CN-122029867-A - Cross-node machine learning operation in a radio access network

Abstract

Certain aspects of the present disclosure provide techniques for performing cross-node machine learning operations in a radio access network. An example method of wireless communication by a first network entity includes providing, to a second network entity, an indication of cross-node machine learning information for a cross-node machine learning session between the first network entity and a User Equipment (UE), obtaining machine learning information associated with the UE, and controlling the cross-node machine learning session based at least in part on the machine learning information.

Inventors

• R. kuma
• A. Holmi
• G.B. Horn
• G. P. Ruijian De'an

Assignees

• 高通股份有限公司

Dates

Publication Date

20260512

Application Date

20240926

Priority Date

20231012

Claims (20)

1. 1. An apparatus configured for wireless communication, the apparatus comprising: One or more memories, and One or more processors coupled to the one or more memories, the one or more processors configured to cause the apparatus to: providing an indication of cross-node machine learning information for a cross-node machine learning session between the apparatus and a User Equipment (UE) to a network entity; Obtaining machine learning information associated with the UE, and The cross-node machine learning session is controlled based at least in part on the machine learning information.
2. 2. The apparatus of claim 1, wherein the cross-node machine learning information comprises one or more parameters associated with the cross-node machine learning session supported by the apparatus.
3. 3. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to: Obtaining a registration request associated with an application, the registration request including an indication of one or more parameters associated with the cross-node machine learning session supported by the application, and In response to the registration request, a registration response is provided indicating that the application is registered.
4. 4. The apparatus of claim 1, wherein to provide the indication of the cross-node machine learning information, the one or more processors are configured to cause the apparatus to provide the indication of the cross-node machine learning information via a Radio Access Network (RAN) intelligent controller (RIC) subscription request.
5. 5. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to: Obtaining capability information associated with the UE, and Providing an indication of a configuration of the UE associated with the cross-node machine learning session to the network entity in response to obtaining the capability information.
6. 6. The apparatus of claim 5, wherein to provide the indication of the configuration of the UE, the one or more processors are configured to cause the apparatus to provide the indication of the configuration via a RIC control request.
7. 7. The apparatus of claim 5, wherein the one or more processors are configured to cause the apparatus to select a machine learning function or model for use by the UE for the cross-node machine learning session based at least in part on the capability information, wherein the indication of the configuration comprises an indication of the selected machine learning function or model.
8. 8. The apparatus of claim 1, wherein: The one or more processors are configured to cause the apparatus to obtain a Radio Access Network (RAN) intelligent controller (RIC) query message requesting initiation of the cross-node machine learning session between the UE and the apparatus, Wherein to provide the indication of the cross-node machine learning information, the one or more processors are configured to cause the apparatus to provide the indication of the cross-node machine learning information via a RIC query response in response to the RIC query message.
9. 9. The apparatus of claim 1, wherein: the one or more processors are configured to cause the apparatus to obtain an indication of the cross-node machine learning session between the apparatus and the UE from the network entity, To control the cross-node machine learning session, wherein the one or more processors are configured to cause the apparatus to control the cross-node machine learning session based at least in part on the indication of the cross-node machine learning session between the UE and the apparatus.
10. 10. The apparatus of claim 9, wherein the indication of the cross-node machine learning session between the UE and the apparatus comprises a UE identifier associated with the UE and one or more machine learning models at the UE for the cross-node machine learning session.
11. 11. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to: providing an indication to the network entity reporting status information associated with the UE; obtaining the status information associated with the UE from the network entity, and Providing an indication of a configuration of the UE associated with the cross-node machine learning session to the network entity in response to obtaining the state information.
12. 12. The apparatus of claim 1, wherein: The apparatus includes a radio access network intelligent controller (RIC) configured to communicate with the network entity via an E2 interface, and The network entity comprises a Central Unit (CU).
13. 13. The apparatus of claim 1, wherein to control the cross-node machine learning session, the one or more processors are configured to cause the apparatus to: determining a model structure based at least in part on the machine learning information, and An indication of the determined model structure to be used by the UE is provided to the network entity.
14. 14. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to: performing cross-node machine learning inference based at least in part on the machine learning information to generate output data, and The output data is provided to the network entity.
15. 15. The apparatus of claim 14, wherein: The machine learning information includes encoded channel state information generated at the UE, and The output data includes decoded channel state information associated with a communication link between the UE and the network entity.
16. 16. An apparatus configured for wireless communication, the apparatus comprising: One or more memories, and One or more processors coupled to the one or more memories, the one or more processors configured to cause the apparatus to: Obtaining, from a network entity, an indication of cross-node machine learning information for a cross-node machine learning session between the network entity and a User Equipment (UE); providing a configuration for the cross-node machine learning session to the UE based at least in part on the cross-node machine learning information; obtaining machine learning information associated with the UE; Providing the machine learning information to the network entity; obtaining output data generated from the machine learning information from the network entity, and Communicate with the UE based at least in part on the output data.
17. 17. The apparatus of claim 16, wherein the cross-node machine learning information comprises one or more parameters associated with the cross-node machine learning session supported by the network entity.
18. 18. The apparatus of claim 16, wherein to obtain the indication of the cross-node machine learning information, the one or more processors are configured to cause the apparatus to obtain the indication of the cross-node machine learning information via a Radio Access Network (RAN) intelligent controller (RIC) subscription request.
19. 19. The apparatus of claim 16, wherein the one or more processors are configured to cause the apparatus to: providing capability information associated with the UE to the network entity, and Obtaining an indication of the configuration of the UE associated with the cross-node machine learning session from the network entity in response to providing the capability information.
20. 20. The apparatus of claim 19, wherein to obtain the indication of the configuration of the UE, the one or more processors are configured to cause the apparatus to obtain the indication of the configuration via a RIC control request.

