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EP-4740572-A1 - METHOD, USER EQUIPMENT, ACCESS NETWORK NODE AND CORE NETWORK NODE

EP4740572A1EP 4740572 A1EP4740572 A1EP 4740572A1EP-4740572-A1

Abstract

An example of the object of the present disclosure is to provide a method, a user equipment, an access network node and a core network node capable of improving AI/ML data acquisition and/or transmission. In a first example aspect, a method performed by a user equipment, UE, the method includes: transmitting, to a first access network node, a first part of measurement result for Artificial Intelligence / Machine Learning, AI/ML, model training; and transmitting, to a second access network node, a second part of the measurement result for the AI/ML model training, wherein at least one of the first part of the measurement result or the second part of the measurement result includes information indicating a model or function for the AI/ML model training.

Inventors

  • WANG XUELONG
  • GUPTA NEERAJ

Assignees

  • NEC Corporation

Dates

Publication Date
20260513
Application Date
20240617

Claims (20)

  1. A method performed by a user equipment, UE, the method comprising: transmitting, to a first access network node, a first part of measurement result for Artificial Intelligence / Machine Learning, AI/ML, model training; and transmitting, to a second access network node, a second part of the measurement result for the AI/ML model training, wherein at least one of the first part of the measurement result or the second part of the measurement result includes information indicating a model or function for the AI/ML model training.
  2. The method according to claim 1, further comprising: receiving, from the first access network node, the information indicating the model or function for the AI/ML model training.
  3. The method according to claim 2, wherein the information indicating the model or function for the AI/ML model training is transmitted from a data collection entity which is connected with a plurality of access network nodes to the first access network node.
  4. The method according to claim 2 or 3, wherein the information indicating the model or function for the AI/ML model training is for transmitting the second part of the measurement result for the AI/ML model training.
  5. The method according to any one of claims 1 to 4, further comprising: performing measurements for the measurement result using at least one beam corresponding to the information indicating the model or function for the AI/ML model training.
  6. The method according to claim 5, wherein the information indicating the model or function for the AI/ML model training indicates at least one of: at least one Synchronization Signal/Physical Broadcast Channel, PBCH, block, SSB, or at least one channel state information reference signal, CSI-RS.
  7. The method according to any one of claims 1 to 6, wherein at least one of the first part of the measurement result or the second part of the measurement result includes information indicating a part of the measurement result which has been transmitted to the first access network node.
  8. The method according to claim 7, wherein the information indicating the part of the measurement result which has been transmitted to the first access network node is included in context information of the UE.
  9. The method according to claim 8, wherein the context information includes at least one of: a Radio Resource Control, RRC, context, or a Protocol Data Convergence Protocol, PDCP, context.
  10. The method according to any one of claims 7 to 9, further comprising: receiving, from the first access network node or the second access network node, the information indicating the part of the measurement result which has been transmitted to the first access network node.
  11. The method according to claim 10, wherein the information indicating the part of the measurement result which has been transmitted to the first access network node is transmitted from the first access network node to the second access network node.
  12. The method according to any one of claims 7 to 11, wherein the information indicating the part of the measurement result which has been transmitted to the first access network node includes at least one of: information of a Radio Resource Control, RRC, segment, or information of a Packet Data Convergence Protocol, PDCP, segment.
  13. The method according to any one of claims 7 to 12, wherein the transmitting the second part of the measurement result for the AI/ML model training is performed using the information indicating the part of the measurement result which has been transmitted to the first access network node.
  14. The method according to any one of claims 7 to 13, wherein the information indicating the part of the measurement result which has been transmitted to the first access network node is transmitted from the first access network node to the second access network node.
  15. The method according to any one of claims 1 to 14, further comprising: transmitting, to the first access network node or the second access network node, at least one of: information indicating that the UE has the second part of the measurement result for the AI/ML model training, a request for scheduling a resource for transmitting the second part of the measurement result for the AI/ML model training, or an establishment cause indicating a purpose of transmitting a measurement result for the AI/ML model training, after transmitting the first part of the measurement result for the AI/ML model training.
  16. The method according to any one of claims 1 to 14, further comprising: receiving, from the first access network node or the second access network node, a request for the UE to transmit the second part of the measurement result for the AI/ML model training until the UE finishes to transmit the first part of the measurement result for the AI/ML model training.
  17. The method according to any one of claims 1 to 16, wherein declaring a Radio Link Failure, RLF, after transmitting the first part of the measurement result for the AI/ML model training; and establishing or resuming a Radio Resource Control, RRC, connection, and wherein the transmitting the second part of the measurement result for the AI/ML model training after the declaring the RLF is performed after the establishing or resuming the RRC connection.
  18. The method according to any one of claims 1 to 17, wherein at least one of the first part of the measurement result or the second part of the measurement result is transmitted on at least one of: a data radio bearer with a specific priority for transmitting measurement results for the AI/ML training, a specific radio bearer with which a special type of logical channel, LCH is defined, or a specific radio bearer which terminates between the UE and the first access network node or the second access network node.
  19. The method according to any one of claims 1 to 18, wherein the first part of the measurement result includes measurement result while the UE is in a Radio Resource Control, RRC, connected state, the second part of the measurement result includes measurement result while the UE is in a RRC idle state or a RRC inactive state, and the transmitting the first part of the measurement result and the transmitting the second part of the measurement result are performed while the UE is in the RRC connected state.
  20. The method according to claim 19, wherein in a case where the UE moves from the RRC connected state to the RRC idle state or the RRC inactive state, receiving configuration information for transmitting the second part of the measurement result for the AI/ML model training.

