WO2025168471A1 - Method of rrc state-based online training signaling - Google Patents

Abstract

The present disclosure describes methods of using the pre-configured AI/ML (artificial intelligence/machine learning) based model training in wireless mobile communication system including base station (e.g., gNB) and mobile station (e.g., UE). In AI/ML model is applied to radio access network, signaling of model information exchange can be heavily congested based on different UE RRC states. Therefore, model operation (e.g., model re-training/updating) can be set up between network and UE by selecting different training modes adaptively.

Description

TITLE

Method of RRC state-based online training signaling

TECHNNICAL FIELD

The present disclosure relates to AI/ML based model online training for radio resource control states, where techniques for pre-configuring and signaling the specific information about model online training applicable to radio access network are presented.

BACKGROUND

In 3GPP (Third Generation Partnership Project), one of the selected study items as the approved Release 18 package is AI/ML (artificial intelligence/machine learning) as described in the related document (RP-213599) addressed in 3GPP TSG (Technical Specification Group) RAN (Radio Access Network) meeting #94e. The official title of AI/ML study item is “Study on AI/ML for NR Air Interface”. The goal of this study item is to identify a common AI/ML framework and areas of obtaining gains using AI/ML based techniques with use cases. According to 3GPP, the main objective of this study item is to study AI/ML framework for air-interface with target use cases by considering performance, complexity, and potential specification impact. In particular, AI/ML model, terminology and description to identify common and specific characteristics for framework are included as one of key work scopes. Regarding AI/ML framework, various aspects are under consideration for investigation and one of key items is about lifecycle management of AI/ML model where multiple stages are included as mandatory for model training, model deployment, model inference, model monitoring, model updating etc. Currently, AI/ML specification work is at the stage of work item discussion for Release 19. Earlier, in 3GPP TR 37.817 for Release 17, titled as Study on enhancement for Data Collection for NR and EN-DC, UE (user equipment) mobility was also considered as one of AI/ML use cases and one of scenarios for model training/inference is that both functions are located within RAN node. Followingly, in Release 18 the new work item of “Artificial Intelligence (AI)ZMachine Learning (ML) for NG-RAN” was initiated to specify data collection enhancements and signaling support within existing NG-RAN interfaces and architecture. For the above active standardization works, RAN-based AI/ML model is considered very significant for both network and UE to meet any desired model operations (e.g., model training, inference, selection, switching, update, monitoring, etc.). Model information can be signaled to pair both networkside and UE-side models for various lifecycle management (LCM) operations.

However, signaling overhead indicating model information can be very high especially when model based LCM is processed between base station (BS/gNB) and multiple UEs. In LCM, model training is one of the most important parts for model deployment and currently there is no specification defined for signaling methods and network-UE behaviors so as to identify the required dataset when model updating/re- training as any activated model can be also impacted due to model/data drift. When AI/ML-enabled UE mobility occurs (due to moving around in different locations), the enabled AI/ML model(s) can be impacted for model performance due to data/model drift. In this case, model re-training/updating can be executed. However, if model is re-trained with original full features/dataset configuration, high signaling overhead and/or high compute power can be very challenging. At UE side, radio resource control (RRC) state can change by switching from one state to another and ML operation with specific LCM stage for UE needs to consider UE RRC state status to achieve target performance.

US2023042545A1 describes a method performed by a network node for a wireless telecommunications network performs operations including providing a resource allocation model that corresponds to a base station and that provides a recommendation regarding resource allocation for UE that is in an operating zone of the base station during a limited resource.

WO20231 44831 A1 describes a method to perform life cycle management of at least one machine learning, ML, model for telecommunications dimensioning in a network where the method includes performing to determine that a performance of a current ML model is not acceptable for a forecast of telecommunications dimensioning with selecting minimal informative dataset.

WO2023173296A1 describes that a device is selectively included or excluded from participating in an online training procedure based on the device's currently reported learning capabilities in order to provide a tradeoff between overhead reductions and training performance.

