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US-20260129713-A1 - METHOD AND APPARATUS OF SUPPORTING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNCTIONALITY

US20260129713A1US 20260129713 A1US20260129713 A1US 20260129713A1US-20260129713-A1

Abstract

A method of supporting artificial intelligence and machine learning functionality is provided. The method includes receiving an RRC reconfiguration message including an inference configuration associated with a UE-side functionality. The method includes determining an applicability status of the inference configuration of the UE-side functionality. The method includes transmitting a first RRC reconfiguration complete message including the applicability status of the inference configuration of the UE-side functionality. In a case that a report configuration of applicability included in the RRC reconfiguration message is enabled, and a current applicability status of the inference configuration of the UE-side functionality is different from a previous applicability status reported in an RRC message, transmitting the current applicability status of the configuration of the UE-side functionality in a second UE assistance information message.

Inventors

  • Hung-Chen Chen

Assignees

  • WNC CORPORATION

Dates

Publication Date
20260507
Application Date
20251105

Claims (20)

  1. 1 . A method of supporting artificial intelligence and machine learning functionality, wherein the method is implemented by a user equipment (UE), and comprises: receiving, from a base station, a Radio Resource Control (RRC) reconfiguration message including an inference configuration associated with a UE-side functionality; determining an applicability status of the inference configuration of the UE-side functionality; transmitting, to the base station, a first RRC reconfiguration complete message including the applicability status of the inference configuration of the UE-side functionality; and in a case that a report configuration of applicability included in the RRC reconfiguration message is enabled, and a current applicability status of the inference configuration of the UE-side functionality is different from a previous applicability status reported in an RRC message, transmitting, to the base station, the current applicability status of the configuration of the UE-side functionality in a second UE assistance information message.
  2. 2 . The method as claimed in claim 1 , wherein the applicability status is either applicable or inapplicable.
  3. 3 . The method as claimed in claim 1 , wherein the first RRC reconfiguration complete message including the applicability status has a processing latency requirement upon receiving the RRC reconfiguration message, and the applicability status is determined to be applicable or inapplicable by the end of a processing latency based on the processing latency requirement.
  4. 4 . The method as claimed in claim 1 , wherein the RRC message is one of a second RRC reconfiguration complete message or a first UE assistance information message.
  5. 5 . The method as claimed in claim 1 , further comprising: sending, to the base station, a training data collection request of the UE-side functionality.
  6. 6 . The method as claimed in claim 1 , further comprising: sending information of a preferred training data configuration for data collection associated with the UE-side functionality.
  7. 7 . The method as claimed in claim 1 , further comprising: receiving, from the base station, a capability enquiry message for asking functionalities supported by the UE.
  8. 8 . The method as claimed in claim 1 , further comprising: receiving, from the base station, a log data measurement configuration associated with a network-side functionality; performing a logging of measurement based on the log data measurement configuration; receiving, from the base station, a logged data request indication in a UE information request message; transmitting, to the base station, logged data in a UE information response message; and discarding the logged data upon the UE information response message is successfully delivered.
  9. 9 . The method as claimed in claim 8 , further comprising: discarding the logged data upon the UE transitions from an RRC Connected state to an RRC IDEL state or to an RRC INACTIVE state.
  10. 10 . The method as claimed in claim 8 , further comprising: stopping the logging of measurement when a memory of the UE is full or when the UE receives a command from the base station to stop the logging of measurement.
  11. 11 . An apparatus of supporting artificial intelligence and machine learning functionality, wherein the apparatus is a user equipment (UE) and comprises: a transceiver which, during operation, wirelessly communicates with at least one network node; and a processor communicatively coupled to the transceiver such that, during operation, the processor performs operations comprising: receiving, from a base station, a Radio Resource Control (RRC) reconfiguration message including an inference configuration associated with a UE-side functionality; determining an applicability status of the inference configuration of the UE-side functionality; transmitting, to the base station, a first RRC reconfiguration complete message including the applicability status of the inference configuration of the UE-side functionality; and in a case that a report configuration of applicability included in the RRC reconfiguration message is enabled, and a current applicability status of the configuration of the UE-side functionality is different from a previous applicability status reported in an RRC message, transmitting, to the base station, the current applicability status of the configuration of the UE-side functionality in a second UE assistance information message.
  12. 12 . The apparatus as claimed in claim 11 , wherein the applicability status is either applicable or inapplicable.
  13. 13 . The apparatus as claimed in claim 11 , wherein the first RRC reconfiguration complete message including the applicability status has a processing latency requirement upon receiving the RRC reconfiguration message, and the applicability status is determined to be applicable or inapplicable by the end of a processing latency based on the processing latency requirement.
  14. 14 . The apparatus as claimed in claim 11 , wherein the RRC message is one of a second RRC reconfiguration complete message or a first UE assistance information message.
