US-20260129484-A1 - MACHINE LEARNING TEST MODE FOR CONFORMANCE TESTING IN A WIRELESS COMMUNICATIONS SYSTEM

Abstract

Methods, systems, and devices for wireless communication are described. A user equipment (UE) may receive a message indicating a configuration for a test mode that includes one or more parameters associated with a machine learning (ML) model for conformance testing of the UE. The UE may receive, based on the message, an activation message activating the test mode and perform, based on activation of the test mode, a conformance test of the UE using the ML model in accordance with the one or more parameters.

Inventors

• Yogesh Tugnawat
• Pradeep Sagane Gowda
• Vijay Balasubramanian
• Sitaramanjaneyulu Kanamarlapudi
• Fernando Alonso Macias

Assignees

• QUALCOMM INCORPORATED

Dates

Publication Date

20260507

Application Date

20241106

Claims (20)

1. 1 . A user equipment (UE), comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to: receive a message indicating a configuration for a test mode for the UE, wherein the test mode comprises one or more parameters associated with a machine learning model for conformance testing of the UE; receive, based at least in part on the message, an activation message activating the test mode; and perform, based at least in part on activation of the test mode, a conformance test of the UE using the machine learning model in accordance with the one or more parameters.
2. 2 . The UE of claim 1 , wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: receive a deactivation message deactivating the test mode based at least in part on performing the conformance test.
3. 3 . The UE of claim 1 , wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: initialize the machine learning model based at least in part on activation of the test mode.
4. 4 . The UE of claim 3 , wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: perform an authentication procedure associated with the machine learning model, wherein determining that the machine learning model is successfully initialized at the UE is based at least in part on the authentication procedure.
5. 5 . The UE of claim 3 , wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: receive, from a network entity, the machine learning model, wherein initializing the machine learning model is based on receiving the machine learning model.
6. 6 . The UE of claim 1 , wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: train the machine learning model based at least in part on activation of the test mode.
7. 7 . The UE of claim 6 , wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: perform machine learning inference using inference data and the trained machine learning model.
8. 8 . The UE of claim 6 , wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: receive training data, wherein training the machine learning model is based at least in part on the training data.
9. 9 . The UE of claim 6 , wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: receive an indication of whether to train the machine learning model in an offline mode or an online mode based at least in part on activation of the test mode, wherein training the machine learning model is based on the indication of whether to train the machine learning model in the offline mode or the online mode.
10. 10 . The UE of claim 1 , wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: deactivate a second machine learning model based at least in part on activation of the test mode.
11. 11 . The UE of claim 1 , wherein the activation message is based on capability information associated with the UE.
12. 12 . The UE of claim 1 , wherein, to perform the conformance test, the one or more processors are individually or collectively operable to execute the code to cause the UE to: perform a beam management operation using the machine learning model; perform a channel state information reporting operation using the machine learning model; or perform a positioning operation using the machine learning model.
13. 13 . The UE of claim 1 , wherein the one or more parameters comprise an indication of the machine learning model, an indication of whether training data will be used to train the machine learning model, a trigger for the UE to request the machine learning model, or a combination thereof.
14. 14 . A method for wireless communications at a user equipment (UE), comprising: receiving a message indicating a configuration for a test mode for the UE, wherein the test mode comprises one or more parameters associated with a machine learning model for conformance testing of the UE; receiving, based at least in part on the message, an activation message activating the test mode; and performing, based at least in part on activation of the test mode, a conformance test of the UE using the machine learning model in accordance with the one or more parameters.
15. 15 . The method of claim 14 , further comprising: receiving a deactivation message deactivating the test mode based at least in part on performing the conformance test.
16. 16 . The method of claim 14 , further comprising: initializing the machine learning model based at least in part on activation of the test mode.
17. 17 . The method of claim 14 , further comprising: training the machine learning model based at least in part on activation of the test mode.
18. 18 . A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors to: receive a message indicating a configuration for a test mode for a user equipment (UE), wherein the test mode comprises one or more parameters associated with a machine learning model for conformance testing of the UE; receive, based at least in part on the message, an activation message activating the test mode; and perform, based at least in part on activation of the test mode, a conformance test of the UE using the machine learning model in accordance with the one or more parameters.
19. 19 . The non-transitory computer-readable medium of claim 18 , wherein the instructions are further executable by the one or more processors to: receive a deactivation message deactivating the test mode based at least in part on performing the conformance test.
20. 20 . The non-transitory computer-readable medium of claim 18 , wherein the instructions are further executable by the one or more processors to: initialize the machine learning model based at least in part on activation of the test mode.

Description

FIELD OF TECHNOLOGY The following relates to wireless communication, including a machine learning (ML) test mode for conformance testing in a wireless communications system. BACKGROUND Wireless communications systems are widely deployed to provide various types of communication content such as voice, video, packet data, messaging, broadcast, and so on. These systems may be capable of supporting communication with multiple users by sharing the available system resources (e.g., time, frequency, and power). Examples of such multiple-access systems include fourth generation (4G) systems such as Long Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, or LTE-A Pro systems, and fifth generation (5G) systems which may be referred to as New Radio (NR) systems. These systems may employ technologies such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple-access communications system may include one or more base stations, each supporting wireless communication for communication devices, which may be known as user equipment (UE). A wireless communications system may support conformance testing. Conformance testing may allow a network entity to monitor one or more actions of a user equipment (UE) during a communications procedure such that the network entity may determine whether the UE meets a minimum level of performance. SUMMARY The systems, methods, and devices of this disclosure each have several innovative aspects, no single one of which is solely responsible for the desirable attributes disclosed herein. A method for wireless communications by a UE is described. The method may include receiving a message indicating a configuration for a test mode for the UE, where the test mode includes one or more parameters associated with a ML model for conformance testing of the UE, receiving, based on the message, an activation message activating the test mode, and performing, based on activation of the test mode, a conformance test of the UE using the ML model in accordance with the one or more parameters. A UE for wireless communications is described. The UE may include one or more memories storing processor executable code, and one or more processors coupled with the one or more memories. The one or more processors may individually or collectively be operable to execute the code to cause the UE to receive a message indicating a configuration for a test mode for the UE, where the test mode includes one or more parameters associated with a ML model for conformance testing of the UE, receive, based on the message, an activation message activating the test mode, and perform, based on activation of the test mode, a conformance test of the UE using the ML model in accordance with the one or more parameters. Another UE for wireless communications is described. The UE may include means for receiving a message indicating a configuration for a test mode for the UE, where the test mode includes one or more parameters associated with a ML model for conformance testing of the UE, means for receiving, based on the message, an activation message activating the test mode, and means for performing, based on activation of the test mode, a conformance test of the UE using the ML model in accordance with the one or more parameters. A non-transitory computer-readable medium storing code for wireless communications is described. The code may include instructions executable by one or more processors to receive a message indicating a configuration for a test mode for the UE, where the test mode includes one or more parameters associated with a ML model for conformance testing of the UE, receive, based on the message, an activation message activating the test mode, and perform, based on activation of the test mode, a conformance test of the UE using the ML model in accordance with the one or more parameters. Some examples of the method, UE, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving a deactivation message deactivating the test mode based on performing the conformance test. Some examples of the method, UE, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for initializing the ML model based on activation of the test mode. Some examples of the method, UE, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for performing an authentication procedure associated with the ML model, where determining that the ML model may be successfully initialized at the UE may be based on the authentication procedure. Some examples of the method, UE, and non-transitory computer-readable medium described herein