Granted Patent B2

US 12,413,485 · App. 18/447,411 · Granted Sep 9, 2025

Inventor: Montgomery Nelson Groff (Denver, CO)

Assignee: DISH Wireless L.L.C.

H04L41/40H04L41/16H04W24/02

Abstract

Claims (83)

1. A method, comprising:

performing a first plurality of spectrum administration service (SAS) operations in accordance with existing SAS configuration commands during a first time duration, wherein:

the first plurality of SAS operations is configured to establish one or more communication sessions between a plurality of network components in a core network and a plurality of user equipment;

the existing SAS configuration commands are configured to provide control information to perform a first plurality of SAS operations;

the first plurality of SAS operations is associated with controlling access between the plurality of user equipment and one or more Citizens Broadband Radio Service (CBRS) channels; and

the one or more CBRS channels are communication channels configured to provide connectivity between the plurality of user equipment and the core network;

collecting a plurality of channel parameters, the plurality of channel parameters comprising unstructured data associated with the first plurality of SAS operations and the one or more CBRS channels;

storing the plurality of channel parameters in a data lake comprising the plurality of channel parameters;

transforming, using a machine learning algorithm, the unstructured data of the plurality of channel parameters into structured data in the data lake, the structured data being representative of a plurality of conditions in the one or more CBRS channels during the first time duration;

generating, using the machine learning algorithm, a plurality of routing modifications based at least in part upon the plurality of channel parameters and the existing SAS configuration commands, the routing modifications being configured to modify routing of resources in the plurality of CBRS channels to be allocated in a communication network;

generating, using the machine learning algorithm, a plurality of optimized SAS configuration commands based at least in part upon the plurality of routing modifications, the existing SAS configuration commands, a transformed version of the unstructured data representative of a plurality of conditions in the one or more CBRS channels during the first time duration, the optimized SAS configuration commands comprising possible updates to the plurality of existing SAS configuration commands, and the routing modifications;

comparing, using the machine learning algorithm, the plurality of optimized SAS configuration commands to the plurality of existing SAS configuration commands;

determining, using the machine learning algorithm, that the plurality of optimized SAS configuration commands comprise one or more commands that are different to those comprised in the plurality of existing SAS configuration commands; and

in response to determining that the plurality of optimized SAS configuration commands comprise commands that are different to those comprised in the plurality of existing SAS configuration commands, training the machine learning algorithm using input data comprising the structured data representative of the plurality of conditions in the one or more CBRS channels during the first time duration, and the routing modifications; and

performing a second plurality of SAS operations associated with the one or more CBRS channel in accordance with the optimized SAS configuration commands during a second time duration, the second time duration being different from the first time duration.

2. The method of claim 1 , further comprising:

in response to determining that the plurality of optimized SAS configuration commands comprise same commands to those comprised in the plurality of existing SAS configuration commands, performing the second plurality of SAS operations in accordance with the existing SAS configuration commands.

3. The method of claim 1 , wherein:

the plurality of channel parameters from the plurality of communication channels is collected over a predefined time duration during the first time duration.

4. The method of claim 3 , wherein:

the plurality of channel parameters is stored in the data lake automatically in response to collecting the plurality of channel parameters over the predefined time duration.

5. The method of claim 1 , wherein:

the plurality of channel parameters comprises a channel connectivity registry comprising connectivity interruptions and connectivity success rates, dynamic routing information, static routing information, a plurality of channel communication frequency bands.

6. The method of claim 5 , wherein:

the connectivity interruptions comprise communication interruptions in the one or more CBRS channels over a predefined time duration during the first time duration.

7. The method of claim 5 , wherein:

the connectivity success rates comprise a percentage of successful communication transactions in the one or more CBRS channels over a predefined time duration.

8. A non-transitory computer readable medium storing instructions that when executed by a processor cause the processor to:

perform a first plurality of spectrum administration service (SAS) operations in accordance with existing SAS configuration commands during a first time duration, wherein:

the first plurality of SAS operations is configured to establish one or more communication sessions between a plurality of network components in a core network and a plurality of user equipment;

the existing SAS configuration commands are configured to provide control information to perform a first plurality of SAS operation;

the first plurality of SAS operations is associated with controlling access between the plurality of user equipment and one or more Citizen Broadband Radio Service (CBRS) channels; and

the one or more CBRS channels are communication channels configured to provide connectivity between the plurality of user equipment and the core network;

collect a plurality of channel parameters, the plurality of channel parameters comprising unstructured data associated with the first plurality of SAS operations and the one or more CBRS channels;

store the plurality of channel parameters in a data lake comprising the plurality of channel parameters;

transform, using a machine learning algorithm, the unstructured data of the plurality of channel parameters into structured data in the data lake, the structured data being representative of a plurality of conditions in the one or more CBRS channels during the first time duration;

generate, using the machine learning algorithm, a plurality of routing modifications based at least in part upon the plurality of channel parameters and the existing SAS configuration commands, the routing modifications being configured to modify routing of resources in the plurality of CBRS channels to be allocated in a communication network;

generate, using the machine learning algorithm, a plurality of optimized SAS configuration commands based at least in part upon the plurality of routing modifications, the existing SAS configuration commands, a transformed version of the unstructured data representative of a plurality of conditions in the one or more CBRS channels during the first time duration, the optimized SAS configuration commands comprising possible updates to the plurality of existing SAS configuration commands, and the routing modifications;

compare, using the machine learning algorithm, the plurality of optimized SAS configuration commands to the plurality of existing SAS configuration commands;

determine, using the machine learning algorithm, the plurality of optimized SAS configuration commands to the plurality of existing SAS configuration commands; and

in response to determining that the plurality of optimized SAS configuration commands comprise commands that are different to those comprised in the plurality of existing SAS configuration commands, training the machine learning algorithm using input data comprising the structured data representative of the plurality of conditions in the one or more CBRS channels during the first time duration, and the routing modifications; and

perform a second plurality of SAS operations associated with the one or more CBRS channel in accordance with the optimized SAS configuration commands during a second time duration, the second time duration being different from the first time duration.

