Method of grouping-based data sharing

Abstract

The present disclosure describes a novel method of using the pre-configured AI / ML (artificial intelligence / machine learning) with grouping-based ML element data sharing in wireless mobile communication system including base station (e.g., gNB, TRP, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling overhead can be significantly increased for frequent signaling exchanges with varying ML element information (e.g., model, dataset, ML configuration parameter, etc.). Therefore, model operation (e.g., model training, inferencing, monitoring, updating, etc.) can be set up between network and UE by configuring groups of ML element data sharing for ML operation.

Description

[0001] 202407099

[0002] - 1 -

[0003] TITLE

[0004] Method of grouping-based data sharing

[0005] TECHNICAL FIELD

[0006] The present disclosure relates to the field of AI / ML based model operation with grouping-based ML element data sharing, where techniques for configuring groups of ML element data sharing applicable to radio access network are presented.

[0007] BACKGROUND

[0008] 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.

[0009] Also in 3GPP, two-sided (AI / ML) model is defined as 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. Also for onesided (AI / ML) model, UE-side (AI / ML) model is defined as an AI / ML model whose inference is performed entirely at the UE and network-side (AI / ML) model is defined as an AI / ML model whose inference is performed entirely at the network. 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 202407099

[0010] - 2 -

[0011] 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 (Al) / Machine 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.

[0012] 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 ML condition changes, 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.

[0013] 3GPP has defined frameworks for AI / ML-based functionalities such as beam management, mobility prediction, and coverage optimization, while emphasizing interoperability, lifecycle management, and testability. However, current specifications lack a standardized mechanism for organizing and sharing granular ML resources such as datasets, inference outputs, model parameters, architectures, and hyperparameters between UE and network nodes. This absence limits collaborative learning and efficient model adaptation across heterogeneous devices. The invention addresses this gap by introducing an ML element ID-based grouping approach that enables structured resource sharing, dynamic configuration, and lifecycle-aware coordination, thereby supporting Al-native RAN principles in future 6G systems. 202407099

[0014] - 3 -

[0015] US2024086766A describes a method of receiving a request for retrieving or executing a machine learning model or a combination of ML models with a specified output feature and specified input data type and distribution of input values for a ML model or combination of ML models.

[0016] US20240154709A1 describes the configuration message indicating a model identifications and measurement for generating a data set corresponding to the model ID.

[0017] US20240267755A1 describes the RRC (radio resource control) model including a model identifier, a model structure, or parameters that indicate the AI / ML model to be implemented by the device.

[0018] WO2023209577A1 describes a method of indicating and configuring machine learning model support, including a ML type and / or version information of at least one model associated with a certain functionality.

[0019] WO2024128636A1 describes a method for AI / ML model / functionality life cycle management including metadata related to the AI / ML model / functionality and assigning an identifier to the AI / ML model / functionality.

[0020] WO2024165946A1 describes a method of AI / ML model identifiers usage with signaling procedures required to be defined between the terminal device and the network to determine the validity of a given AI / ML model.

[0021] WO20241 68831 A1 describes a method of general framework for model / functionality identification with RAN awareness of AI / ML model through network function / server, RAN awareness of AI / ML model through UE report, and the model ID usage in different aspects of LCM.

[0022] BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is an exemplary table of mapping relation of group IDs with a set of ML element IDs. 202407099

[0024] - 4 -

[0025] Figure 2 is an exemplary table of sharing information differentiated in each group.

[0026] Figure 3 is an exemplary flow chart of processing ML element ID based grouping operation at network side.

[0027] Figure 4 is an exemplary flow chart of processing ML element ID based grouping operation at UE side.

[0028] DETAILED DESCRIPTION

[0029] 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.

[0030] 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.

[0031] 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 202407099

[0032] - 5 - 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.

[0033] 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.

[0034] 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. 202407099

[0035] - 6 -

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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 202407099

[0041] - 7 - 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.

[0042] 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.

[0043] 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”)).

[0044] 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 202407099

[0045] - 8 - 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.

[0046] 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 functions / acts specified in the flowchart diagrams and / or block diagrams.

[0047] 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 202407099

[0048] - 9 - manufacture including instructions which implement the function / act specified in the flowchart diagrams and / or block diagrams.

[0049] 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.

[0050] 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).

[0051] 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.

[0052] 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 202407099

[0053] - 10 - systems that perform the specified functions or acts, or combinations of special purpose hardware and code.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] The UEs are located in a coverage of the BS. The coverage of the BS corresponds 202407099

[0059] - 11 - for example to the area in which UEs can decode a PDCCH transmitted by the BS.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] AI / ML Model is a data driven algorithm that applies AI / ML techniques to generate set of outputs based on set of inputs. 202407099

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[0065] 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.