WO2023216043A1 describes that a UE may measure a plurality of wireless channel features over a period of time to train a machine learning model associated with UE mobility state where UE may identify a UE mobility state of the UE based on the plurality of wireless channel features and the machine learning model.

US2023319656A1 describes a method for transmitting and receiving data by a terminal in a wireless communication system where the configuration information includes information about a receiver model and a zone to which the base station belongs.

The present disclosure solves the cited problem by the proposed embodiments and describes a method of RRC state-based online training signaling, comprising the steps, configuring online training modes, wherein the modes can be the preconfigured index information about combinations of model collaboration types, which can be two-sided model and one-sided model, and RRC states, wherein the RRC- States are in connected and/or active state and/or inactive state and/or idle state; transmitting the configured online training modes to UE based on UE RRC state switching via system information or dedicated RRC signaling; performing online training operation according to RRC state conditions with the associated assistance information about ML configuration based on the specific index indication message.

In some embodiments of the method according to the first aspect, the method is characterized by, that the online training modes are formed as multiple versions of look-up tables or list of index-based information in advance by considering a set of information about ML applications, LCM, ML applicable conditions, ML functionality and model characteristics along with UE ML capability. In some embodiments of the method according to the first aspect, the method is characterized by, that the online training mode is applied to UE by adapting to different combinations of RRC states and one-/two-sided model types.

In some embodiments of the method according to the first aspect, the method is characterized by, that any further updated online training mode configuration can be sent to UE via system information and/or RRC or L1/L2 signaling according to UE RRC state status.

In some embodiments of the method according to the first aspect, the method is characterized by, that there are multiple methods of decision on online training mode activation, comprising the steps network-assisted method: specific online training mode is determined by network side; UE autonomous method : specific online training mode is determined by UE autonomously by sending the related update report to gNB after the selected mode execution; hybrid method : specific online training mode is indicated to UE by network side and UE can accept it if matched with UE's mode selection or reject it by applying alternative mode if not matched with UE's mode selection; whereby the method is applied to other LCM phases such as dataset collection, model inferencing, if necessary when the pre-configured mode selection information is generated in advance for each LCM phases.

In some embodiments of the method according to the first aspect, the method is characterized by, that depending on different LCM phases, online training modes can be extended to other ML model activities, whereby ML model activities can be inferencing, updating, dataset collection by using the same combinations of model collaboration types and RRC states, whereby these can be look-up table and/or index-based indication.

In some embodiments of the method according to the first aspect, the method is characterized by, that the pre-configured index table about combinations of model collaboration types and RRC states can be sent to UE via system information or dedicated RRC signaling. In some embodiments of the method according to the first aspect, the method is characterized by, that the size of the pre-configured index table can be configured for different number of online training modes based on various implementation-specific applications or deployments.

In some embodiments of the method according to the first aspect, the method is characterized by, that UE group can be assigned to activate any specific online training mode by sending configuration information about online training modes via multicast signaling if there are a number of UEs to apply online training modes.

In some embodiments of the method according to the first aspect, the method is characterized by, that UE RRC state can be switched between connected state and inactive/idle state in periodic or aperiodic way so that two-sided model collaboration can be operated.

In some embodiments of the method according to the first aspect, the method is characterized by, that the related ML configuration, which is a ML assistance information, is required to provide to UE such as frequency of online learning operation with RRC state switching for re-connection.

In some embodiments of the method according to the first aspect, the method is characterized by, that UE can apply the indicated online training mode sent by gNB directly or UE can apply the selected online training mode autonomously based on the pre-configured criteria given by network.

T In some embodiments of the method according to the first aspect, the method is characterized by, that indication of online training mode can be sent via L1/L2/L3 signaling together with RRC message or separately with L1/L2 signaling.

