WO2025168462A1 - Method of advanced online training signaling for ran - Google Patents

Abstract

The present application 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 ML conditions. Therefore, model operation (e.g., model re-training/updating) can be set up between network and UE by identifying the most applicable selection of parameter combinations.

Description

TITLE

Method of advanced online training signaling for RAN

TECHNNICAL FIELD

The present disclosure relates to AI/ML based model online training using mapping relationship information of a set of parameters, 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 network-side 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 retrained with original full features/dataset configuration, high signaling overhead and/or high compute power can be very challenging.

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.

The present disclosure solves the cited problem by the proposed embodiments and describes a method advanced online training signaling for RAN in a wireless communication system, comprising, transmitting the pre-configured mapping relation information e.g., via system information or dedicated RRC message), selecting specific mapping relation to be sent as indication message, performing online training with the specific parameter set ID e.g., based on the indicated model input data category.

In some embodiments of the method according to the first aspect, the method is characterized by, that the index-based mapping relation table can be configured to indicate different combinations of finite set of parameter IDs for online training.

In some embodiments of the method according to the first aspect, the method is characterized by, that mapping tables can be multiple versions for use with varying index sizes depending on ML condition change like UE ML capability and/or UE mobility and/or ML configuration and/or site and model performance change.

In some embodiments of the method according to the first aspect, the method is characterized by, that the online training index or ID can be selected based on mapping relation with the configured combination of parameter ID information.

In some embodiments of the method according to the first aspect, the method is characterized by, that the triggering criteria can be configured by network side in advance and it can be based on measuring frequency of model drift occurrences and/or data distribution changes as the configured metric. In some embodiments of the method according to the first aspect, the method is characterized by, that either network side or UE side can activate it where L1/L2/L3 signaling can be used for activation indication.

In some embodiments of the method according to the first aspect, the method is characterized by, that when there are multiple UEs as a group to apply for online training, the same online training index or ID can be sent to them via multicast signaling.

In some embodiments of the method according to the first aspect, the method is characterized by, that index information with two or more model parameter categories for use in mapping relation can be used as indication message.

In some embodiments of the method according to the first aspect, the method is characterized by, that specific online training configuration can be activated based on the associated model parameter IDs.

In some embodiments of the method according to the first aspect, the method is characterized by, that one or more combinations of model parameter IDs can be configured to form index-based mapping relation tables to represent any selected combination as index for indication.

In some embodiments of the method according to the first aspect, the method is characterized by, that multiple model parameter categories are used as a form of IDs so that different combinations of subset of IDs can be used to configure index-based mapping relation table.

In some embodiments of the method according to the first aspect, the method is characterized by, that online training configuration can be determined based on the pre-configured combinations of those IDs.

According to a second aspect, the present disclosure relates to an apparatus for 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 execute according to any one of the embodiments of the first aspect

User Equipment comprising an apparatus according according to any one of the embodiments of the second aspect. 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 advanced online training signaling for RAN, wherein the wireless communication systems comprises user equipment according according to the third aspect, gNB according 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 execute 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 index or ID.

Figure 2 is an exemplary composition of ID set for online training.

Figure 3 is an exemplary block diagram of mapping relationship of parameter IDs and online training index.

Figure 4 is an exemplary flow chart of configuring mapping relationship at network side.

Figure 5 is an exemplary flow chart of performing online training with the indication at UE side. Figure 6 is an exemplary signaling flow of determining online training activation at network side.

Figure 7 is an exemplary signaling flow of determining online training activation at UE side.

Figure 8 is an exemplary flow chart of performing dataset transfer at network side.

Figure 9 is an exemplary flow chart of performing online training with local dataset or dataset transfer at UE side.

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 using mapping relationship information of a set of parameters. 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, the mapping relation information about a set of parameter IDs for online training is configured and the pre-configured mapping relation information can be sent through system information or dedicated RRC message. The selected mapping relation can be also sent as indication message by gNB and online training can then be performed with the specific parameter set ID (e.g., based on the indicated model input data category). For example, index-based mapping relation table can be configured to indicate different combinations of finite set of parameter IDs for online training. Depending on ML condition change (e.g., UE ML capability, UE mobility, ML configuration, site, etc.) and model performance change, mapping tables can be multiple versions for use with varying index sizes. The online training index or ID can be selected based on mapping relation with the configured combination of parameter ID information. For triggering method of online training, the triggering criteria can be configured by network side in advance and it can be based on measuring frequency of model drift occurrences and/or data distribution changes as the configured metric. For example, if the measured metric is above the threshold, then online training can be triggered for activation. For determining online training activation, either network side or UE side can activate it where L1/L2/L3 signaling can be used for activation indication. When there are multiple UEs as a group to apply for online training, the same online training index or ID can be sent to them via multicast signaling.