[0066] AI / ML model Inference is a process of using trained AI / ML model to produce set of outputs based on set of inputs.

[0067] 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.

[0068] 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.

[0069] AI / ML model transfer is a delivery of an AI / ML model over the air interface in manner that is not transparent to 3GPP signaling, either parameters of model structure known at the receiving end or new model with parameters. Delivery may contain full model or partial model.

[0070] 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.

[0071] 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.

[0072] 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. 202407099

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[0074] 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.

[0075] Model activation means enable an AI / ML model for specific AI / ML-enabled feature.

[0076] Model deactivation means disable an AI / ML model for specific AI / ML-enabled feature.

[0077] Model download means Model transfer from the network to UE.

[0078] 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.

[0079] Model monitoring is A procedure that monitors the inference performance of the AI / ML model.

[0080] 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.

[0081] Model switching is deactivating currently active AI / ML model and activating different AI / ML model for specific AI / ML-enabled feature.

[0082] Model update is Process of updating the model parameters and / or model structure of model.

[0083] Model upload is Model transfer from UE to the network. 202407099

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[0085] Network-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the network.

[0086] Offline field data is the data collected from field and used for offline training of the AI / ML model.

[0087] 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.

[0088] Online field data is the data collected from field and used for online training of the AI / ML model.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] Semi-supervised learning is a process of training model with mix of labelled data and unlabeled data. 202407099

[0093] - 15 -

[0094] Supervised learning is a process of training model from input and its corresponding labels.

[0095] 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.

[0096] UE-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the UE.

[0097] Unsupervised learning is a process of training model without labelled data.

[0098] 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.

[0099] 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.

[0100] The following explanation will provide the detailed description of the mechanism about pre-configuring and signaling the specific information about model online training by configuring a set of UE behaviors. 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. 202407099

[0101] - 16 -

[0102] 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. For determining information about model identification (e.g., model ID), frequent model ID assignment / re-assignment processes might occur due to model drift related to model performance variation and / or model applicable condition change. In addition, if all identified models at UE side are to be assessed and monitored, the associated signaling overhead and computing power demand can increase significantly. And UEs often have a list of active / inactive ML models, each assigned a unique identifier. And the network and UEs may have to take frequent signaling exchanges to determine the related configuration. This can lead to increased signaling overhead and reduced radio resource efficiency. For example, more than one ML models are in active between gNB and UE where multiple UEs have their own on-device models in operation. In addition, each model might be in different LCM phases such as data collection, training, inferencing or monitoring, resulting in high signaling overhead for model-related resource transfers (e.g., datasets, model parameters, architectures, hyperparameters). It is necessary to investigate an efficient mechanism to help reduce the overall signaling overhead along with resource efficiency improvement. In this method, the differentiated ML element ID-based grouping is configured so that 202407099

[0103] - 17 - each groups have different ML elements to be shared with UEs for ML operation. Specifically, Group A is dataset sharing group, Group B is output inference sharing group, Group C is model parameter sharing group, Group D is model architecture sharing group, and Group E is hyperparameter sharing group. UEs within these groups leverage the shared resources (datasets, output inferences, model parameters, architectures, hyperparameters) to enhance model performance, enable collaborative learning, and improve overall system efficiency. However, the list of groups is allowed to be adjusted for the specific size (e.g., number of groups) and the associated ML elements with each groups based on ML applications or deployment scenarios for implementation. ML element ID-based groups are allowed to be adjusted for different purposes by maintaining multiple combinations of groups for different UE groups or any single UE.

[0104] The network can assign specific ML element IDs to each group, ensuring that the UEs can identify the purpose of the group based on the ML element ID information. The network can then configure the UEs with the information about these differentiated ML element ID-based groups, including the group IDs and the associated ML element IDs. The network provides the mapping information and differentiated group details to the UEs through system information or dedicated RRC signaling. The network can dynamically update the mapping and group information as new models are introduced or existing models are switched, sending updates via L1 / L2 or RRC signaling. UEs report their available models, datasets, and / or model- related resources to the network (e.g., using the associated identifier format via L1 / L2 or RRC signaling), allowing the network to maintain an up-to-date status and facilitate the sharing process. The mapping relation table associates a unique group ID with a set of ML element IDs. Each group ID represents a logical grouping of related models and / or other ML elements such as local dataset, model output, model architecture, etc.

