Geolocation of wireless network users

Patent metadata

Field	Value
Publication number	US-12004114-B2
Application number	US-202117346179-A
Country	US
Kind code	B2
Filing date	Jun 11, 2021
Priority date	Jun 11, 2021
Publication date	Jun 4, 2024
Grant date	Jun 4, 2024

Abstract

A method includes selecting a first machine learning model from a plurality of machine learning models that are trained for use in performing geolocation, wherein the first machine learning model is selected to perform geolocation within a first cell of a plurality of cells of a wireless network, acquiring event data from a plurality of wireless devices within the first cell, grouping the event data into a plurality of records, wherein each record of the plurality of records contains event data that indicates a common wireless device of the plurality of wireless devices, a common cell of the plurality of cells, and a common timestamp, and generating a predicted location of a first wireless device of the plurality of wireless devices, using the first machine learning model, wherein the first machine learning model outputs the predicted location in response to an input of a record of the plurality of records.

First claim

What is claimed is:

1. A method comprising:
selecting, by a processing system including at least one processor, a first machine learning model from among a plurality of machine learning models that are trained for use in performing geolocation, wherein the first machine learning model is selected to perform geolocation within a first cell of a plurality of cells of a wireless network;
acquiring, by the processing system, event data from a plurality of wireless devices operating within the first cell;
grouping, by the processing system, the event data into a plurality of records, wherein each record of the plurality of records contains event data that indicates a common wireless device of the plurality of wireless devices, a common cell of the plurality of cells, and a common timestamp; and
generating, by the processing system, a predicted location of a first wireless device of the plurality of wireless devices, using the first machine learning model, wherein the first machine learning model outputs the predicted location in response to an input of a record of the plurality of records.

2. The method of claim 1 , wherein the first machine learning model comprises one selected from a group of: a recurrent neural network with Long Short-term Memory (LSTM) units, a probabilistic model, and a lookup table.

3. The method of claim 1 , wherein the selecting is based at least in part on a set of computational resources available to the processing system.

4. The method of claim 1 , wherein the selecting is based at least in part on a desired throughput for the first cell, and wherein the desired throughput indicates a desired number of predicted locations to be generated per unit of time.

5. The method of claim 1 , wherein the event data comprises signal strength data for the plurality of wireless devices and timing events for the plurality of wireless devices.

6. The method of claim 5 , wherein the signal strength data comprises at least one selected from a group of: reference signals received power, serving cell, and neighbor cell.

7. The method of claim 5 , wherein the timing events comprise timing advance.

8. The method of claim 1 , wherein the grouping comprises:
selecting, by the processing system, a first event from the event data to function as a pivot event;
identifying, by the processing system, a second event which corresponds to the pivot event;
deriving, by the processing system, a key from a wireless device of the plurality of wireless devices associated with the second event, a cell of the plurality of cells associated with the second event, and a timestamp associated with the second event; and
using, by the processing system, the key to correlate the wireless device of the plurality of wireless devices associated with the second event, the cell of the plurality of cells associated with the second event, and the timestamp associated with the second event with other events from the event data.

9. The method of claim 8 , wherein the pivot event comprises an event which contains a timing advance feature.

10. The method of claim 8 , wherein the grouping is performed using a chain correlator comprising a plurality of data mappers and a plurality of data correlators.

11. The method of claim 1 , wherein the generating comprises refining the predicted location with global positioning system data from the first wireless device to generate an updated predicted location.

12. The method of claim 11 , wherein the updated predicted location comprises a weighted combination of the predicted location and the global positioning system data.

13. The method of claim 1 , wherein the generating comprises refining the predicted location using a filtering technique.

14. The method of claim 13 , wherein the filtering technique comprises at least one selected from a group of: an extended Kalman filtering, a particle filtering, and a smoothing function.

15. The method of claim 1 , further comprising:
augmenting, by the processing system, the plurality of records with static data from an external data source.

16. The method of claim 15 , wherein the augmenting comprises replacing a value in a record of the plurality of records with a value from the static data.

17. The method of claim 1 , further comprising:
creating, by the processing system, a time-series model of the plurality of records.

18. The method of claim 17 , further comprising:
batching, by the processing system, the time-series model with another time-series model, prior to the generating the predicted location.

19. A non-transitory computer-readable storage device storing a plurality of instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising:
selecting a first machine learning model from among a plurality of machine learning models that are trained for use in performing geolocation, wherein the first machine learning model is selected to perform geolocation within a first cell of a plurality of cells of a wireless network;
acquiring event data from a plurality of wireless devices operating within the first cell;
grouping the event data into a plurality of records, wherein each record of the plurality of records contains event data that indicates a common wireless device of the plurality of wireless devices, a common cell of the plurality of cells, and a common timestamp; and
generating a predicted location of a first wireless device of the plurality of wireless devices, using the first machine learning model, wherein the first machine learning model outputs the predicted location in response to an input of the a record of the plurality of records.

20. An apparatus comprising:
a processing system including at least one processor; and
a non-transitory computer-readable storage device storing a plurality of instructions which, when executed by the processing system cause the processing system to perform operations, the operations comprising:
selecting a first machine learning model from among a plurality of machine learning models that are trained for use in performing geolocation, wherein the first machine learning model is selected to perform geolocation within a first cell of a plurality of cells of a wireless network;
acquiring event data from a plurality of wireless devices operating within the first cell;
grouping the event data into a plurality of records, wherein each record of the plurality of records contains event data that indicates a common wireless device of the plurality of wireless devices, a common cell of the plurality of cells, and a common timestamp; and
generating a predicted location of a first wireless device of the plurality of wireless devices, using the first machine learning model, wherein the first machine learning model outputs the predicted location in response to an input of a record of the plurality of records.

Assignees

• At & T Ip I Lp
• At&T Intellectual Propety I L P

Inventors

• Bhorkar Abhijeet
• Jiang Baofeng
• Wong Peter

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• H04W64/00 Primary

  Locating users or terminals {or network equipment} for network management purposes, e.g. mobility management · CPC title

• G06F16/211

  Schema design and management · CPC title

• G06F16/29

  Geographical information databases · CPC title