In some embodiments of the method according to the first aspect, the method is characterized by, that online training update at UE is sent to gNB when RRC state is switched from inactive/idle state to connected state. In some embodiments of the method according to the first aspect, the method is characterized by, that UE RRC state can be switched between connected state and inactive/idle state in periodic or aperiodic way with the iterative online training activation so that ML update information can be shared and ML reconfiguration can be provided between network side and UE side.

According to a second aspect, the present disclosure relates to an apparatus for RRC state-based online training signaling in a wireless communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to execute the steps according to any one of the embodiments of the first aspect

According to a third aspect, the present disclosure relates to an user equipment comprising an apparatus to any one of the embodiments of the second aspect.

According to a fourth aspect, the present disclosure relates to a gNB comprising an apparatus according to any one of the embodiments of the second aspect.

According to a fourth aspect, the present disclosure relates to a wireless communication system for RRC state-based online training signaling, wherein the wireless communication systems comprises user equipment according to the third aspect, gNB according to the fourth aspect, whereby the user Equipment and the gNB each comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to proceed the steps according to any one of the embodiments of the first aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is an exemplary table of online training modes.

Figure 2 is an exemplary flow chart of configuring online training modes at network side.

Figure 3 is an exemplary flow chart of activating online training mode at UE side. Figure 4 is an exemplary signaling flow of activating online training mode with RRC state switching. Figure 5 is an exemplary signaling flow of updating ML information based on online training with RRC state switching.

Figure 6 is an exemplary signaling flow of iterative online training activation with RRC state switching.

DETAILED DESCRIPTION

The detailed description set forth below, with reference to annexed drawings, is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. In particular, although terminology from 3GPP 5G NR may be used in this disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the invention.

Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

In some embodiments, a more general term “network node” may be used and may correspond to any type of radio network node or any network node, which communicates with a UE (directly or via another node) and/or with another network node. Examples of network nodes are NodeB, MeNB, ENB, a network node belonging to MCG or SCG, base station (BS), multi-standard radio (MSR) radio node such as MSR BS, eNodeB, gNodeB, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base transceiver station (BTS), access point (AP), transmission points, transmission nodes, RRU, RRH, nodes in distributed antenna system (DAS), core network node (e.g. Mobile Switching Center (MSC), Mobility Management Entity (MME), etc), Operations & Maintenance (O&M), Operations Support System (OSS), Self Optimized Network (SON), positioning node (e.g. Evolved- Serving Mobile Location Centre (E-SMLC)), Minimization of Drive Tests (MDT), test equipment (physical node or software), etc.

In some embodiments, the non-limiting term user equipment (UE) or wireless device may be used and may refer to any type of wireless device communicating with a network node and/or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communication, PDA, PAD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, UE category Ml, UE category M2, ProSe UE, V2V UE, V2X UE, etc.

Additionally, terminologies such as base station/gNodeB and UE should be considered non-limiting and do in particular not imply a certain hierarchical relation between the two; in general, “gNodeB” could be considered as device 1 and “UE” could be considered as device 2 and these two devices communicate with each other over some radio channel. And in the following the transmitter or receiver could be either gNodeB (gNB), or UE.

As will be appreciated by one skilled in the art, aspects of the embodiments may be embodied as a system, apparatus, method, or program product. Accordingly, embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects.

For example, the disclosed embodiments may be implemented as a hardware circuit comprising custom very-large-scale integration (“VLSI”) circuits or gate arrays, off- the-shelf semiconductors such as logic chips, transistors, or other discrete components. The disclosed embodiments may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, or the like. As another example, the disclosed embodiments may include one or more physical or logical blocks of executable code which may, for instance, be organized as an object, procedure, or function.