[0105] This mapping allows for efficient organization and management of the models based on their grouping, which can be useful for various applications and operations. The network creates differentiated ML element ID-based groups for specific purposes, such as dataset sharing, output inference sharing, model parameter sharing, model 202407099

[0106] - 18 - architecture sharing, and hyperparameter sharing. The network coordinates the sharing of model-related resources among the UEs based on the reported resources and the established mapping and grouping. The UE receives the mapping relation about the differentiated ML element ID-based group information from the network. The UE reports its available models, datasets, model parameters, architectures, and hyperparameters to the network.

[0107] The UE monitors for updates to the mapping and group information from the network, received through L1 / L2 signaling. The UE selects the most suitable combination of ML model operation mode and data collection mode based on the received mapping information. The UE leverages the differentiated ML element ID-based group information to identify the appropriate sharing groups for its available resources and engages in the targeted sharing as coordinated by the network. The UE utilizes the shared resources from other UEs within the same groups to enhance its local model performance, enable collaborative learning, and improve overall system efficiency.

[0108] To support standardized AI / ML operations in NG-RAN systems, the invention introduces a mechanism for ML element ID-based grouping, wherein each group is identified by a unique Group ID and associated with a set of ML element IDs representing shareable ML components such as datasets, inference outputs, model parameters, architectures, and hyperparameters. This grouping enables the network to organize and manage ML resources efficiently across UEs, facilitating collaborative learning and adaptive model deployment. The network configures these groups via broadcast and / or dedicated RRC signaling, and may dynamically update group membership and element associations using L1 / L2 signaling or MAC CE, ensuring low-latency responsiveness to model lifecycle phases. UEs report their ML capabilities and resource availability using standardized identifier formats, allowing the network to maintain an up-to-date view of distributed ML status. The grouping mechanism supports both functionality-based lifecycle management where the group is tied to an ML-enabled function such as beam prediction or mobility optimization and model-ID based lifecycle management, where specific model instances are tracked and coordinated. Furthermore, the invention enables inter-node synchronization of ML group configurations across gNB-DU / CU via F1 , and across 202407099

[0109] - 19 - gNBs via Xn, aligning with 3GPP for AI / ML-driven coverage and capacity optimization. The ML element grouping also supports privacy and testability requirements by allowing per-element sharing controls. This invention is foundational for Al-native 6G RAN architectures, as it provides a scalable and interoperable framework for distributed learning and dynamic model orchestration across heterogeneous network entities and UEs.

[0110] Figure 1 shows an exemplary table of mapping relation of group IDs with a set of ML element IDs. In this example, the mapping relation table associates a unique group ID with a set of ML element IDs. Each group ID represents a logical grouping of related models and / or other ML elements such as local dataset, model output, model architecture, etc. This mapping allows for efficient organization and management of the models based on their grouping, which can be useful for various applications and operations.

[0111] Figure 2 shows an exemplary table of sharing information differentiated in each group. In this example, this table shows the description about availability of sharing information differentiated in each group. Especially, the key difference between Group C and Group D in the proposed ML element ID-based grouping is the focus of the shared resources. In Group C, UEs within this group can share their local model parameters, such as weights and biases, with other UEs in the same group. This enables collaborative model training and federated learning, where the UEs can aggregate their model parameters to create a more robust and accurate shared model. In Group D, UEs within this group can share their local model architectures, including the network topology, layer configurations, and other structural details. This allows the UEs to collaborate on model design and exploration, enabling them to develop more efficient and effective model architectures.

[0112] Figure 3 shows an exemplary flow chart of processing ML element ID based grouping operation at network side. In this example, the network establishes a mapping relation between different ML model operation modes (e.g., UE-sided, network-sided, two-sided) and corresponding data collection modes (e.g., UE-sided, network-sided). This mapping information is broadcasted to the UEs through RRC 202407099

[0113] - 20 - signaling or system information. The network creates differentiated ML element ID- based groups for specific purposes, such as dataset sharing, output inference sharing, model parameter sharing, model architecture sharing, and hyperparameter sharing. The group information is also sent to the UEs via system information or dedicated RRC signaling. The UEs report their available models, datasets, model parameters, architectures, and hyperparameters to the network. As new models are introduced or existing models are retired, the network updates the mapping and group information, and sends these updates to the UEs through L1 / L2 or RRC signaling. The network coordinates the sharing of model-related resources among the UEs based on the reported resources and the established mapping and grouping.

