Method of model adjustment signaling using representative model

Abstract

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model representation in wireless mobile communication system including base station (e.g., gNB, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, ML conditions can vary dynamically and model performance can then be degraded. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring representative model and the associated variation models.

Description

[0001] TITLE

[0002] Method of model adjustment signaling using representative model

[0003] TECHNNICAL FIELD

[0004] The present disclosure relates to AI / ML based model representation, where techniques for pre-configuring and signaling the specific information about representative model and variation model applicable to radio access network are presented.

[0005] BACKGROUND

[0006] 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. 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 one-sided (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 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.

[0007] 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. For example, when the trained ML model is deployed in RAN, model performance for inferencing can be easily degraded if target ML condition is not well aligned with real ML condition measured for specific model operation. ML condition refers to the (non)real-time operational state of a ML model within a wireless communication system and it includes factors such as network load, dataset availability, device ML capability status, environmental conditions (e.g., site, signal interference), and / or device power constraints that may influence the selection and activation of a variation model. Also ML capability refers to the processing capacity and efficiency of a device (e.g., UE or network-side entity) in handling ML operations and it includes factors such as available computing resources (CPU, GPU, or NPU), memory capacity, model execution speed, supported ML frameworks, and the ability to process different complexity levels of variation models. US 2023069342 describes how to assist determination of the model update time in consideration of cost for the update of a model.

[0008] US 2023022737 explains supporting generation of machine learning model when a certain machine learning model is changed. US 2019012876 provides projections, predictions, and recommendations for computing system.

[0009] US 2019332895 shows that the monitored states are to decide to change a trained ML model as currently used.

[0010] EP 4075348 describes control of machine learning model, which can be based on a federated learning method collectively performed by nodes of a decentralized distributed database.

[0011] US 2021019612 provides the self-healing system that can automatically provide a diagnostic, and it can also automatically provide an action if the performance of the model predictions has changed over time.

[0012] The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model representation in wireless mobile communication system including base station (e.g., gNB, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, ML conditions can vary dynamically and model performance can then be degraded. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring representative model and the associated variation models.

[0013] The present disclosure solves the cited problem by the proposed embodiments and describes a method of model adjustment signaling using representative model by configuring a set of representative models within a wireless communication system, comprising: associating one or multiple variation models for a representative model; pre-determining mapping relation between representative model and the related variation model(s); setting index or ID values for representative models and variation models; providing mapping relation to UEs. In some embodiments of the method according to the first aspect, the method is characterized by, that one or more variation models for activation can be selected among candidate variation models associated with representative model.

[0014] In some embodiments of the method according to the first aspect, the method is characterized by, that both representative models and variation models in mapping relation information can be indicated using index or ID value.

[0015] In some embodiments of the method according to the first aspect, the method is characterized by, that a set of specific variation models can be determined based on the configured measurement technique such as similarity metric or K-means clustering.

[0016] In some embodiments of the method according to the first aspect, the method is characterized by, that any available variation model(s) associated with the specific representative model can be chosen for activation depending on device ML condition or ML capability.

[0017] In some embodiments of the method according to the first aspect, the method is characterized by, that mapping relation information can be sent via system information or dedicated RRC signaling.

[0018] In some embodiments of the method according to the first aspect, the method is characterized by, that any additional changes or updates about mapping relation information can be also sent via L1 / L2 or RRC signaling.

[0019] In some embodiments of the method according to the first aspect, the method is characterized by, that decision of specific variation model(s) can be made by either network side or UE side depending on implementation scenarios.

[0020] In some embodiments of the method according to the first aspect, the method is characterized by, that representative model can be defined as one of available variation models, ensemble model of the aggregated variation models, or any derived model achieving the average performance across the associated variation models.

[0021] In some embodiments of the method according to the first aspect, the method is characterized by, that multiple variation models can be chosen and activated if applicable depending on different use cases.

[0022] In some embodiments of the method according to the first aspect, the method is characterized by, that there can be different mapping relation tables for various model applications and / or ML conditions.

[0023] In some embodiments of the method according to the first aspect, the method is characterized by, that one or more mapping relation tables can be configured and applied to ML operation between network side and UE side.

[0024] In some embodiments of the method according to the first aspect, the method is characterized by, that different variation models with a given representative model can have different complexity levels with inferencing performance difference.

[0025] In some embodiments of the method according to the first aspect, the method is characterized by, that UE can select either representative model itself or any variation model(s) available at UE when representative model is transferred to UE.

[0026] In some embodiments of the method according to the first aspect, the method is characterized by, that any activation and / or decision of selecting variation models can be performed by network side depending on implementation scenarios.