Furthermore, embodiments may take the form of a program product embodied in one or more computer readable storage devices storing machine readable code, computer readable code, and/or program code, referred hereafter as code. The storage devices may be tangible, non- transitory, and/or non-transmission. The storage devices may not embody signals. In a certain embodiment, the storage devices only employ signals for accessing code

Any combination of one or more computer readable medium may be utilized. The computer readable medium may be a computer readable storage medium. The computer readable storage medium may be a storage device storing the code. The storage device may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the storage device would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random-access memory (“RAM”), a read-only memory (“ROM”), an erasable programmable read-only memory (“EPROM” or Flash memory), a portable compact disc readonly memory (“CD-ROM”), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

Code for carrying out operations for embodiments may be any number of lines and may be written in any combination of one or more programming languages including an object- oriented programming language such as Python, Ruby, Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the “C” programming language, or the like, and/or machine languages such as assembly languages. The code may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (“LAN”), wireless LAN (“WLAN”), or a wide area network (“WAN”), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider (“ISP”)).

Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments. One skilled in the relevant art will recognize, however, that embodiments may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of an embodiment. Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment, but mean “one or more but not all embodiments” unless expressly specified otherwise. The terms “including,” “comprising,” “having,” and variations thereof mean “including but not limited to,” unless expressly specified otherwise. An enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms “a,” “an,” and “the” also refer to “one or more” unless expressly specified otherwise.

Aspects of the embodiments are described below with reference to schematic flowchart diagrams and/or schematic block diagrams of methods, apparatuses, systems, and program products according to embodiments. It will be understood that each block of the schematic flowchart diagrams and/or schematic block diagrams, and combinations of blocks in the schematic flowchart diagrams and/or schematic block diagrams, can be implemented by code. This code may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the fimctions/acts specified in the flowchart diagrams and/or block diagrams

The code may also be stored in a storage device that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the storage device produce an article of manufacture including instructions which implement the function/act specified in the flowchart diagrams and/or block diagrams.

The code may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer implemented process such that the code which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart diagrams and/or block diagrams.

The flowchart diagrams and/or block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, systems, methods, and program products according to various embodiments. In this regard, each block in the flowchart diagrams and/or block diagrams may represent a module, segment, or portion of code, which includes one or more executable instructions of the code for implementing the specified logical function(s).

It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more blocks, or portions thereof, of the illustrated Figures.

Although various arrow types and line types may be employed in the flowchart and/or block diagrams, they are understood not to limit the scope of the corresponding embodiments. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the depicted embodiment. For instance, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted embodiment. It will also be noted that each block of the block diagrams and/or flowchart diagrams, and combinations of blocks in the block diagrams and/or flowchart diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and code. The description of elements in each figure may refer to elements of proceeding figures. Like numbers refer to like elements in all figures, including alternate embodiments of like elements.

The detailed description set forth below, with reference to the figures, is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. For instance, although 3GPP terminology, from e.g., 5G NR, may be used in this disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the present disclosure.

The disclosure is related to wireless communication system, which may be for example a 5G NR wireless communication system. More specifically, it represents a RAN of the wireless communication system, which is used exchange data with UEs via radio signals. For example, the RAN may send data to the UEs (downlink, DL), for instance data received from a core network (CN). The RAN may also receive data from the UEs (uplink, UL), which data may be forwarded to the CN.

In the examples illustrated, the RAN comprises one base station, BS. Of course, the RAN may comprise more than one BS to increase the coverage of the wireless communication system. Each of these BSs may be referred to as NB, eNodeB (or eNB), gNodeB (or gNB, in the case of a 5G NR wireless communication system), an access point or the like, depending on the wireless communication standard(s) implemented.

The UEs are located in a coverage of the BS. The coverage of the BS corresponds for example to the area in which UEs can decode a PDCCH transmitted by the BS.