[0114] Figure 4 shows an exemplary flow chart of processing ML element ID-based grouping operation at UE side. In this example, the UE receives the mapping relation between ML model operation modes and data collection modes, as well as the differentiated ML element ID-based group information from the network. The UE reports its available models, datasets, model parameters, architectures, and hyperparameters to the network. The UE monitors for updates to the mapping and group information from the network, received through L1 / L2 signaling. The UE selects the most suitable combination of ML model operation mode and data collection mode based on the received mapping information. The UE leverages the differentiated ML element ID-based group information to identify the appropriate sharing groups for its available resources and engages in the targeted sharing as coordinated by the network. The UE utilizes the shared resources from other UEs within the same groups to enhance its local model performance, enable collaborative learning, and improve overall system efficiency.

Claims

202407099- 21 -CLAIMS1. Method of grouping-based data sharing by configuring the differentiated ML element ID-based grouping in a wireless communication system, comprising:• Defining a list of groups with the associated sharing information;• Generating the mapping relation information to associate a unique group ID with a set of ML element IDs;• Providing group information to UEs for ML operation.

2. The method according to previous claim 1 , wherein each groups have different ML elements to be shared with UEs for ML operation.

3. The method according to one of the previous claims, wherein a list of groups are configured with the associated ML elements, comprising:• Group A is dataset sharing group;• Group B is output inference sharing group;• Group C is model parameter sharing group;• Group D is model architecture sharing group;• Group E is hyperparameter sharing group.

4. The method according to one of the previous claims, wherein the shared resources (datasets, output inferences, model parameters, architectures, hyperparameters) are utilized among UEs or UE groups to enhance model performance, enable collaborative learning, and improve overall system efficiency.

5. The method according to one of the previous claims, wherein ML element ID- based groups are adjusted for different purposes by maintaining multiple combinations of groups for different UE groups or any single UE.202407099- 22 -6. The method according to one of the previous claims, wherein the network assigns specific ML element IDs to each group, ensuring that the UEs identify the purpose of the group based on the ML element ID information.

7. The method according to one of the previous claims, wherein the network configures the UEs with the information about these differentiated ML element ID- based groups, including the group IDs and the associated ML element IDs.

8. The method according to one of the previous claims, wherein the network provides the mapping relation information and differentiated group details to the UEs through system information or dedicated RRC signaling.

9. The method according to one of the previous claims, wherein the network dynamically updates the mapping relation and group information as new models are introduced or existing models are switched, sending updates via L1 / L2 or RRC signaling.

10. The method according to one of the previous claims, wherein UEs report their available models, datasets, and / or model-related resources to the network (e.g., using the associated identifier format via L1 / L2 or RRC signaling), allowing the network to maintain an up-to-date status and facilitate the sharing process.11 .The method according to one of the previous claims, wherein each group ID represents a logical grouping of related models and / or other ML elements such as local dataset, model output, model architecture, etc.

12. The method according to one of the previous claims, wherein the network creates differentiated ML element ID-based groups for specific purposes, such as dataset sharing, output inference sharing, model parameter sharing, model architecture sharing, and hyperparameter sharing.202407099- 23 -13. The method according to one of the previous claims, wherein the network coordinates the sharing of ML model-related resources among the UEs based on the reported resources and the established mapping and grouping.

14. The method according to one of the previous claims, wherein the UE receives the mapping relation about the differentiated ML element ID-based group information from the network.

15. The method according to one of the previous claims, wherein the UE reports its available models, datasets, model parameters, architectures, and hyperparameters to the network.

16. The method according to one of the previous claims, wherein the UE monitors for updates to the mapping and group information from the network, received through L1 / L2 signaling.

17. The method according to one of the previous claims, wherein the UE leverages the differentiated ML element ID-based group information to identify the appropriate sharing groups for its available resources and engages in the targeted sharing as coordinated by the network.

18. The method according to one of the previous claims, wherein the UE utilizes the shared resources from other UEs within the same groups to enhance its local model performance, enable collaborative learning, and improve overall system efficiency.

19. The method according to one of the previous claims, wherein the list of ML element ID-based groups is adjusted for the specific size (e.g., number of groups) and the associated ML elements with each groups based on ML applications or deployment scenarios for implementation20. Apparatus for grouping-based data sharing by configuring the differentiated ML element ID-based grouping in a wireless communication system, comprising, the202407099- 24 - 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 18. 21 . User Equipment comprising an apparatus according to claim 20.

22. gNB comprising an apparatus according to claim 20.

23. Wireless communication system grouping-based data sharing by configuring the differentiated ML element ID-based grouping, wherein the wireless communication systems comprises user equipment according to claim 21 , gNB according to claim 22, 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 19.

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