  15. 15 . The apparatus as claimed in claim 11 , wherein the processor further performs operations comprising: sending, to the base station, a training data collection request of the UE-side functionality.
  16. 16 . The apparatus as claimed in claim 11 , wherein the processor further performs operations comprising: sending information of a preferred training data configuration for data collection associated with the UE-side functionality.
  17. 17 . The apparatus as claimed in claim 11 , wherein the processor further performs operations comprising: receiving, from the base station, a capability enquiry message for asking functionalities supported by the UE.
  18. 18 . The apparatus as claimed in claim 11 , wherein the processor further performs operations comprising: receiving, from the base station, a log data measurement configuration associated with a network-side functionality; performing a logging of measurement based on the log data measurement configuration; receiving, from the base station, a logged data request indication in a UE information request message; transmitting, to the base station, logged data in a UE information response message; and discarding the logged data upon the UE information response message is successfully delivered.
  19. 19 . The apparatus as claimed in claim 18 , wherein the processor further performs operations comprising: discarding the logged data upon the UE transitions from an RRC Connected state to an RRC IDEL state or to an RRC INACTIVE state.
  20. 20 . The apparatus as claimed in claim 18 , wherein the processor further performs operations comprising: stopping the logging of measurement when a memory of the UE is full or when the UE receives a command from the base station to stop the logging of measurement.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63/717,395, entitled “Method and Apparatus of Supporting Artificial Intelligence and Machine Learning in Radio Access Network”, filed on Nov. 7, 2024, the entirety of which is incorporated by reference herein. TECHNICAL FIELD The present disclosure generally relates to wireless communication. More specifically, aspects of the present disclosure relate to a method and an apparatus of supporting artificial intelligence (AI) and machine learning (ML) functionality. BACKGROUND Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims listed below and are not admitted as prior art by inclusion in this section. Nowadays, Artificial Intelligence/Machine Learning (AI/ML) techniques and relevant applications are being increasingly adopted by a wide variety of industries and have proved to be successful. One can foresee the potential benefits of AI/ML in a radio access network (RAN). For example, the AI/ML techniques can be adopted to boost the performance of the radio interface, reduce power consumption, or further improve user experience. However, the applications of AI/ML to wireless communications have been thus far limited to implementation-based approaches at the network or the UE sides, without any support from the 3GPP (the 3rd Generation Partnership Project) specifications. This results in a variety of implementations and UE behaviors from different vendors. Standardizing support for adopting AI/ML techniques can prove beneficial from a performance perspective because both the network and the UE sides can have consistent behavior for Life Cycle Management (LCM) operations. Specifically, LCM of AI/ML model (e.g., model training, model deployment, model inference, model monitoring, model updating) and AI/ML functionality can be controllable, and inference accuracy can be increased accordingly. The adoption of AI/ML technology in an RAN is opening a new era for creating more business value in terms of improved system performance, higher efficiency, and better user experience. It will create new business models and use cases for 5G and future generation mobile networks. Although it has been proven that the air interface can be improved by the support of AI/ML, the overall design of adopting AI/ML techniques in RAN for the air interface is still under discussion. In 3GPP Release 19, the target is to provide normative support for the general framework for AI/ML for air interface and to enable the recommended use cases in the preceding study (e.g., in 3GPP TR 38.843 v18), including beam management, positioning accuracy enhancements, and CSI (Channel Status Information) feedback enhancement. BM (Beam Management) is an example. The objective is to perform DL (Downlink) Tx beam prediction for a UE-side model and for a network (NW)-side model. A UE-side (AI/ML) model is an AI/ML Model whose inference is performed entirely at the UE. Conversely, an NW-side (AI/ML) model is an AI/ML Model whose inference is performed entirely at the network. As introduced in 3 GPP Rel-18 TR 38.843, FIG. 1 shows an example of the inference procedure for beam management for BM-Case1 and BM-Case2. Measurements based on Set B of beams are used as model input. In addition, beam ID information may also be provided as input to the AI/ML model. BM-Case1 is to perform Spatial-domain DL Tx beam prediction for Set A of beams based on measurement results of Set B of beams. That is, in the evaluation, the measurements of Set B are used as model input to predict Top-1/N beams from Set A. BM-Case2 is to perform temporal DL Tx beam prediction for Set A of beams based on the historic measurement results of Set B of beams. That is, in the evaluation, the measurements from historic time instances of Set B are used as model input for temporal DL beam prediction of beams from Set A. Based on model output (e.g., probability of each beam in Set A to be the Top-1 beam, predicted L1-RSRPs), Top-1/N beam(s) among Set A of beams can be predicted and/or potentially with predicted L1-RSRPs. RSRP stands for Reference Signal Receiving Power. Therefore, as stated in RP-242399, specification support of an AI/ML general framework for one-side AI/ML models (i.e., UE-side model or NW-side model) is required. The detailed designs for LCM to facilitate model training, inference, performance monitoring, and/or data collection for both UE-side and NW-side models are still under discussion. Therefore, there is a need to provide proper schemes to address this issue. SUMMARY The following summary is illustrative only and is not intended to be limiting in any way. That is, the following summary is provided to introduce concepts, highlights, benefits, and advantages of the novel and non-obvious techniques described herein. Select, not all, implementations are described further in the detailed description below. Thus, the f