IP Library › Granted Patent US 12665818
Granted Patent B2
US 12665818 · App. 18/379,469 · Granted Jun 23, 2026

Management of cross-node machine learning operations in a radio access network

Inventors: Rajeev Kumar (San Diego, CA); Aziz Gholmieh (Del Mar, CA); Gavin Bernard Horn (La Jolla, CA); Geetha Priya Rajendran (Bangalore, IN)
Assignee: QUALCOMM Incorporated
H04L41/16H04W24/02H04W68/005
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Quick Facts
Patent No.
US 12665818
App. No.
18/379,469
Granted
Jun 23, 2026
Kind
B2
Abstract

Certain aspects of the present disclosure provide techniques for managing cross-node artificial intelligence (AI) and/or machine learning (ML) operations in a radio access network (RAN). An example method of wireless communication by a first network entity includes obtaining machine learning input data associated with a user equipment (UE); providing, to a second network entity, an indication of machine learning output data generated using the machine learning input data; and providing, to the second network entity, control signaling for a cross-node machine learning session between the UE and the first network entity based at least in part on one or more performance indicators associated with the cross-node machine learning session.

Claims (62)

1 . An apparatus configured for wireless communications, comprising one or more processors coupled to one or more memories, the one or more processors being configured to cause the apparatus to:

obtain machine learning input data associated with a user equipment (UE);

provide, to a network entity, an indication of machine learning output data generated using the machine learning input data; and

provide, to the network entity, control signaling for a cross-node machine learning session between the UE and the apparatus based at least in part on one or more performance indicators associated with the cross-node machine learning session,

wherein the one or more processors are configured to cause the apparatus to provide an indication of a configuration associated with monitoring the cross-node machine learning session at the UE or at the network entity.

2 . The apparatus of claim 1 , wherein:

(i) to obtain the machine learning input data, the one or more processors are configured to cause the apparatus to obtain the machine learning input data via a radio access network intelligent controller (RIC) indication message; and to provide the machine learning output data, the one or more processors are configured to cause the apparatus to provide, to the network entity, the machine learning output data via a RIC control request; or

(ii) to obtain the machine learning input data, the one or more processors are configured to cause the apparatus to obtain the machine learning input data via a cross-node specific request message; and to provide the machine learning output data, the one or more processors are configured to cause the apparatus to provide, to the network entity, the machine learning output data via a cross-node specific response message.

3 . The apparatus of claim 1 , wherein:

the one or more processors are configured to cause the apparatus to monitor the one or more performance indicators associated with the cross-node machine learning session; and

to provide the control signaling, the one or more processors are configured to cause the apparatus to provide the control signaling in response to the monitoring of the one or more performance indicators.

4 . The apparatus of claim 3 , wherein:

the one or more processors are configured to cause the apparatus to obtain a RIC indication message comprising an indication of monitoring information used for the monitoring; and

to monitor the one or more performance indicators, the one or more processors are configured to cause the apparatus to monitor the one or more performance indicators based at least in part on the monitoring information.

5 . The apparatus of claim 1 , wherein the indication of the configuration is associated with monitoring the cross-node machine learning session at the UE.

6 . The apparatus of claim 5 , wherein the configuration indicates one or more events that trigger reporting of monitoring information and indicates information to report from the UE as the monitoring information.

7 . The apparatus of claim 1 , wherein the indication of the configuration is associated with monitoring the cross-node machine learning session at the network entity.

8 . The apparatus of claim 1 , wherein:

the apparatus comprises a radio access network (RAN) intelligent controller (RIC) configured to communicate with the network entity via an E2 interface; and

the network entity comprises a central unit (CU).

9 . An apparatus configured for wireless communications, comprising one or more processors coupled to one or more memories, the one or more processors being configured to cause the apparatus to:

obtain machine learning input data associated with a user equipment (UE);

provide, to a network entity, an indication of machine learning output data generated using the machine learning input data; and

provide, to the network entity, control signaling for a cross-node machine learning session between the UE and the apparatus based at least in part on one or more performance indicators associated with the cross-node machine learning session,

wherein:

the control signaling comprises a RIC control message indicating to deactivate a machine learning function or model used at the UE; or

the one or more processors are configured to cause the apparatus to: obtain, from the network entity, a RIC indication message requesting the apparatus to deactivate the cross-node machine learning session between the UE and the apparatus; and to provide the control signaling, the one or more processors are configured to cause the apparatus to provide, to the network entity, the control signaling in response to obtaining the RIC indication message.

