US-20260129415-A1 - WIRELESS COMMUNICATION METHOD AND APPARATUS OF SUPPORTING ARTIFICIAL INTELLIGENCE

Abstract

Embodiments of the present application relate to a wireless communication method and apparatus of supporting artificial intelligence. An exemplary method may include: receiving, first configuration information on a protocol layer responsible for artificial intelligence (AI) management in access stratum, from a network side; receiving, indication information on a set of AI operations from the network side; and transmitting, feedback on at least one of the set of AI operations to the network side, via a message on the protocol layer based on the first configuration information.

Inventors

• Congchi ZHANG
• Jianfeng Wang
• Mingzeng Dai
• Le Yan
• Haiming Wang

Assignees

• LENOVO (BEIJING) LIMITED

Dates

Publication Date

20260507

Application Date

20211101

Claims (20)

1. 1 . A user equipment (UE) for wireless communication, comprising: at least one memory; and at least one processor coupled to the at least one memory, and configured to cause the UE to: receive, from a network entity, first configuration information on a protocol layer responsible for artificial intelligence (AI) management in access stratum; receive, from the network entity, indication information on a set of AI operations from the network side; and transmit, to the network entity, feedback on at least one of the set of AI operations via a message on the protocol layer based on the first configuration information.
2. 2 . The UE of claim 1 , wherein, the first configuration information indicates at least one of the following: a list of descriptions about AI operations that a base station can provide or want the UE to support; a list of indexes about AI operations that a base station can provide or want UE to support; an index identifying a current entity of the protocol layer; associated radio bearer IDs to identify an associated protocol data convergence protocol (PDCP) entity; an indication to update an AI operations list in a base station; or rules to provide AI model performance feedback to a base station.
3. 3 . The UE of claim 1 , wherein, the at least one processor is further configured to cause the UE to: receive third configuration information on a radio link control (RLC) entity associated with an entity of the protocol layer, wherein the RLC entity is always configured in a RLC acknowledged mode or is in a default RLC acknowledged mode.
4. 4 . The UE of claim 1 , wherein, for the set of AI operations, at least one entity of the protocol layer is configured as follows: when there is at least one AI task in the set of AI operations, a corresponding entity of the protocol layer is configured for each AI task, and when there is at least one AI model in the set of AI operations, a corresponding entity of the protocol layer is configured for each AI model; or only one entity of the protocol layer is configured for the set of AI operations.
5. 5 . The UE of claim 1 , wherein, the indication information indicates a requirement on the set of AI operations in a radio resource control (RRC) message or a message of the protocol layer, and the at least one of the set of AI operations is adopted by the UE based on implementation of the UE.
6. 6 . The UE of claim 1 , wherein, the at least one processor is further configured to cause the UE to: receive a first message including indication information on the set of AI operations; transmit a second message indicating to subscribe at least part of the set of AI operations; and receive a third message indicating the at least one of the set of AI operations.
7. 7 . The UE of claim 6 , wherein, when that the protocol layer is configured for the UE before receiving the first message, the first message is one of a radio resource control (RRC) message, a system information block (SIB), and a message of the protocol layer; the second message is one of an RRC message and a message of the protocol layer; and the third message is a message of the protocol layer.
8. 8 . The UE of claim 6 , wherein, when the protocol layer is configured for the UE after receiving the first message and before transmitting the second message, the first message is one of a radio resource control (RRC) message and system information block (SIB); the second message is one of a RRC message and a message of the protocol layer; and the third message is a message of the protocol layer.
9. 9 . The UE of claim 6 , wherein, when the protocol layer is configured for the UE after transmitting the second message and before receiving the third message, the first message is one of a radio resource control (RRC) message and system information block (SIB); the second message is a RRC message; and the third message is a message of the protocol layer.
10. 10 . The UE of claim 6 , wherein, when the protocol layer is configured for the UE after receiving the third message, the first message is one of a radio resource control (RRC) message and system information block (SIB); and the second message and the third message are RRC messages.
11. 11 . The UE of claim 1 , wherein, a control protocol data unit (PDU) of the protocol layer comprises information indicating at least one of the following: message type; subscribe or unsubscribe; index of AI task wanted to subscribe or unsubscribe; index of AI model wanted to subscribe or unsubscribe; and list of index of available AI operations from a base station.
12. 12 . The UE of claim 1 , wherein, a data protocol data unit (PDU) of the protocol layer comprises information indicating at least one of the following: message type; AI task index; AI model index; input AI task index input AI model index; output AI task index; output AI model Index; and model payload.
13. 13 . The UE of claim 1 , wherein, a message format of the protocol layer comprises: control protocol data unit (PDU) and data PDU, wherein the control PDU and data PDU are sent over different logical channels (LCHs) and radio bearers.
14. 14 . A network apparatus, comprising: at least one memory; and at least one processor coupled to the at least one memory and and configured to cause the network apparatus to: transmit, to a user equipment (UE), first configuration information on a protocol layer responsible for artificial intelligence (AI) management in access stratum; transmit, to the UE, indication information on a set of AI operations; and receive, from the UE, feedback on at least one of the set of AI operations via a message on the protocol layer based on the first configuration information.
15. 15 . A method performed by a user equipment (UE, the method comprising: receiving, from a network entity, first configuration information on a protocol layer responsible for artificial intelligence (AI) management in access stratum; receiving, from the network entity, indication information on a set of AI operations; and transmitting, to the network entity, feedback on at least one of the set of AI operations via a message on the protocol layer based on the first configuration information.
16. 16 . A processor for wireless communication, comprising: at least one controller coupled to at least one memory and configured to cause the processor to: receive, from a network entity, first configuration information on a protocol layer responsible for artificial intelligence (AI) management in access stratum; receive, from the network entity, indication information on a set of AI operations; and transmit, to the network entity, feedback on at least one of the set of AI operations via a message on the protocol layer based on the first configuration information.
17. 17 . The processor of claim 16 , wherein the first configuration information indicates at least one of the following: a list of descriptions about AI operations that a base station can provide or want the processor to support; a list of indexes about AI operations that a base station can provide or want the processor to support; an index identifying a current entity of the protocol layer; associated radio bearer IDs to identify an associated protocol data convergence protocol (PDCP) entity; an indication to update an AI operations list in a base station; or rules to provide AI model performance feedback to a base station.
18. 18 . The processor of claim 16 wherein the at least one controller is further configured to cause the processor to: receive third configuration information on a radio link control (RLC) entity associated with an entity of the protocol layer, wherein the RLC entity is always configured in a RLC acknowledged mode or is in a default RLC acknowledged mode.
19. 19 . The processor of claim 16 , wherein, for the set of AI operations, at least one entity of the protocol layer is configured as follows: when there is at least one AI task in the set of AI operations, a corresponding entity of the protocol layer is configured for each AI task, and when there is at least one AI model in the set of AI operations, a corresponding entity of the protocol layer is configured for each AI model; or only one entity of the protocol layer is configured for the set of AI operations.
20. 20 . The processor of claim 16 , wherein the indication information indicates a requirement on the set of AI operations in a radio resource control (RRC) message or a message of the protocol layer, and the at least one of the set of AI operations is adopted by the processor based on implementation of the processor.