An example of a wireless device suitable for implementing any method, discussed in the present disclosure, performed at a UE corresponds to an apparatus that provides wireless connectivity with the RAN of the wireless communication system, and that can be used to exchange data with said RAN. Such a wireless device may be included in a UE. The UE may for instance be a cellular phone, a wireless modem, a wireless communication device, a handheld device, a laptop computer, or the like. The UE may also be an Internet of Things (loT) equipment, like a wireless camera, a smart sensor, a smart meter, smart glasses, a vehicle (manned or unmanned), a global positioning system device, etc., or any other equipment that may run applications that need to exchange data with remote recipients, via the wireless device.

The wireless device comprises one or more processors and one or more memories. The one or more processors may include for instance a central processing unit (CPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc. The one or more memories may include any type of computer readable volatile and non-volatile memories (magnetic hard disk, solid-state disk, optical disk, electronic memory, etc.). The one or more memories may store a computer program product, in the form of a set of programcode instructions to be executed by the one or more processors to implement all or part of the steps of a method for exchanging data, performed at a UE’s side, according to any one of the embodiments disclosed herein.

The wireless device can comprise also a main radio, MR, unit. The MR unit corresponds to a main wireless communication unit of the wireless device, used for exchanging data with BSs of the RAN using radio signals. The MR unit may implement one or more wireless communication protocols, and may for instance be a 3G, 4G, 5G, NR, WiFi, WiMax, etc. transceiver or the like. In preferred embodiments, the MR unit corresponds to a 5G NR wireless communication unit.

AI/ML Model is a data driven algorithm that applies AI/ML techniques to generate set of outputs based on set of inputs. AI/ML model delivery is a generic term referring to delivery of an AI/ML model from one entity to another entity in any manner. Note is An entity could mean network node/function (e.g., gNB, LMF, etc.), UE, proprietary server, etc.

AI/ML model Inference is a process of using trained AI/ML model to produce set of outputs based on set of inputs.

AI/ML model testing is a subprocess of training, to evaluate the performance of final AI/ML model using dataset different from one used for model training and validation. Differently from AI/ML model validation, testing does not assume subsequent tuning of the model.

AI/ML model training is a process to train an AI/ML Model [by learning the input/output relationship] in data driven manner and obtain the trained AI/ML Model for inference.

AI/ML model transfer is a delivery of an AI/ML model over the air interface in manner that is not transparent to 3GPP signalling, either parameters of model structure known at the receiving end or new model with parameters. Delivery may contain full model or partial model.

AI/ML model validation is a subprocess of training, to evaluate the quality of an AI/ML model using dataset different from one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training.

Data collection is a process of collecting data by the network nodes, management entity, or UE for the purpose of AI/ML model training, data analytics and inference.

Federated learning I federated training is a machine learning technique that trains an AI/ML model across multiple decentralized edge nodes e.g., UEs, gNBs each performing local model training using local data samples. The technique requires multiple interactions of the model, but no exchange of local data samples. Functionality identification is a process/method of identifying an AI/ML functionality for the common understanding between the NW and the UE. Note is Information regarding the AI/ML functionality may be shared during functionality identification. Where AI/ML functionality resides depends on the specific use cases and sub use cases.

Model activation means enable an AI/ML model for specific AI/ML-enabled feature.

Model deactivation means disable an AI/ML model for specific AI/ML-enabled feature.

Model download means Model transfer from the network to UE.

Model identification is A process/method of identifying an AI/ML model for the common understanding between the NW and the UE. The process/method of model identification may or may not be applicable and regarding the AI/ML model may be shared during model identification.

Model monitoring is A procedure that monitors the inference performance of the AI/ML model.

Model parameter update is Process of updating the model parameters of model. Model selection is the process of selecting an AI/ML model for activation among multiple models for the same AI/ML enabled feature. Model selection may or may not be carried out simultaneously with model activation.

Model switching is deactivating currently active AI/ML model and activating different AI/ML model for specific AI/ML-enabled feature.

Model update is Process of updating the model parameters and/or model structure of model.

Model upload is Model transfer from UE to the network. Network-side (AI/ML) model is an AI/ML Model whose inference is performed entirely at the network.

