Growth metric predicted loading on wireless access nodes

• US 9,219,592 B1
• Filed: 10/22/2013
• Issued: 12/22/2015
• Est. Priority Date: 10/22/2013
• Status: Expired due to Fees

• Current Assignee: Sprint Communications Company LP (Deutsche Telekom AG)
• Original Assignee: Sprint Communications Company LP (Deutsche Telekom AG)
• Inventors: Gan, Seng Chai, Nichols, Cheryl Marie
• Primary Examiner(s): Zaidi, Iqbal
• Application Number:
  US14/059,778
• Time in Patent Office: 791 Days
• Field of Search: 370280-311, 709203-227
• US Class Current: 1/1
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04W 16/22 Traffic simulation tools or models
• CPC Class Codes: H04W 72/04 Wireless resource allocation

Abstract

Systems, methods, and software for operating a wireless communication system to predict loading on a wireless access node are provided herein. In one example, a method includes allocating past wireless communication usage in a geographic area served by the wireless access node among different wireless communication device types, identifying a growth metric for each of the wireless communication device types based on past device sales in a geographic region larger than the geographic area, and applying the growth metric for each of the wireless communication device types to the past communication usage allocated to each of the wireless communication device types to predict future communication usage for each of the wireless communication device types at the wireless access node. The method also includes aggregating the predicted future communication usage loading for each of the wireless communication device types to predict the future loading on the wireless access node.

Claims

1. A method of operating a wireless communication system to predict loading on a wireless access node, the method comprising:

in a network control system, allocating past wireless communication usage in a geographic area served by the wireless access node among a plurality of different wireless communication device types by at least determining a usage model for the wireless access node, wherein the usage model identifies at least a quantity of communication session types per each of the plurality of different wireless communication device types;

in the network control system, identifying a growth metric for each of the wireless communication device types based on past device sales in a geographic region larger than the geographic area; and

in the network control system, applying the growth metric for each of the wireless communication device types to the past communication usage allocated to each of the wireless communication device types to predict future communication usage for each of the wireless communication device types at the wireless access node;

in the network control system, aggregating the predicted future communication usage loading for each of the wireless communication device types to predict the future loading on the wireless access node; and

adjusting a wireless access resource of the wireless access node based on the future loading for the wireless access node.

2. The method of claim 1, wherein the geographic area comprises a sector of the wireless access node and the geographic region comprises an area associated with a zip code.

3. The method of claim 1, wherein the geographic area comprises a sector of the wireless access node and the geographic region comprises a metro area.

4. The method of claim 1, wherein the geographic area comprises a sector of the wireless access node and the geographic region comprises a state.

5. The method of claim 1, wherein the geographic area comprises a sector of the wireless access node and the geographic region comprises a country.

6. The method of claim 1, wherein the geographic area comprises a sector of the wireless access node and the geographic region is global.

7. The method of claim 1, wherein allocating the past wireless communication usage in the geographic area served by the wireless access node among the plurality of different wireless communication device types comprises:

identifying communication session characteristics comprising the communication session types, communication session durations, and the plurality of different wireless communication device types for a plurality of communication sessions handled by the wireless access node; and

processing at least the communication session characteristics to determine the usage model for the wireless access node.

8. The method of claim 1, wherein the growth metric comprises user device sales information indicating a change in sales quantity of at least one of the plurality of different wireless communication device types.

9. The method of claim 1, wherein the plurality of different wireless communication device types comprise at least one of wireless communication device models and wireless communication device manufacturers.

10. A software apparatus to operate a wireless communication system to predict loading on a wireless access node, comprising:

software configured to direct a processing system to;

allocate past wireless communication usage in a geographic area served by the wireless access node among a plurality of different wireless communication device types by at least determining a usage model for the wireless access node, wherein the usage model identifies at least a quantity of communication session types per each of the plurality of different wireless communication device types;

identify a growth metric for each of the wireless communication device types based on past device sales in a geographic region larger than the geographic area; and

apply the growth metric for each of the wireless communication device types to the past communication usage allocated to each of the wireless communication device types to predict future communication usage for each of the wireless communication device types at the wireless access node;

aggregate the predicted future communication usage loading for each of the wireless communication device types to predict the future loading on the wireless access node;

adjust a wireless access resource of the wireless access node based on the future loading for the wireless access node; and

one or more non-transitory storage media that stores the software.

11. The software apparatus of claim 10, wherein the geographic area comprises a sector of the wireless access node and the geographic region comprises an area associated with a zip code.

12. The software apparatus of claim 10, wherein the geographic area comprises a sector of the wireless access node and the geographic region comprises a metro area.

13. The software apparatus of claim 10, wherein the geographic area comprises a sector of the wireless access node and the geographic region comprises a state.

14. The software apparatus of claim 10, wherein the geographic area comprises a sector of the wireless access node and the geographic region comprises a country.

15. The software apparatus of claim 10, wherein the geographic area comprises a sector of the wireless access node and the geographic region is global.

16. The software apparatus of claim 10, comprising:

the software further configured to direct the processing system to;

identify communication session characteristics comprising the communication session types, communication session durations, and the plurality of different wireless communication device types for a plurality of communication sessions handled by the wireless access node; and

process at least the communication session characteristics to determine the usage model for the wireless access node

process the usage model to allocate the past wireless communication usage in the geographic area served by the wireless access node among the plurality of different wireless communication device types.

17. The software apparatus of claim 10, wherein the growth metric comprises user device sales information indicating a change in sales quantity of at least one of the plurality of different wireless communication device types.

18. The software apparatus of claim 10, wherein the plurality of different wireless communication device types comprise at least one of wireless communication device models and wireless communication device manufacturers.