10 . The apparatus of claim 9 , wherein the control signaling comprises the RIC control message indicating to deactivate the machine learning function or model used at the UE.

11 . The apparatus of claim 9 , wherein:

the one or more processors are configured to cause the apparatus to: obtain, from the network entity, the RIC indication message requesting the apparatus to deactivate the cross-node machine learning session between the UE and the apparatus; and

to provide the control signaling, the one or more processors are configured to cause the apparatus to provide, to the network entity, the control signaling in response to obtaining the RIC indication message.

12 . A method for wireless communications, comprising:

obtaining machine learning input data associated with a user equipment (UE);

providing, to a network entity, an indication of machine learning output data generated using the machine learning input data; and

providing, to the network entity, control signaling for a cross-node machine learning session between the UE and the apparatus based at least in part on one or more performance indicators associated with the cross-node machine learning session,

wherein the method further comprises providing an indication of a configuration associated with monitoring the cross-node machine learning session at the UE or at the network entity.

13 . The method of claim 12 , wherein:

(i) obtaining the machine learning input data comprises obtaining the machine learning input data via a radio access network intelligent controller (RIC) indication message; and providing the machine learning output data comprises providing, to the network entity, the machine learning output data via a RIC control request; or

(ii) obtaining the machine learning input data comprises obtaining the machine learning input data via a cross-node specific request message; and providing the machine learning output data comprises providing, to the network entity, the machine learning output data via a cross-node specific response message.

14 . The method of claim 12 , wherein:

the method comprises monitoring the one or more performance indicators associated with the cross-node machine learning session; and

providing the control signaling comprises providing the control signaling in response to the monitoring of the one or more performance indicators.

15 . The method of claim 14 , wherein:

the method comprises obtaining a RIC indication message comprising an indication of monitoring information used for the monitoring; and

monitoring the one or more performance indicators comprises monitoring the one or more performance indicators based at least in part on the monitoring information.

16 . The method of claim 12 , wherein the indication of the configuration is associated with monitoring the cross-node machine learning session at the UE.

17 . The method of claim 16 , wherein the configuration indicates one or more events that trigger reporting of monitoring information and indicates information to report from the UE as the monitoring information.

18 . The method of claim 12 , wherein the indication of the configuration is associated with monitoring the cross-node machine learning session at the network entity.

19 . The method of claim 12 , wherein:

the method is performed by an apparatus that comprises a radio access network (RAN) intelligent controller (RIC) configured to communicate with the network entity via an E2 interface; and

the network entity comprises a central unit (CU).

20 . A method for wireless communications, comprising:

obtaining machine learning input data associated with a user equipment (UE);

providing, to a network entity, an indication of machine learning output data generated using the machine learning input data; and

providing, to the network entity, control signaling for a cross-node machine learning session between the UE and the apparatus based at least in part on one or more performance indicators associated with the cross-node machine learning session,

wherein:

the control signaling comprises a RIC control message indicating to deactivate a machine learning function or model used at the UE; or

the method comprises: obtaining, from the network entity, a RIC indication message requesting the apparatus to deactivate the cross-node machine learning session between the UE and the apparatus; and providing the control signaling by providing, to the network entity, the control signaling in response to obtaining the RIC indication message.

21 . The method of claim 20 , wherein the control signaling comprises the RIC control message indicating to deactivate the machine learning function or model used at the UE.

22 . The method of claim 20 , wherein:

the method comprises obtaining, from the network entity, the RIC indication message requesting the apparatus to deactivate the cross-node machine learning session between the UE and the apparatus; and

providing the control signaling comprises providing, to the network entity, the control signaling in response to obtaining the RIC indication message.