System and method for throughput prediction for cellular networks

Patent metadata

Field	Value
Publication number	US-11476959-B2
Application number	US-202016855479-A
Country	US
Kind code	B2
Filing date	Apr 22, 2020
Priority date	Aug 31, 2018
Publication date	Oct 18, 2022
Grant date	Oct 18, 2022

Abstract

Aspects of the subject disclosure may include, for example, a method in which a processing system identifies a plurality of performance indicators comprising device performance indicators for a plurality of communication devices on a cellular network and network performance indicators for the cellular network. The method also includes obtaining historical data regarding the plurality of performance indicators for each of a series of time points during a past time period; the historical data for each of the plurality of performance indicators form an array of values for that performance indicator. The method further includes generating from each array a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period; the set of inputs comprises quantiles of the array, and the algorithm comprises a machine learning algorithm. Other embodiments are disclosed.

First claim

What is claimed is:

1. A method, comprising:
identifying, by a processing system including a processor, a plurality of performance indicators, the plurality of performance indicators including network performance indicators regarding a cellular network and device performance indicators regarding a communication device communicating with the cellular network;
obtaining, by the processing system, data regarding each of the plurality of performance indicators for a past time period having a predetermined length, wherein the data regarding each of the plurality of performance indicators forms an array of values for that performance indicator;
generating, by the processing system, a statistical summarization of the array;
generating, by the processing system based on the data, a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period having a predetermined length, the algorithm comprising a machine learning algorithm trained according to the data;
obtaining, by the processing system, a predicted throughput for the cellular network based on the algorithm; and
allocating, by the processing system, network resources of the cellular network based on the predicted throughput.

2. The method of claim 1 , further comprising providing, by the processing system, guidance based on the predicted throughput to a network element of the cellular network, a server connected to the cellular network, a client connected to the cellular network, an application executing on the cellular network, or a combination thereof.

3. The method of claim 1 , wherein the set of inputs comprises the statistical summarization of the array.

4. The method of claim 1 , wherein the statistical summarization comprises quantiles of the array.

5. The method of claim 1 , wherein the machine learning algorithm comprises a regression algorithm.

6. The method of claim 1 , wherein the predicted throughput corresponds to a statistical indicator of the throughput over the future time period.

7. The method of claim 1 , further comprising selecting, by the processing system, the length of the past time period and the length of the future time period.

8. The method of claim 1 , further comprising selecting, by the processing system, the algorithm from a plurality of algorithms based on comparing an actual throughput for the cellular network with the predicted throughput obtained using each of the plurality of algorithms.

9. The method of claim 1 , wherein the communication device is a mobile device, and wherein the device performance indicators include a physical speed of the communication device.

10. The method of claim 1 , wherein the network comprises a plurality of cells, and wherein the network performance indicators include a cell load for each of the plurality of cells.

11. A device comprising:
a processing system including a processor; and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations comprising:
identifying a plurality of performance indicators, the plurality of performance indicators including network performance indicators regarding a cellular network and device performance indicators regarding a mobile communication device communicating with the cellular network;
obtaining data regarding each of the plurality of performance indicators for a past time period having a predetermined length, wherein the data regarding each of the plurality of performance indicators forms an array of values for that performance indicator;
generating a statistical summarization of the array;
generating, based on the data, a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period having a predetermined length, the algorithm comprising a machine learning algorithm trained according to the data;
obtaining a predicted throughput for the cellular network based on the algorithm; and
allocating network resources of the cellular network based on the predicted throughput.

12. The device of claim 11 , wherein the device performance indicators include a physical speed of the communication device.

13. The device of claim 11 , wherein the set of inputs comprises the statistical summarization of the array.

14. The device of claim 11 , wherein the predicted throughput corresponds to a statistical indicator of the throughput over the future time period.

15. The device of claim 11 , wherein the operations further comprise selecting the length of the past time period and the length of the future time period.

16. The device of claim 11 , wherein the operations further comprise selecting the algorithm from a plurality of algorithms based on comparing an actual throughput for the cellular network with the predicted throughput obtained using each of the plurality of algorithms.

17. A non-transitory machine-readable medium comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations comprising:
identifying a plurality of performance indicators, the plurality of performance indicators including network performance indicators regarding a cellular network and device performance indicators regarding a communication device communicating with the cellular network;
obtaining data regarding each of the plurality of performance indicators for a past time period having a predetermined length, wherein the data regarding each of the plurality of performance indicators forms an array of values for that performance indicator;
generating a statistical summarization of the array;
generating, based on the data, a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period having a predetermined length, the algorithm comprising a machine learning algorithm trained according to the data, the machine learning algorithm comprising a regression algorithm;
obtaining a predicted throughput for the cellular network based on the algorithm; and
allocating network resources of the cellular network based on the predicted throughput.

18. The non-transitory machine-readable medium of claim 17 , wherein the set of inputs comprises the statistical summarization of the array.

19. The non-transitory machine-readable medium of claim 17 , wherein the predicted throughput corresponds to a statistical indicator of the throughput over the future time period.

20. The non-transitory machine-readable medium of claim 17 , wherein the operations further comprise selecting the length of the past time period and the length of the future time period.

Assignees

• At & T Ip I Lp
• Univ College Cork National Univ Of Ireland
• Univ College Cork—National Univ Of Ireland

Inventors

• Jana Rittwik
• Halepovic Emir
• Sinha Rakesh
• Gopalakrishnan Vijay
• Zahran Ahmed
• Raca Darijo
• Sreenan Cormac John
• Bathula Balagangadhar G
• Varvello Matteo

Classifications

• H04W72/542

  using measured or perceived quality · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N20/00

  Machine learning · CPC title