Description

TECHNICAL FIELD Embodiments of the present application are related to wireless communication technology, especially, related to artificial intelligence (AI) application in wireless communication, e.g., a wireless communication method and apparatus of supporting AI. BACKGROUND OF THE INVENTION AI, at least including machine learning (ML) is used to learn and perform certain tasks via training neural networks (NNs) with vast amounts of data, which is successfully applied in computer vison (CV) and nature language processing (NLP) areas. Deep learning (DL), which is a subordinate concept of ML, utilizes multi-layered NNs as an “AI model” to learn how to solve problems and/or optimize performance from vast amounts of data. If AI models used on AI-based methods are well trained, the AI-based methods can obtain better performance than traditional methods. Thus, 3rd generation partnership program (3GPP) has been considering to introduce AI into 3GPP since 2016, including several study items and work items in SA1, SA2, SA5 and RAN3. In the current 3GPP radio access network (RAN) release (R) 18 candidate proposal discussions, an AI model can be used to optimize user equipment (UE) and RAN operation in access stratum (AS), which includes: UE can use an AI model to better estimate the channel condition at physical (PHY) layer; and RAN nodes can use an AI model to predict UE mobility and make proper handover decisions. Thus, interactions between UE and RAN nodes are needed to exchange AI models and/or relevant parameters in some cases, for example: AI models used by UE are provided by the RAN node to do channel condition estimation; and the AI model used by the RAN node to predict UE mobility (i.e., a mobility prediction model) can be trained in a distributed learning way. An exemplary distributed learning way is: a number of UEs can assist the RAN node to train the mobility prediction model using their local data and send the updated model parameters back to the RAN node. Comparing with the conventional AI model distributions (or operations) at the application layer, e.g., AI model distributions or operations for autonomous driving, AI models for AS operations as illustrated above may only be applicable in the access stratum and does not need to be aware by the application layer. However, neither the user plane (UP) nor control plane (CP) in current access stratum is appropriate to support interactions associated with AI task and AI model between UE and the RAN node. Therefore, how to handle the AI model distributions (or operations) and AI task participation (e.g., distributed learning) in access stratum should be solved. SUMMARY One objective of the embodiments of the present application is to provide a technical solution for wireless communication, especially for supporting AI in wireless communication, which can solve the technical problem on how to handle AI model distribution and AI task participation in AS. Some embodiments of the present application provide a UE, which includes: at least one receiving circuitry; at least one transmitting circuitry; and at least one processor coupled to the at least one receiving circuitry and the at least one transmitting circuitry, wherein the at least one processor is configured to: receive, via the at least one receiving circuitry, first configuration information on a protocol layer responsible for AI management in access stratum from a network side; receive, via the at least one receiving circuitry, indication information on a set of AI operations from the network side; and transmit, via the at least one transmitting circuitry, feedback on at least one of the set of AI operations to the network side, via a message on the protocol layer based on the first configuration information. In some embodiments of the present application, each AI operation of the set of AI operations is related to at least one of: AI task or AI model. In some embodiments of the present application, the first configuration information is transmitted via a radio resource control (RRC) message. In some embodiments of the present application, the first configuration information indicates at least one of the following: a list of descriptions about AI operations that a base station can provide or want UE to support; a list of indexes about AI operations that a base station can provide or want UE to support; an index identifying a current entity of the protocol layer; associated radio bearer ID(s) to identify an associated protocol data convergence protocol (PDCP) entity; an indication to update an AI operations list in a base station; or rules to provide AI model performance feedback to a base station. In some embodiments of the present application, the at least one processor is further configured to: receive, via the at least one receiving circuitry, second configuration information on a PDCP entity associated with an entity of the protocol layer, wherein the second information indicates at least one of t