Description

METHOD, USER EQUIPMENT, ACCESS NETWORK NODE AND CORE NETWORK NODE The present disclosure relates to a method, a user equipment, an access network node and a core network node. Under the 3GPP standards, a NodeB (or an eNB in LTE, gNB in 5G) is the radio access network (RAN) node (or simply 'access node', 'access network node' or 'base station') via which communication devices (user equipment or 'UE') connect to a core network and communicate with other communication devices or remote servers. Example embodiments of the disclosure will now be described, by way of example, with reference to the accompanying drawings in which: Fig. 1 schematically illustrates a mobile ('cellular' or 'wireless') communication system; Fig. 2 illustrates a typical frame structure that may be used in the communication system of Fig. 1; Fig. 3 is a schematic block diagram illustrating the main components of a DU 50 that may be used as part of the RAN node 5 for the communication system 1 shown in Fig. 1; Fig. 4 is a schematic block diagram illustrating the main components of a CU 60 that may be used as part of the RAN node 5 for the communication system 1 shown in Fig. 1; Fig. 5 shows a mobility procedure in which handover occurs from a source (R)AN node to a target (R)AN node; Fig. 6 illustrates a framework in respect of an AI/ML model; Fig. 7 shows an illustration of a method of training an AI/ML model, and of monitoring the performance of the AI/ML model; Fig. 8 shows an example of an AI/ML request and an AI/ML response; Fig. 9 shows an example of an AI/ML information update; Fig. 10 shows a method for determining, at a base station, whether AI/ML data collection is allowed at a UE; Fig. 11 shows a method of RRC-based AI/ML data collection; Fig. 12 illustrates a method in which the remaining PDCP packets are transmitted to the target base station when transmission of the AI/ML data is interrupted by handover; Fig. 13 shows an example of AI/ML data collection when the UE transitions between different RRC states; Fig. 14 is a schematic block diagram illustrating the main components of a UE for the communication system of Fig. 1; Fig. 15 is a schematic block diagram illustrating the main components of a base station for the communication system of Fig. 1; and Fig. 16 is a schematic block diagram illustrating the main components of a core network node or function for the communication system of Fig. 1. The present disclosure relates to a communication system. The disclosure has particular but not exclusive relevance to wireless communication systems and devices thereof operating according to the 3rd Generation Partnership Project (3GPP) standards or equivalents or derivatives thereof (including LTE-Advanced, Next Generation or 5G networks, future generations, and beyond). The disclosure has particular, although not necessarily exclusive, relevance to data collection for artificial intelligence and machine learning (AI/ML) models used in 'New Radio' systems (also referred to as 'Next Generation' systems), and similar systems. (Related Arts) 　　Recent developments of the 3GPP standards are referred to as the Long-Term Evolution (LTE) of Evolved Packet Core (EPC) network and Evolved UMTS Terrestrial Radio Access Network (E-UTRAN), also commonly referred as '4G'. In addition, the term '5G' and 'new radio' (NR) refer to an evolving communication technology that is expected to support a variety of applications and services. Various details of 5G networks are described in, for example, the 'NGMN 5G White Paper' V1.0 by the Next Generation Mobile Networks (NGMN) Alliance, which document is available from https://www.ngmn.org/5g-white-paper.html. 3GPP intends to support 5G by way of the so-called 3GPP Next Generation (NextGen) radio access network (RAN) and the 3GPP NextGen core network. Under the 3GPP standards, a NodeB (or an eNB in LTE, gNB in 5G) is the RAN node (or simply 'access node', 'access network node' or 'base station') via which communication devices (user equipment or 'UE') connect to a core network and communicate with other communication devices or remote servers. For simplicity, the present application will use the term RAN node, base station, or access network node to refer to any such access nodes. Some of the additional developments in 3GPP relate to the use of artificial intelligence (AI) and machine learning (ML), often abbreviated to AI/ML. Predictions or inferences generated using an AI/ML model can be used as part of various methods for improving the reliability or efficiency of communications in the network. For example, AI/ML models can be used to predict the path of a UE based on previous mobility of the UE, used for beam management, or used in methods of encoding and transmitting information. An AI/ML model may be hosted at a base station (or any other suitable network node), and the base station may perform control of communication resources or control related to the status of a UE (e.g. control of UE mobility, o