Description

Cross-node machine learning operation in a radio access network Cross Reference to Related Applications The present application claims priority from U.S. patent application serial No. 18/379,475, entitled "CROSS-NODE MACHINE LEARNING OPERATIONS IN A RADIO ACCESS NETWORK (CROSS-NODE machine learning operation in radio access networks)" filed on 10 months 12 of 2023, the entire contents of which are incorporated herein by reference. Introduction to the invention Technical Field Aspects of the present disclosure relate to wireless communications, and more particularly, to techniques for implementing machine learning and/or artificial intelligence aspects in a Radio Access Network (RAN). Description of related Art Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, broadcast, or other similar types of services. These wireless communication systems may employ multiple-access techniques that are capable of supporting communication with several users by sharing the available wireless communication system resources with those users. Despite the tremendous technological advances made over the years in wireless communication systems, challenges remain. For example, complex and dynamic environments may still attenuate or block signals between a wireless transmitter and a wireless receiver. Accordingly, there is a continuing desire to improve the technical performance of wireless communication systems, including, for example, improving the speed and data carrying capacity of communications, improving the efficiency of use of shared communication media, reducing the power used by transmitters and receivers in performing communications, improving the reliability of wireless communications, avoiding redundant transmissions and/or receptions and associated processing, improving the coverage area of wireless communications, increasing the number and types of devices that can access the wireless communication system, increasing the ability of different types of devices to communicate with each other, increasing the number and types of wireless communication media available for use, and the like. Accordingly, there is a need for further improvements in wireless communication systems to overcome the foregoing technical challenges and others. Disclosure of Invention One aspect provides a method of wireless communication by a first network entity. The method includes providing, to a second network entity, an indication of cross-node machine learning information for a cross-node machine learning session between the first network entity and a User Equipment (UE), obtaining machine learning information associated with the UE, and controlling the cross-node machine learning session based at least in part on the machine learning information. Another aspect provides a method of wireless communication by a first network entity. The method includes obtaining, from a second network entity, an indication of cross-node machine learning information for a cross-node machine learning session between the second network entity and a UE, providing, to the UE, a configuration for the cross-node machine learning session based at least in part on the cross-node machine learning information, obtaining machine learning information associated with the UE, providing the machine learning information to the second network entity, obtaining, from the second network entity, output data generated from the machine learning information, and communicating with the UE based at least in part on the output data. Another aspect provides a method of wireless communication by an apparatus (e.g., a user equipment). The method includes providing capability information associated with a cross-node machine learning session between the apparatus and a second network entity to a first network entity, obtaining a configuration for the cross-node machine learning session from the first network entity, and communicating with the second network entity in accordance with the configuration for the cross-node machine learning session. Other aspects provide one or more apparatuses operable to, configured, or otherwise adapted to perform any portion of any of the methods described herein (e.g., such that execution may be accomplished by only one apparatus or in a distributed manner across multiple apparatuses); one or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors of one or more devices, cause the one or more devices to perform any portion of any of the methods described herein (e.g., such that instructions may be included in only one computer-readable storage medium or distributed across multiple computer-readable media comprising instructions such that instructions may be executed by only one processor or by multiple processors in a distributed manner, such that each of the one or more devices may comprise one or more processo