Method of model-sharing signaling in a wireless communication system

Abstract

The present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model information sharing in wireless mobile communication system including base station (e.g., gNB) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling of model information supporting multiple UEs and / or applications can be heavily loaded when ML applicable conditions vary across different UEs / applications. Therefore, model- sharing can be adaptively performed between network and UE by reducing signaling overhead with model performance improvement.

Description

[0001] TITLE

[0002] Method of model-sharing signaling in a wireless communication system

[0003] TECHNNICAL FIELD

[0004] The present disclosure relates to AI / ML based model information sharing, where techniques for pre-configuring and signaling the multiple model-sharing 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”, and currently RAN WG1 (Working Group 1 ) and WG2 are actively working on specification. 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 will be one of key work scope. 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. Earlier, in 3GPP TR 37.817 for Release 17, titled as Study on enhancement for Data Collection for NR and EN-DC, UE (user equipment) mobility was also considered as one of AI / ML use cases and one of scenarios for model training / inference is that both functions are located within RAN node. Followingly, in Release 18 the new work item of “Artificial Intelligence (AI)ZMachine Learning (ML) for NG-RAN” was initiated to specify data collection enhancements and signaling support within existing NG-RAN interfaces and architecture. For the above active standardization works, model identification (e.g., model ID) to support 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 ID information can be signaled to pair both network-side and UE-side models for various lifecycle management (LCM) operations.

[0007] US2022353803A1 describes a machine-learning architecture for network slicing where the UE selects a machine-learning architecture that provides a quality-of- service level requested by an application the network-slice manager can determine an appropriate machine-learning architecture that satisfies a quality-of- service level associated with the application.

[0008] US20230048206A1 describes methods for controlling machine learning model structures where the machine learning model structure may be controlled based on an environmental condition.

[0009] W02022207102A1 describes a method for a feasibility check of a new RAN slice performed by a network node with computing the estimate of occupied resources using historical data of a measurement of utilization of each resource.

[0010] US2016379137A1 describes a method for processing on an acceleration component a machine learning classification model where the machine learning classification model includes a plurality of decision trees, the decision trees including a first amount of decision tree data by including slicing the model into a plurality of model slices.

[0011] However, when there are multiple UEs and / or multiple applications sharing the same common AI / ML model, some UEs might experience ML applicable condition change and those UEs then cannot continue to use the same common AI / ML model due to model drift or model performance degradation. Therefore, it is necessary to specify using common AI / ML model to support multiple UEs and / or multiple applications when the used common model can fail (e.g., due to model drift) between base station (BS / gNB) and UE. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is an exemplary block diagram of joint model state and separate model state.

[0013] Figure 2 is an exemplary block diagram of split / combine modes in association with separate / joint model states.

[0014] Figure 3 is an exemplary block diagram of multi-stack model based joint-to-separate model state transition.

[0015] Figure 4 is an exemplary block diagram of multi-stack model based model state switching.

[0016] Figure 5 is an exemplary signaling flow of configuring model state with model operation mode.

[0017] Figure 6 is an exemplary signaling flow of network-UE behaviors for model state switching.

[0018] Figure 7 is an exemplary flow chart of network side behavior for applying model state based model operation.

[0019] Figure 8 is an exemplary flow chart of UE side behavior for applying model state based model operation.

[0020] According to a first aspect, the present disclosure relates to a method of modelsharing signaling in a wireless communication system with configuring two model states with the associated modes for AI / ML model, comprising, configuring AI / ML model operation categorized into split mode and combine mode, configuring two model states as joint model state and separate model state in association with model operation modes, setting AI / ML model to consist of a number of component-wise models (e.g., in serialized and / or parallelized structure or cascaded type as multistack model), switching between separate model state and joint model state for multiple UEs and / or applications.

[0021] In some embodiments, the method according to the first aspect can further comprise one or more of the following optional features, considered either alone or in any technically possible combination. In some embodiments of the method according to the first aspect, the method is characterized by, that AI / ML model is configured to be used for either the dedicated UE / application or multiple UEs / applications where a model can be commonly used across multiple UEs and / or applications while any dedicated UE or application can be served with a model.

[0022] In some embodiments of the method according to the first aspect, the method is characterized by, that a model for split mode is derived into two or more separate models to serve different UEs and / or applications with dedicated separate models, respectively.

[0023] In some embodiments of the method according to the first aspect, the method is characterized by, that wherein multiple models for combine mode are integrated into unified model to serve multiple UEs / applications.

