At&t Intellectual Property I, L.p.

Abraham George

Wenjie Zhao

Jie Chen

Ye Ge

Arun Jotshi

Carolyn Roche Johnson

Wei Yuan

Kenichi Futamura

Mohan Gawande

Sichong Guan

Sarat Puthenpura

Sam Houston Parker

David Gerald Belanger

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

Systems and methods may use a math programming model for designing an edge cloud network. The edge cloud network design may depend on various factors, including the number of edge cloud nodes, edge cloud node location, or traffic coverage, among other things.

5G edge cloud network design

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

The described technology is generally directed towards user equipment geolocation. Network measurement data associated with user equipment can be separated into static periods in which the user equipment was not moving, and moving periods in which the user equipment was moving. Static location processing can be applied to determine static locations from the static period network measurements, and moving location processing can be applied to determine moving locations from the moving period network measurements. Resulting static location information and moving location information can then be merged in order to improve the accuracy of both the static and the moving location information. The enhanced accuracy location information can be stored and used for any desired application.

Moving user equipment geolocation

Static user equipment geolocation

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.

User equipment geolocation

5D edge cloud network design

A method and apparatus for providing planning of a plurality of mobile switching centers in a wireless network are disclosed. For example, the method obtains input data, and determines a limit for at least one mobile switching center parameter in accordance with the input data. The method determines if the limit for the at least one mobile switching center parameter is exceeded and determines an optimal output for an objective function, wherein the objective function is based on a plurality of penalty factors, if the limit for the at least one mobile switching center parameter is exceeded.

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Litchfield, CT, United States of America

Abraham George