[0027] According to a second aspect, the present disclosure relates to an apparatus for adjustment signaling using representative model by configuring a set of representative models within a wireless communication system the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps according to any one of the embodiments of the first aspect. According to a third aspect, the present disclosure relates to user Equipment comprising an apparatus according to the second aspect.

[0028] According to a fourth aspect, the present disclosure relates to Base station comprising an apparatus according to the second aspect.

[0029] According to a fifth aspect, the present disclosure relates to wireless communication system for adjustment signaling using representative model by configuring a set of representative models, wherein the wireless communication systems comprises at least a user equipment according to the third aspect, at least a 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 implement steps according to the first aspect

[0030] According to a sixth aspect, the present disclosure relates to a computer program product comprising instructions which, when executed by at least one processor, configure said at least one processor to carry out a method according to the first aspect said at least one processor to carry out a method for exchanging data according to any one of the embodiments of the present disclosure. The computer program product can use any programming language, and can be in the form of source code, object code, or in any intermediate form between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0031] According to a seventh aspect, the present disclosure relates to a computer-readable storage medium comprising instructions which, when executed by at least one processor, configure said at least one processor to carry out a method according to any one of the embodiments of the present disclosure.

[0032] BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is an exemplary table of mapping relation between representative models and variation models. Figure 2 is an exemplary block diagram of relationship structure of representative model and variation models.

[0034] Figure 3 is an exemplary flow chart of configuring representative models and variation models at network side.

[0035] Figure 4 is an exemplary flow chart of activating variation model(s) at UE side.

[0036] Figure 5 is an exemplary signaling flow of UE's autonomous decision for variation model(s) for activation.

[0037] DETAILED DESCRIPTION

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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”)).

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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).

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] AI / ML model Inference is a process of using trained AI / ML model to produce set of outputs based on set of inputs.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] Model activation means enable an AI / ML model for specific AI / ML-enabled feature.

[0071] Model deactivation means disable an AI / ML model for specific AI / ML-enabled feature.

[0072] Model download means Model transfer from the network to UE.

[0073] 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.

[0074] Model monitoring is a procedure that monitors the inference performance of the AI / ML model.

[0075] 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.

[0076] 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.

[0077] Model upload is Model transfer from UE to the network.

[0078] Network-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the network.

[0079] Offline field data is the data collected from field and used for offline training of the AI / ML model.

[0080] 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.

[0081] Online field data is the data collected from field and used for online training of the AI / ML model.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] Supervised learning is a process of training model from input and its corresponding labels.

[0086] 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.

[0087] UE-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the UE.

[0088] Unsupervised learning is a process of training model without labelled data.

[0089] 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.

[0090] 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.

[0091] 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. 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.

[0092] 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. Variation of UE ML condition and / or ML capability status makes model pairing or alignment difficult so that the overall model performance gets degraded.

[0093] In this method, a set of representative models are pre-configured to be associated with one or multiple variation models so that mapping relation between representative model and the related variation model(s) can be pre-determined. One or more variation models for activation can be selected among candidate variation models associated with representative model. Both representative models and variation models in mapping relation information can be indicated using index or ID value. A set of specific variation models can be determined based on the configured measurement technique such as similarity metric or K-means clustering. Depending on device ML condition or ML capability, any available variation model(s) associated with the specific representative model can be chosen for activation. ML condition refers to the (non)real-time operational state of a ML model within a wireless communication system and it includes factors such as network load, dataset availability, device ML capability status, environmental conditions (e.g., site, signal interference), and / or device power constraints that may influence the selection and activation of a variation model. Also ML capability refers to the processing capacity and efficiency of a device (e.g., UE or network-side entity) in handling ML operations and it includes factors such as available computing resources (CPU, GPU, or NPU), memory capacity, model execution speed, supported ML frameworks, and the ability to process different complexity levels of variation models. Mapping relation information can be sent via system information or dedicated RRC signaling. Any additional changes or updates about mapping relation information can be also sent via L1 / L2 or RRC signaling. Decision of specific variation model(s) can be made by either network side or UE side depending on implementation scenarios.

[0094] Representative model can be defined as one of available variation models, ensemble model of the aggregated variation models, or any derived model achieving the average performance across the associated variation models.

[0095] Multiple variation models can be chosen and activated if applicable depending on different use cases. There can be different mapping relation tables for various model applications and / or ML conditions. One or more mapping relation tables can be configured and applied to ML operation between network side and UE side. With a given representative model, different variation models can have different complexity levels with inferencing performance difference. When representative model is transferred to UE, UE can select either representative model itself or any variation model(s) available at UE.