[0024] In some embodiments of the method according to the first aspect, the method is characterized by, that split mode is based on unicast model information signaling for different UEs and / or applications, individually.

[0025] In some embodiments of the method according to the first aspect, the method is characterized by, that combine mode is based on multicast / groupcast model information signaling for group of UEs and / or applications.

[0026] In some embodiments of the method according to the first aspect, the method is characterized by, that the associated model(s) activated with split mode is on separate model state as each UEs are served with dedicated ML models individually where model can be split into multiple (sub-)models applied to different UEs / applications in separate model state.

[0027] In some embodiments of the method according to the first aspect, the method is characterized by, that the associated model(s) activated with combine mode is on joint model state as the connected UEs are served with the common same ML model where multiple models can be combined together in joint model state with multicast / groupcast or broadcast.

[0028] In some embodiments of the method according to the first aspect, the method is characterized by, split / combine mode-based ML operation configuration information is generated by network side and shared with UE side.

[0029] In some embodiments of the method according to the first aspect, the method is characterized by, that operation configuration information is generated via system information or RRC.

[0030] In some embodiments of the method according to the first aspect, the method is characterized by, that split / combine mode with separate / joint model state can be adaptively changed based on the ML applicable conditions or environments where split / combine mode switching information can be sent as indication message between gNB and UE when ML model operation can be switched between joint model state and separate model state.

[0031] In some embodiments of the method according to the first aspect, the method is characterized by, the indication message is sent via L1 / L2 or RRC signaling.

[0032] In some embodiments of the method according to the first aspect, the method is characterized by, that the specific threshold to determine split / combine mode for the associated model(s) can be configured to assess the relevant model state between joint model and separate model states for the served UEs and / or applications on each UEs.

[0033] In some embodiments of the method according to the first aspect, the method is characterized by, that each component-wise models of AI / ML model can be on either split or combine mode with separate model state or joint model state.

[0034] In some embodiments of the method according to the first aspect, the method is characterized by, that either unicast or multicast / broadcast can be enabled to signal model information about each component-wise models across multiple UEs and / or applications depending on model states of each component-wise models.

[0035] In some embodiments of the method according to the first aspect, the method is characterized by, that each component-wise models of AI / ML model can be switched between separate model state and joint model state for multiple UEs and / or applications.

[0036] In some embodiments of the method according to the first aspect, the method is characterized by, that joint / separate model states can be switched dynamically where model switching decision can be executed on network side or UE side depending on implementation scenarios.

[0037] According to a second aspect, the present disclosure relates to a wireless device comprising at least one memory and at least one processor configured to carry out a method according to any one of the embodiments of the first aspect.

[0038] According to a third aspect, the present disclosure relates to a user equipment, UE, comprising a wireless device according to any one of the embodiments of the present disclosure.

[0039] According to a fourth aspect, the present disclosure relates to a base station, BS, comprising at least one memory and at least one processor configured to carry out a method according to any one of the embodiments of the first aspect.

[0040] According to a fifth aspect, the present disclosure relates to a wireless communication system comprising at least one base station according to any one of the embodiments of the present disclosure and at least one user equipment according to any one of the embodiments of the present disclosure.

[0041] 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.

[0042] According to a sixth 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.

[0043] DETAILED DESCRIPTION

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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”)).

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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).

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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. Also, the order of steps of any methods disclosed herein, in particular in the figures, is provided only for illustration purposes and is not meant to limit the present disclosure which may be applied with the same steps executed in a different order and / or with all or part of the steps executed in parallel or jointly, 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. Also, in a figure, steps represented surrounded by a dashed line are to be considered as optional for the embodiment represented in this figure. 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. 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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 program-code instructions to be executed by the one or more processors to implement all or part of the steps of a method for exchanging data, performed at a UE’s side, according to any one of the embodiments disclosed herein. The wireless device can comprise also a main radio, MR, unit. The MR unit corresponds to a main wireless communication unit of the wireless device, used for exchanging data with BSs of the RAN using radio signals. The MR unit may implement one or more wireless communication protocols, and may for instance be a 3G, 4G, 5G, NR, WiFi, WiMax, etc. transceiver or the like. In preferred embodiments, the MR unit corresponds to a 5G NR wireless communication unit.

[0068] The following explanation will provide the detailed description of the mechanism about pre-configuring AI / ML-based model before handover occurrence in wireless mobile communication system including base station (e.g., gNB) and mobile station (e.g., UE).