Offline field data is the data collected from field and used for offline training of the AI/ML model.

Offline training is an AI/ML training process where the model is trained based on collected dataset, and where the trained model is later used or delivered for inference. Note is This definition only serves as guidance. There may be cases that may not exactly conform to this definition but could still be categorized as offline training by commonly accepted conventions.

Online field data is the data collected from field and used for online training of the AI/ML model.

Online training is an AI/ML training process where the model being used for inference) is (typically continuously) trained in (near) real-time with the arrival of new training samples. Note is the notion of (near) real-time vs. non real-time is context- dependent and is relative to the inference time-scale. This definition only serves as guidance.

There may be cases that may not exactly conform to this definition but could still be categorized as online training by commonly accepted conventions. Note is Fine- tuning/re-training may be done via online or offline training. This note could be removed when we define the term fine-tuning.

Reinforcement Learning (RL) is a process of training an AI/ML model from input (a.k.a. state) and feedback signal (a.k.a. reward) resulting from the model’s output (a.k.a. action) in an environment the model is interacting with.

Semi-supervised learning is a process of training model with mix of labelled data and unlabelled data. Supervised learning is a process of training model from input and its corresponding labels.

Two-sided (AI/ML) model is a paired AI/ML Model(s) over which joint inference is performed, where joint inference comprises AI/ML Inference whose inference is performed jointly across the UE and the network, i.e, the first part of inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa.

UE-side (AI/ML) model is an AI/ML Model whose inference is performed entirely at the UE.

Unsupervised learning is a process of training model without labelled data.

Proprietary-format models is ML models of vendor-Zdevice-specific proprietary format, from 3GPP perspective. They are not mutually recognizable across vendors and hide model design information from other vendors when shared.

Open-format models is ML models of specified format that are mutually recognizable across vendors and allow interoperability, from 3GPP perspective. They are mutually recognizable between vendors and do not hide model design information from other vendors when shared.

