated cell location of the cell and location data associated with devices associated with the cell. CAMC can determine whether a recorded azimuth of the cell is validated based on analysis of the estimated azimuth of the cell and the recorded azimuth of the cell. CAMC can tag the recorded azimuth of the cell as validated if applicable azimuth accuracy criteria is met, inaccurate if applicable azimuth criteria is not met, or omni if the cell is an omni cell.

Automated cell azimuth estimation and validation

The described technology is generally directed towards user equipment (UE) geolocation using a long history of network information. In some examples, a long history of network information associated with a UE can be processed to identify frequently repeated serving cell and correlated timing advance values. The frequently repeated serving cell and correlated timing advance values are indicative of frequently visited places. Next, the long history can be leveraged to determine locations of the frequently visited places with enhanced accuracy, and the resulting enhanced accuracy locations can be identified in a location lookup table for the UE. When the UE subsequently connects to the frequently repeated serving cell and the correlated timing advance value is observed, the location lookup table can be used to quickly assign an enhanced accuracy location to the UE.

User equipment geolocation using a history of network information

Intelligent, automated, fixed wireless internet planning (e.g., using a computerized tool) is enabled. For instance, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining, for a user equipment determined to be within a defined coverage area, a signal to interference and noise ratio, based on the signal to interference and noise ratio, determining a spectral efficiency value corresponding to the user equipment, based on the spectral efficiency value and a total available bandwidth of a network via which the defined coverage area is enabled, determining an available throughput corresponding to the user equipment, and in response to a determination that the available throughput exceeds a threshold throughput, designating the user equipment, in a data store, as being covered within the defined coverage area by the total available bandwidth of the network.

Planning of fixed wireless internet

Locations of cells of a communication network can be estimated, determined, and validated. Cell location component (CLC) can analyze timing advance (TA) measurement data and/or location data associated with devices associated with a base station associated with one or more cells. CLC can estimate a first location of the base station, based on the TA measurement data and/or location data, to facilitate estimating the location of an associated cell. CLC can validate the estimated cell location or recorded cell location of the cell (recorded in a cell location pool) based on analysis of estimated cell location, recorded cell location, TA measurement data, and/or location data, and, based on the validation, can tag the cell location determination as accurate, acceptable, bad, or uncertain. CLC can request additional monitoring of a cell location determination tagged as uncertain, or investigation of a cell location determination tagged as bad.

Automated cell location estimation and validation

The described technology is generally directed towards user equipment (UE) geolocation. A machine learning model can be trained to estimate UE locations based on historical network communication data associated with the UEs. In order to train the machine learning model, known previous UE locations and corresponding historical network communication data can be provided to the machine learning model. A variety of other information, such as topographical information, can also be provided to the machine learning model. The machine learning model can be trained to predict the known previous UE locations based on the corresponding historical network communication data and any other provided information. Once it is trained, the machine learning model can be deployed to estimate real-time UE locations based on historical network communication data associated with the UEs.

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Holmdel, NJ, United States of America

Ye Ge