[0069] The following explanation will provide the detailed description of the mechanism about pre-configuring and signaling the specific information about model selection using association between models and index values. 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 selection 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. However, when there are multiple UEs and / or multiple applications sharing the same common AI / ML model, some UEs might experience ML applicable condition change and those UEs then cannot continue to use the same common AI / ML model due to model drift or model performance degradation. Therefore, it is necessary to specify using common AI / ML model to support multiple UEs and / or multiple applications when the used common model can fail (e.g., due to model drift) between network and UE.

[0070] In this method, AI / ML model is configured to be used for either the dedicated UE / application or multiple UEs / applications where a model can be commonly used across multiple UEs and / or applications while any dedicated UE or application can be served with a model. Accordingly, AI / ML model operation can be categorized into two modes such as split mode and combine mode. For split mode, a model is derived into two or more separate models to serve different UEs and / or applications with dedicated separate models, respectively. For combine mode, multiple models are integrated into unified model to serve multiple UEs / applications. In the model signaling aspect, split mode is based on unicast model information signaling for different UEs and / or applications, individually, and combine mode is based on multicast / groupcast model information signaling for group of UEs and / or applications. Therefore, when AI / ML is activated with split mode, the associated model(s) is on separate model state and each UEs are served with dedicated ML models individually where model can be split into multiple (sub-)models applied to different UEs / applications in separate model state.

[0071] On the other hand, when AI / ML is activated with combine mode, the associated model(s) is on joint model state and the connected UEs are served with the common same ML model where multiple models can be combined together in joint model state with multicast / groupcast or broadcast. Split / combine mode-based ML operation configuration information is generated by network side and shared with UE side (e.g., via system information or RRC). Also Split / combine mode with separate / joint model state can be adaptively changed based on the ML applicable conditions or environments where split / combine mode switching information can be sent as indication message (e.g., via L1 / L2 or RRC signaling) between gNB and UE when ML model operation can be switched between joint model state and separate model state.

[0072] To determine split / combine mode for the associated model(s), the specific threshold can be configured to assess the relevant model state between joint model and separate model states for the served UEs and / or applications on each UEs. When AI / ML model consists of a number of component-wise models (e.g., in serialized and / or parallelized structure or cascaded type as multi-stack model), each component-wise models can be also on either split or combine mode with separate model state or joint model state as well where component-wise models can be generated by a single entity or different entities as combinations. In this case, each component-wise models need to be switched between separate model state and joint model state for multiple UEs and / or applications. In benefit aspect, signaling overhead due to model information exchange (e.g., model transfer / delivery, model update, model configuration, etc.) can be reduced with adaptive unicast and / or multicast / broadcast signaling with UEs and compute power / resource reduction for model operation with multiple models in parallel can be reduced as different component-wise models are applied to different UEs. It is also noted that componentwise models can be part of AI / ML model or can be independent models with different features and / or functionalities that can be connected to each other.

[0073] Figure 1 shows an exemplary block diagram of joint model state and separate model state. In this example, two UEs are assumed to have AI / ML model operation with model A. For joint model state, common model is used for both UEs. For separate model state, model A is split into two separate models and each models are used for individual UEs, respectively. Specifically for separate model state, two split models can be different versions of the same model where one model is version 1 .x and the other model is version 2.x, as an example. Or two split models can be sub-models of model A based on different levels of model complexity or other model attribute data and / or model properties.

[0074] Figure 2 shows an exemplary block diagram of split / combine modes in association with separate / joint model states. In this example, split mode enables separate model state with UE-specific model signaling (e.g., via unicast) and combine mode enables joint model state with UE group model signaling (e.g., via multicast or broadcast). AI / ML model can be configured on either modes with the associated model states based on ML applicable conditions or environmental information in association with model-specific use case or characteristics.

[0075] Figure 3 shows an exemplary block diagram of multi-stack model based joint-to- separate model state transition. In this example, AI / ML model consists of a number of component-wise models (e.g., in serialized and / or parallelized structure or cascaded type as multi-stack model) and AI / ML model in joint model state is switched into separate model state to serve two UEs separately in this example. In deployment scenarios, there can be many UEs served with AI / ML models in either joint or separate model states where model states can be switched dynamically. Model switching decision can be executed on network side or UE side depending on implementation scenarios.