9. The non-transitory computer readable medium of claim 8 , wherein the processor is further caused to:

in response to determining that the plurality of optimized SAS configuration commands comprise same commands to those comprised in the plurality of existing SAS configuration commands, performing the second plurality of SAS operations in accordance with the existing SAS configuration commands.

10. The non-transitory computer readable medium of claim 8 , wherein:

the plurality of channel parameters from the plurality of communication channels is collected over a predefined time duration during the first time duration.

11. The non-transitory computer readable medium of claim 10 , wherein:

the plurality of channel parameters is stored in the data lake automatically in response to collecting the plurality of channel parameters over the predefined time duration.

12. The non-transitory computer readable medium of claim 8 , wherein:

the plurality of channel parameters comprises a channel connectivity registry comprising connectivity interruptions and connectivity success rates, dynamic routing information, static routing information, a plurality of channel communication frequency bands.

13. The non-transitory computer readable medium of claim 12 , wherein:

the connectivity interruptions comprise communication interruptions in the one or more CBRS channels over a predefined time duration during the first time duration; and

the connectivity success rates comprise a percentage of successful communication transactions in the one or more CBRS channels over the predefined time duration.

14. An apparatus, comprising:

a memory, comprising:

a data lake comprising one or more channel parameters that are stored as structured data, semi-structured data, or unstructured data;

a machine learning algorithm configured to analyze and structure the one or more channel parameters in the data lake; and

a plurality of existing spectrum administration service (SAS) configuration commands configured to establish one or more communication sessions between a plurality of network components in a core network and a plurality of user equipment, the existing SAS configuration commands being configured to provide control information to perform a first plurality of SAS operation; and

a processor communicatively coupled to the memory and configured to:

perform the first plurality of SAS operations in accordance with the existing SAS configuration commands during a first time duration, wherein:

the first plurality of SAS operations is associated with controlling access between the plurality of user equipment and one or more Citizens Broadband Radio Service (CBRS) channels; and

the one or more CBRS channels are communication channels configured to provide connectivity between the plurality of user equipment and the core network;

collect a plurality of channel parameters, the plurality of channel parameters comprising unstructured data associated with the first plurality of SAS operations and the one or more CBRS channels;

store the plurality of channel parameters in the data lake;

transform, using the machine learning algorithm, the unstructured data of the plurality of channel parameters into structured data in the data lake, the structured data being representative of a plurality of conditions in the one or more CBRS channels during the first time duration;

generate, using the machine learning algorithm, a plurality of routing modifications based at least in part upon the plurality of channel parameters and the existing SAS configuration commands, the routing modifications being configured to modify routing of resources in the plurality of CBRS channels to be allocated in a communication network;

generate, using the machine learning algorithm, a plurality of optimized SAS configuration commands based at least in part upon the plurality of routing modifications, the existing SAS configuration commands, a transformed version of the unstructured data representative of a plurality of conditions in the one or more CBRS channels during the first time duration, the optimized SAS configuration commands comprising possible updates to the plurality of existing SAS configuration commands, and the routing modifications;

compare, using the machine learning algorithm, the plurality of optimized SAS configuration commands to the plurality of existing SAS configuration commands;

determine, using the machine learning algorithm, that the plurality of optimized SAS configuration commands comprise one or more commands that are different to those comprised in the plurality of existing SAS configuration commands; and

in response to determining that the plurality of optimized SAS configuration commands comprise commands that are different to those comprised in the plurality of existing SAS configuration commands, train the machine learning algorithm using input data comprising the structured data representative of the plurality of conditions in the one or more CBRS channels during the first time duration, and the routing modifications; and

perform a second plurality of SAS operations associated with the one or more CBRS channels in accordance with the optimized SAS configuration commands during a second time duration, the second time duration being different from the first time duration.

15. The apparatus of claim 14 , wherein the processor is further configured to:

in response to determining that the plurality of optimized SAS configuration commands comprise same commands to those comprised in the plurality of existing SAS configuration commands, perform the second plurality of SAS operations in accordance with the existing SAS configuration commands.

16. The apparatus of claim 14 , wherein:

the plurality of channel parameters from the plurality of communication channels is collected over a predefined time duration during the first time duration.

17. The apparatus of claim 16 , wherein:

the processor is further configured to store the one or more channel parameters in the data lake automatically in response to collecting the one or more channel parameters from the plurality of communication channels over the predefined time duration.

18. The apparatus of claim 14 , wherein:

the one or more channel parameters comprise a channel connectivity registry comprising connectivity interruptions and connectivity success rates, dynamic routing information, static routing information, a plurality of channel communication frequency bands.

19. The apparatus of claim 18 , wherein:

the connectivity interruptions comprise communication interruptions in the one or more CBRS channels over a predefined time duration during the first time duration.

20. The apparatus of claim 18 , wherein:

the connectivity success rates comprise a percentage of successful communication transactions in the one or more CBRS channels over a predefined time duration.

Assignments (2)

Continuity (1)

References Cited (36)