Adaptive beam sweeping for 5G or other next generation network

Patent metadata

Field	Value
Publication number	US-10980028-B2
Application number	US-201916369778-A
Country	US
Kind code	B2
Filing date	Mar 29, 2019
Priority date	Mar 29, 2019
Publication date	Apr 13, 2021
Grant date	Apr 13, 2021

Abstract

Given real-time user equipment (UE) measurements from an open radio access network (O-RAN) infrastructure, a radio access network intelligent controller (RIC) can compute a UE distribution context space. The O-RAN can comprise gNode-Bs, centralized units, and distributed units. The UE distribution context space computations can be performed by a UE context correlator module of the RIC. The UE context correlator module can also utilize a pre-defined UE context model, which contains definitions and values for various UE context attributes to generate adaptive beam-forming patterns.

First claim

What is claimed is:

1. A method, comprising:
based on attribute data representative of attributes of a first number of user equipment, modeling, by network equipment comprising a processor, a real-time distribution of the first number of user equipment at a first time, resulting in a real-time model, wherein the attribute data comprises a time associated with utilization of the first number of user equipment a location of the first number of user equipment, and a social event associated with the first number of user equipment;
correlating, by the network equipment, the attribute data to defined parameters, wherein correlating the attribute data to the defined parameters comprises partitioning the attribute data based on defined context attributes comprising a time context attribute, a location context attribute, and a social event context attribute;
based on a result of correlating the attribute data to the defined parameters, facilitating, by the network equipment, generating a machine-learning model, wherein the machine learning model is at least in part based on a historical user equipment distribution;
based on the machine-learning model and the real-time model, estimating, by the network equipment, a distribution model, resulting in an estimated distribution model, to be used to determine a beam sweep process to be applied by the processor, at a second time later than the first time, to a second number of user equipment, less than the first number of user equipment, that have previously utilized the beam sweep process; and
based on the estimated distribution model, determining, by the network equipment, a beam sweep periodicity to be applied to the beam sweep process.

2. The method of claim 1 , wherein the attribute data comprises a time associated with utilization of the first number of user equipment.

3. The method of claim 1 , wherein the defined parameters comprise a commute time duration, and wherein correlating the attribute data comprises correlating the time associated with the utilization to the commute time.

4. The method of claim 1 , wherein the attribute data comprises a location associated with the utilization of the first number of user equipment.

5. The method of claim 4 , wherein the defined parameters comprise an airport location, and wherein correlating the attribute data comprises correlating the location associated with the utilization to the airport location.

6. The method of claim 1 , wherein the attribute data comprises a concert event associated with the utilization of the first number of user equipment.

7. The method of claim 6 , wherein the defined parameters comprise an emergency event, and wherein correlating the attribute data comprises correlating the event associated with the utilization to the emergency event.

8. A system, comprising: a processor; and
a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:
based on attribute data representative of attributes of a first number of user equipment, modeling a real-time distribution of the first number of user equipment at a first time, resulting in a real-time distribution model, wherein the attribute data comprises a time associated with utilization of the first number of user equipment a location of the first number of user equipment, and a social event associated with the first number of user equipment;
correlating the attribute data to defined parameters, wherein correlating the attribute data to the defined parameters comprises partitioning the attribute data based on defined context attributes comprising a time context attribute, a location context attribute, and a social event context attribute;
in response to correlating the attribute data to the defined parameters and based on historical distribution data, generating a machine-learned distribution model;
based on the real-time distribution model and the machine-learned distribution model, estimating a distribution model for a second time that is later than the first time, resulting in an estimated distribution model applicable to a second number of user equipment, wherein the second number of user equipment is less than the first number of user equipment; and
based on the estimated distribution model, determining a beam sweeping control parameter, wherein the beam sweeping control parameter comprises a scanning periodicity value to be applied, by the processor, to a beam sweep; and
in response to the determining, applying the beam sweeping control parameter to the second number of user equipment at the second time.

9. The system of claim 8 , wherein the operations further comprise:
based on the beam sweeping control parameter, performing a beam sweep in accordance with the estimated distribution model.

10. The system of claim 9 , wherein performing the beam sweep in accordance with the estimated distribution model reduces a power utilization of a base station.

11. The system of claim 8 , wherein the real-time distribution model is based on a time, a hotel location, and a football event associated with the first number of user equipment.

12. The system of claim 8 , wherein the machine-learned distribution model is based on a lunch hour time, an office building location, and a parade event associated with the first number of user equipment.

13. The system of claim 8 , wherein correlating the attribute data to the defined parameters comprises correlating a time of day to a commute time associated with a user of a user equipment of the first number of user equipment.

14. The system of claim 8 , wherein correlating the attribute data to the defined parameters comprises correlating a point-of-interest associated with a user equipment of the first number of user equipment to the location.

15. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
based on correlating mobile device data to a set of defined parameters, of a first number of mobile devices at a first time, generating a current distribution model of the first number of mobile devices, wherein the current distribution model comprises a time associated with utilization of the first number of mobile devices, a location of the first number of mobile devices, and a social event associated with the first number of mobile devices, and wherein correlating the mobile device data to a set of defined parameters comprises partitioning attribute data based on a time context attribute, a location context attribute, and a social event context attribute;
based on historical distribution model, generating a machine-learned distribution model of the first number of mobile devices;
based on the current distribution model and the machine-learned distribution model, generating an estimated distribution model of a second number of mobile devices less than the first number of devices; and
in response to generating the estimated distribution model, obtaining a beam sweeping control parameter, wherein the beam sweeping control parameter comprises a beam sweep periodicity to be applied, by the processor, to a beam sweeping process; and
in response to obtaining the beam sweeping control parameter, applying the beam sweeping control parameter to the second number of mobile devices at a second time later than the first time and based on the beam sweep periodicity.

16. The non-transitory machine-readable medium of claim 15 , wherein the operations further comprise:
utilizing the beam sweeping control parameter to facilitate transmission of

Assignees

• At & T Ip I Lp

Inventors

• Sharma Deva-Datta
• Jana Shraboni
• Tofighbakhsh Mostafa
• Ungureanu Bogdan
• Jana Rittwik

Classifications

• H04W72/51

  based on terminal or device properties · CPC title

• H04B7/06952 Primary

  Selecting one or more beams from a plurality of beams, e.g. beam training, management or sweeping · CPC title

• H04W72/046 Primary

  the resource being in the space domain, e.g. beams · CPC title

• H04B7/043

  using best eigenmode, e.g. beam forming or beam steering · CPC title

• H04W24/02

  Arrangements for optimising operational condition · CPC title