The following explanation will provide the detailed description of the mechanism about pre-configuring and signaling the specific information about model online training with different RRC states. AI/ML based techniques are currently applied to many different applications and 3GPP also started to work on its technical investigation to apply to multiple use cases based on the observed potential gains. AI/ML lifecycle can be split into several stages such as data collection/pre- processing, model training, model testing/validation, model deployment/update, model monitoring etc., where each stage is equally important to achieve target performance with any specific model(s). In applying AI/ML model for any use case or application, one of the challenging issues is to manage the lifecycle of AI/ML model. It is mainly because the data/model drift occurs during model deployment/inference and it results in performance degradation of AI/ML model. Fundamentally, the dataset statistical changes occur after model is deployed and model inference capability is also impacted with unseen data as input. In a similar aspect, the statistical property of dataset and the relationship between input and output for the trained model can be changed with drift occurrence. In this context, model training or re-training is one of key issues for model performance maintenance as model performance such as inferencing and/or training is dependent on different model execution environment with varying configuration parameters. To handle this issue, collaboration between UE and gNB is highly important to track model performance and re-configure model corresponding to different environments. AI/ML model needs model monitoring after deployment because model performance cannot be maintained continuously due to drift and update feedback is then provided to re- train/update the model or select alternative model. When AI/ML model enabled wireless communication network is deployed, it is then important to consider how to handle AI/ML model in activation with re-configuration for wireless devices under operations such as model training, inference, updating, etc. In this method, ML configuration information about online training activation is provided to UE based on UE RRC state switching where online training modes (e.g., such as the preconfigured index information) about combinations of model collaboration types (e.g., two-sided model and one-sided model) and RRC states (e.g., connected/active state, inactive state, idle state) are configured for UE and can be sent through system information or dedicated RRC signaling. In other words, state-dependent AI/ML behavior is defined as part of AI/ML model operation configuration. As defined in 3GPP already, two-sided model is using separate models at both sides of entities for collaborative ML operation and one-sided model is using a model at one of entities of both sides. Online training modes can be formed as multiple versions of look-up tables or list of index-based information in advance by considering a set of information about ML applications, LCM, ML applicable conditions, ML functionality and model characteristics along with UE ML capability. Based on the configured information about ML applications, LCM, ML applicable conditions, ML functionality and model characteristics along with UE ML capability, the relevant online training mode can be applied to UE by adapting to different combinations of RRC states and one-/two-sided model types. If online training mode configuration is further updated, it can be also sent to UE through system information/RRC or L1/L2 signaling according to UE RRC state status. For decision on online training mode activation, there are two methods such as network-assisted method and UE autonomous method where specific online training mode is determined by NW side and provided to UE for network-assisted method, and for UE autonomous method specific online training mode is determined by UE autonomously by sending the related update report to gNB after the selected mode execution. Additionally, hybrid method can be applied where any specific online training mode is indicated to UE by network side and UE can accept it if matched with UE's mode selection or reject it by applying alternative mode if not matched with UE's mode selection. Similarly, this method can be applied to other LCM phases such as dataset collection, model inferencing, etc. if necessary when the pre-configured mode selection information is generated in advance for each LCM phases. Therefore, depending on different LCM phases, online training modes can be extended to other ML model activities (e.g., inferencing, updating, dataset collection) by using the same combinations of model collaboration types and RRC states (e.g., look-up table, index-based indication). The pre-configured index table about combinations of model collaboration types and RRC states can be provided to UE through system information or dedicated RRC signaling. Based on the specific index indication message, UE performs online training operation according to RRC state conditions with the associated assistance information about ML configuration. Size of this index table can be configured for different number of online training modes based on various implementation-specific applications or deployments. Also if there are a number of UEs to apply online training modes, UE group can be assigned to activate any specific online training mode as well. In this case, configuration information about online training modes can be sent through multicast signaling. By using this method, online training operation can be executed more efficiently for different UE RRC states and the associated signaling overhead can be further reduced.

Figure 1 shows an exemplary table of online training modes. In this table, the preconfigured index table about combinations of model collaboration types and RRC states is provided to UE through system information or dedicated RRC signaling. For example, when online training mode index is “2” or “3” for two-sided model, UE RRC state can be switched between connected state and inactive/idle state in periodic or aperiodic way so that two-sided model collaboration can be operated. The related ML configuration (e.g., ML assistance information) is required to provide to UE such as frequency of online learning operation with RRC state switching for re-connection. In addition, the index table can be based on RRC states only or model collaboration types only as well as the combinations of both according to different implementation scenarios.

Figure 2 shows an exemplary flow chart of configuring online training modes at network side. In this flow chart, a set of online training modes is configured at network side so that it can be shared with UE side in advance before online training can be applied for activation.

Figure 3 shows an exemplary flow chart of activating online training mode at UE side. In this example, any specific online training mode is enabled when UE is triggered for activation. Specifically, UE can apply the indicated online training mode sent by gNB directly or UE can apply the selected online training mode autonomously based on the pre-configured criteria given by network.

Figure 4 shows an exemplary signaling flow of activating online training mode with RRC state switching. In this example, a specific online training mode is provided by network side and UE applies it with RRC state switching. Indication of online training mode can be sent through L1/L2/L3 signaling (e.g., together with RRC message or separately with L1/L2 signaling).

Figure 5 shows an exemplary signaling flow of updating ML information based on online training with RRC state switching. In this example, online training update at UE is sent to gNB when RRC state is switched from inactive/idle state to connected state. This operation is pre-configured based on online training modes provided to UE in advance. Figure 6 shows an exemplary signaling flow of iterative online training activation with RRC state switching. In this example, UE RRC state can be switched between connected state and inactive/idle state in periodic or aperiodic way with the iterative online training activation so that ML update information can be shared and ML reconfiguration can be provided between network side and UE side.