[0096] Figure 1 shows an exemplary table of mapping relation between representative models and variation models. In this example, a set of representative models are preconfigured to be associated with one or multiple variation models so that mapping relation between representative model and the related variation model(s) can be predetermined. One or more variation models for activation can be selected among candidate variation models associated with representative model. Both representative models and variation models in mapping relation information can be indicated using index or ID value.

[0097] Figure 2 shows an exemplary block diagram of relationship structure of representative model and variation models. In this example, representative model can be defined as one of available variation models, ensemble model of the aggregated variation models, or any derived model achieving the average performance across the associated variation models. Multiple variation models can be chosen and activated if applicable depending on different use cases. With a given representative model, different variation models can have different complexity levels with inferencing performance difference. When representative model is transferred to UE, UE can select either representative model itself or any variation model(s) available at UE.

[0098] Figure 3 shows an exemplary flow chart of configuring representative models and variation models at network side. In this example, a set of representative models are pre-configured to be associated with one or multiple variation models by network side. Depending on device ML condition or ML capability, any available variation model(s) associated with the specific representative model can be chosen for activation. Mapping relation information can be sent via system information or dedicated RRC signaling. Any additional changes or updates about mapping relation information can be also sent via L1 / L2 or RRC signaling.

[0099] Figure 4 shows an exemplary flow chart of activating variation model(s) at UE side. In this example, multiple variation models can be chosen and activated if applicable depending on different use cases. There can be different mapping relation tables for various model applications and / or ML conditions. One or more mapping relation tables can be configured and applied to ML operation between network side and UE side. With a given representative model, different variation models can have different complexity levels with inferencing performance difference. When representative model is transferred to UE, UE can select either representative model itself or any variation model(s) available at UE. Figure 5 shows an exemplary signaling flow of UE's autonomous decision for variation model(s) for activation. In this example, multiple variation models can be chosen and activated if applicable depending on different use cases. With a given representative model, different variation models can have different complexity levels with inferencing performance difference. When representative model is transferred to UE, UE can select either representative model itself or any variation model(s) available at UE. In addition, any activation and / or decision of selecting variation models can be performed by network side depending on implementation scenarios.

Claims

CLAIMS1. A method of model adjustment signaling using representative model by configuring a set of representative models within a wireless communication system, comprising:• Associating one or multiple variation models for a representative model;• Pre-determining mapping relation between representative model and the related variation model(s);• Setting index or ID values for representative models and variation models;• Providing mapping relation to UEs.

2. The method according to previous claim 1 , wherein one or more variation models for activation can be selected among candidate variation models associated with representative model.

3. The method according to one of the previous claims, wherein both representative models and variation models in mapping relation information can be indicated using index or ID value.

4. The method according to one of the previous claims, wherein a set of specific variation models can be determined based on the configured measurement technique such as similarity metric or K-means clustering.

5. The method according to one of the previous claims, wherein any available variation model(s) associated with the specific representative model can be chosen for activation depending on device ML condition or ML capability.

6. The method according to one of the previous claims, wherein mapping relation information can be sent via system information or dedicated RRC signaling.

7. The method according to one of the previous claims, wherein any additional changes or updates about mapping relation information can be also sent via L1 / L2 or RRC signaling.

8. The method according to one of the previous claims, wherein decision of specific variation model(s) can be made by either network side or UE side depending on implementation scenarios.

9. The method according to one of the previous claims, wherein representative model can be defined as one of available variation models, ensemble model of the aggregated variation models, or any derived model achieving the average performance across the associated variation models.

10. The method according to one of the previous claims, wherein multiple variation models can be chosen and activated if applicable depending on different use cases.11 . The method according to one of the previous claims, wherein there can be different mapping relation tables for various model applications and / or ML conditions.

12. The method according to one of the previous claims, wherein one or more mapping relation tables can be configured and applied to ML operation between network side and UE side.

13. The method according to one of the previous claims, wherein different variation models with a given representative model can have different complexity levels with inferencing performance difference.

14. The method according to one of the previous claims, wherein UE can select either representative model itself or any variation model(s) available at UE when representative model is transferred to UE.

15. The method according to one of the previous claims, wherein any activation and / or decision of selecting variation models can be performed by network side depending on implementation scenarios.

16. Apparatus for adjustment signaling using representative model by configuring a set of representative models within a wireless communication system the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 1 to 15.

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

18. gNB comprising an apparatus according to claim 16.

19. Wireless communication system for adjustment signaling using representative model by configuring a set of representative models, wherein the wireless communication systems comprises at least a user equipment according to claim 16, at least a gNB according to claim 13, whereby the user Equipment and the gNB each comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 1 to 15.

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