Figure 1 shows an exemplary table of online training index or ID. In this example, the index-based mapping relation table can be configured to indicate different combinations of finite set of parameter IDs for online training. If there are two or more model parameter categories for use in mapping relation, index information can be used as indication message where specific online training configuration can be activated based on the associated model parameter IDs. Therefore, this exemplary table can vary by having one or more combinations of model parameter IDs by expanding columns. Any selected combination can then be represented as index for indication. Model parameter categories include data category ID, data size ID, model ID, ML conditions such as network deployment conditions and UE ML capability, etc.

Figure 2 shows an exemplary composition of ID set for online training. In this example, multiple model parameter categories are used as a form of IDs so that different combinations of subset of IDs can be used to configure index-based mapping relation table. For example, data category ID and data size ID can be combined to represent a list of index-based mapping relation so that any specific index can be selected to activate online training.

Figure 3 shows an exemplary block diagram of mapping relationship of parameter IDs and online training index. In this example, the mapping relation information about a set of parameter IDs for online training is configured where there are multiple model parameter categories represented by IDs and online training configuration can be determined based on the pre-configured combinations of those IDs. Figure 4 shows an exemplary flow chart of configuring mapping relationship at network side. In this example, mapping relation for online training is configured by network so that it can be transmitted to UE in advance before applying for activation.

Figure 5 shows an exemplary flow chart of performing online training with the indication at UE side. In this example, UE can perform online training for activation according to reception of online training index or related ID information.

Figure 6 shows an exemplary signaling flow of determining online training activation at network side. In this example, model monitoring is executed in advance before online training operation so that network side determines online training activation for UE. When online training activation is requested to UE, the pre-configured index or ID information can be sent to reduce signaling overhead.

Figure 7 shows an exemplary signaling flow of determining online training activation at UE side. In this example, online training activation is autonomously enabled at UE side without receiving any related indication message as the pre-configured online training index or ID information is updated in advance.

Figure 8 shows an exemplary flow chart of performing dataset transfer at network side. In this example, dataset transfer can be performed for online training activation when the transferred dataset for online training is required.

Figure 9 shows an exemplary flow chart of performing online training with local dataset or dataset transfer at UE side. In this example, there are two options that local dataset only can be used for online training or dataset transfer can be also used for online training if local dataset is not available. By using this method, online training can be more efficiently performed with signaling overhead reduction.

Claims

1. A method advanced online training signaling for RAN in a wireless communication system, comprising:

• Transmitting the pre-configured mapping relation information to UE via system information or dedicated RRC message;

• Selecting specific mapping relation to be based on network conditions, UE capabilities, and Al model performance monitoring;

• Transmitting the selected mapping relationship as an indication message to the UE;

• Performing online training at the UE based on the indicated parameter set ID and the corresponding AI/ML model input data category.

2. The method according to previous one of the previous claims, wherein indexbased mapping relation table is configured at network entity to indicate different combinations of finite set of parameter IDs for online training.

3. The method according to previous one of the previous claims, wherein mapping tables can be multiple versions for use with varying index sizes depending on ML condition change like UE ML capability and/or UE mobility and/or ML configuration and/or site and model performance change.

4. The method according to previous one of the previous claims, wherein the online training index or ID can be selected based on mapping relation with the configured combination of parameter ID information.

5. The method according to previous one of the previous claims, wherein the triggering criteria can be configured by network side in advance and it can be based on measuring frequency of model drift occurrences and/or data distribution changes as the configured metric.

6. The method according to previous one of the previous claims, wherein either network side or UE side can initiate online training activation where L1/L2/L3 signaling can be used for activation indication.

7. The method according to previous one of the previous claims, wherein when there are multiple UEs as a group to apply for online training, the same online training index or ID can be sent to them via multicast signaling.

8. The method according to previous one of the previous claims, wherein index information with two or more model parameter categories, whereby model parameter categories can be data category ID, data size ID, model ID, network deployment conditions, UE ML capability, for use in mapping relation can be used as indication message.

9. The method according to previous one of the previous claims, wherein specific online training configuration can be activated based on the associated model parameter IDs.

10. The method according to previous one of the previous claims, wherein one or more combinations of model parameter IDs can be configured to form indexbased mapping relation tables to represent any selected combination as index for indication.

11 . The method according to previous one of the previous claims, wherein multiple model parameter categories are used as a form of IDs so that different combinations of subset of IDs can be used to configure index-based mapping relation table.

12. The method according to previous one of the previous claims, wherein online training configuration can be determined based on the pre-configured combinations of those IDs.

13. Apparatus advanced online training signaling for RAN, 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 12.

14. User Equipment comprising an apparatus according to claim 13.

15. gNB comprising an apparatus according to claim 13.

16. Wireless communication system for advanced online training signaling for RAN, wherein the wireless communication systems comprises user equipment according to claim 14, gNB according to claim 15, 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 12.