SYSTEM AND METHOD FOR SCALABLE RADIO RESOURCE MANAGEMENT FOR WIRELESS LOCAL AREA NETWORKS

• US 20190320332A1
• Filed: 10/20/2017
• Published: 10/17/2019
• Est. Priority Date: 10/21/2016
• Status: Active Grant

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: HALABIAN, Hassan, SAHABI, Afshin, SKOF, Mike
• Granted Patent: US 11,122,448 B2
• Time in Patent Office: Days
• CPC Class Codes: H04W 16/18 Network planning tools
• CPC Class Codes: H04W 24/02 Arrangements for optimising operational condition
• CPC Class Codes: H04W 84/12 WLAN [Wireless Local Area Networks]
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

A method and node for providing scalable radio resource management, RRM, in a wireless local area network, WLAN, are provided. According to one aspect, a method in a self-organizing network, SON, manager node includes receiving radio frequency, RF, scan data from wireless access points, AP, creating network segments of limited sizes based on the RF scan data, and periodically executing an RRM optimization algorithm in an RRM time slot in each of successive RRM periods.

Claims

1. A method in a self-organizing network, SON, manager node of providing scalable radio resource management, RRM, in a wireless local area network, WLAN, the method comprising:

receiving radio frequency, RF, scan data from wireless access points, AP;

creating network segments of limited sizes based on the RF scan data; and

periodically executing an RRM optimization algorithm in an RRM time slot in each of successive RRM periods.

2. The method of claim 1, wherein sizes of the network segments are less than a predetermined threshold.

3. The method of claim 2, wherein the predetermined threshold is based on a number of APs over which to execute the RRM optimization algorithm.

4. The method of claim 3, wherein the predetermined threshold is based on an amount of time between successive RRM time slots.

5. The method of claim 1, wherein the SON manager node is a cloud server.

6. The method of claim 5, wherein the cloud server interfaces with:

a configuration interface configured to transmit operating parameters to APs; and

a data interface configured to receive the RF scan data from the APs.

11. The node of claim 1, wherein the SON manager node is a cloud server.

12. The node of claim 11, further including:

a configuration interface configured to transmit operating parameters to APs; and

a data interface configured to receive the RF scan data from the APs.

7. A self-organizing network, SON, manager node configured to provide scalable radio resource management, RRM, in a wireless local area network, WLAN, the node comprising:

processing circuitry configured to;

store RF scan data;

receive radio frequency, RF, scan data from wireless access points, AP;

create network segments of limited sizes based on the RF scan data;

schedule periodic execution of an RRM optimization algorithm in RRM time slots in successive RRM periods; and

periodically execute the RRM optimization algorithm during a scheduled RRM time slot in each of successive RRM periods.

8. The node of claim 7, wherein sizes of the network segments are less than a predetermined threshold.

9. The node of claim 8, wherein the predetermined threshold is based on a number of APs over which to run the RRM optimization algorithm.

10. The node of claim 9, wherein the predetermined threshold is based on an amount of time between successive RRM time slots.

13. A self-organizing network, SON, manager node configured to provide scalable radio resource management, RRM, in a wireless local area network, WLAN, the node comprising:

a data collector module configured to receive radio frequency, RF, scan data from wireless access points, AP;

a network slicer module configured to create network segments of limited sizes based on the RF scan data;

an RRM scheduler module configured to schedule repetitive execution of an RRM optimization algorithm in RRM time slots; and

an RRM optimizer module configured to execute the RRM optimization algorithm during a scheduled RRM time slot.