Claims

1. A method of RRC state-based online training signaling, comprising:

• Configuring online training modes, wherein the modes can be the preconfigured index information about combinations of model collaboration types, which can be two-sided model and one-sided model, and RRC states, wherein the RRC-States are in connected and/or active state and/or inactive state and/or idle state;

• Transmitting the configured online training modes to UE based on UE RRC state switching via system information or dedicated RRC signaling;

• Performing online training operation according to RRC state conditions with the associated assistance information about ML configuration based on the specific index indication message.

2. The method according to previous claim 1 , wherein online training modes is formed as multiple versions of look-up tables or list of index-based information in advance by considering a set of information about ML applications, LCM, ML applicable conditions, ML functionality and model characteristics along with UE ML capability.

3. The method according to one of the previous claims, wherein the online training mode is applied to UE by adapting to different combinations of RRC states and one-/two-sided model types as state-dependent AI/ML behavior.

4. The method according to one of the previous claims, wherein Any further updated online training mode configuration can be sent to UE via system information and/or RRC or L1/L2 signaling according to UE RRC state status.

5. The method according to one of the previous claims, wherein there are multiple methods of decision on online training mode activation, comprising:

• Network-assisted method: specific online training mode is determined by network side; • UE autonomous method : specific online training mode is determined by UE autonomously by sending the related update report to gNB after the selected mode execution;

• Hybrid method : specific online training mode is indicated to UE by network side and UE can accept it if matched with UE's mode selection or reject it by applying alternative mode if not matched with UE's mode selection;

• whereby the method is applied to other LCM phases such as dataset collection, model inferencing, if necessary when the pre-configured mode selection information is generated in advance for each LCM phases.

6. The method according to one of the previous claims, wherein depending on different LCM phases, online training modes can be extended to other ML model activities, whereby ML model activities can be inferencing, updating, dataset collection by using the same combinations of model collaboration types and RRC states, whereby these can be look-up table and/or index-based indication.

7. The method according to one of the previous claims, wherein the pre-configured index table about combinations of model collaboration types and RRC states can be sent to UE via system information or dedicated RRC signaling.

8. The method according to one of the previous claims, wherein size of the preconfigured index table can be configured for different number of online training modes based on various implementation-specific applications or deployments.

9. The method according to one of the previous claims, wherein UE group can be assigned to activate any specific online training mode by sending configuration information about online training modes via multicast signaling if there are a number of UEs to apply online training modes.

10. The method according to one of the previous claims, wherein UE RRC state can be switched between connected state and inactive/idle state in periodic or aperiodic way so that two-sided model collaboration can be operated.

11. The method according to previous claim 10, wherein the related ML configuration, which is a ML assistance information, is required to provide to UE such as frequency of online learning operation with RRC state switching for re-connection.

12. The method according to one of the previous claims, wherein UE can apply the indicated online training mode sent by gNB directly or UE can apply the selected online training mode autonomously based on the pre-configured criteria given by network.

13. The method according to one of the previous claims, wherein indication of online training mode can be sent via L1/L2/L3 signaling together with RRC message or separately with L1/L2 signaling.

14. The method according to one of the previous claims, wherein online training update at UE is sent to gNB when RRC state is switched from inactive/idle state to connected state.

15. The method according to one of the previous claims, wherein UE RRC state can be switched between connected state and inactive/idle state in periodic or aperiodic way with the iterative online training activation so that ML update information can be shared and ML reconfiguration can be provided between network side and UE side.

16. Apparatus for RRC state-based online training signaling, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 1 to 15.

17. User Equipment comprising an apparatus according to claim 16.

18. gNB comprising an apparatus according to claim 16.

19. Wireless communication system for RRC state-based online training signaling, wherein the wireless communication systems comprises user equipment according to claim 17, gNB according to claim 18, whereby the user Equipment and the gNB each comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 1 to 15.