[0076] Figure 4 shows an exemplary block diagram of multi-stack model based model state switching. In this example, each component-wise models of AI / ML model can be on either split or combine mode with separate model state or joint model state. In this case, each component-wise models need to be switched between separate model state and joint model state for multiple UEs and / or applications. Depending on model states of each component-wise models, either unicast or multicast / broadcast can be enabled to signal model information about each component-wise models across multiple UEs and / or applications.

[0077] Figure 5 shows an exemplary signaling flow of configuring model state with model operation mode. In this example, model state decision is made by network side based on UE feedback signaling about model monitoring information. To determine split / combine mode for the associated model(s), the specific threshold can be configured to assess the relevant model state between joint model and separate model states for the served UEs and / or applications on each UEs.

[0078] Figure 6 shows an exemplary signaling flow of network-UE behaviors for model state switching. In this example, model state decision is made by network side and model states are switched adaptively based on model monitoring information. However, model state can be also determined by UE side based on the pre-configured threshold information provided by network side so that model state can be autonomously switched on UE side as well. After adapting the relevant model state, UE reports to network side for model state update so that network side can reconfigure the overall network-UE model operation.

[0079] Figure 7 shows an exemplary flow chart of network side behavior for applying model state based model operation. In this example, ML configuration is generated and provided to UE when network side configures model state based AI / ML model operation with the associated ML configuration information. To trigger the relevant model state between joint model and separate model states, the criteria can be preconfigured with the implementation-specific metric for measurement process.

[0080] Figure 8 shows an exemplary flow chart of UE side behavior for applying model state based model operation. In this example, AI / ML model operation at UE is activated based on ML configuration information sent by network side and model state can be determined by either network side or UE side. When UE determines model state autonomously, a single-bit indication can be used to indicate either joint model state or separate model state together with the associated model assistance information related to model state change.

Claims

CLAIMS1 . A method of model-sharing signaling in a wireless communication system with configuring two model states with the associated modes for AI / ML model, comprising:• Configuring AI / ML model operation categorized into split mode and combine mode;• Configuring two model states as joint model state and separate model state in association with model operation modes;• Setting AI / ML model to consist of a number of component-wise models (e.g., in serialized and / or parallelized structure or cascaded type as multistack model);• Switching between separate model state and joint model state for multiple UEs and / or applications.

2. The method according to previous claim 1 , wherein AI / ML model is configured to be used for either the dedicated UE / application or multiple UEs / applications where a model can be commonly used across multiple UEs and / or applications while any dedicated UE or application can be served with a model.

3. The method according to any one of the preceding claims, wherein a model for split mode is derived into two or more separate models to serve different UEs and / or applications with dedicated separate models, respectively.

4. The method according to any one of the preceding claims, wherein multiple models for combine mode are integrated into unified model to serve multiple UEs / applications.

5. The method according to any one of the preceding claims, wherein split mode is based on unicast model information signaling for different UEs and / or applications, individually.

6. The method according to any one of the preceding claims, wherein combine mode is based on multicast / groupcast model information signaling for group of UEs and / or applications.

7. The method according to any one of the preceding claims, wherein the associated model(s) activated with split mode is on separate model state as each UEs are served with dedicated ML models individually where model can be split into multiple (sub-)models applied to different UEs / applications in separate model state.

8. The method according to any one of the preceding claims, wherein the associated model(s) activated with combine mode is on joint model state as the connected UEs are served with the common same ML model where multiple models can be combined together in joint model state with multicast / groupcast or broadcast.

9. The method according to any one of the preceding claims, wherein split / combine mode-based ML operation configuration information is generated by network side and shared with UE side.

10. The method according to any one of the preceding claims, wherein operation configuration information is generated via system information or RRC.11 . The method according to any one of the preceding claims, wherein split / combine mode with separate / joint model state can be adaptively changed based on the ML applicable conditions or environments where split / combine mode switching information can be sent as indication message between gNB and UE when ML model operation can be switched between joint model state and separate model state.

12. The method according to any one of the preceding claims, wherein the indication message is sent via L1 / L2 or RRC signaling.

13. The method according to any one of the preceding claims, wherein the specific threshold to determine split / combine mode for the associated model(s) can beconfigured to assess the relevant model state between joint model and separate model states for the served UEs and / or applications on each UEs.

14. The method according to any one of the preceding claims, wherein each component-wise models of AI / ML model can be on either split or combine mode with separate model state or joint model state.

15. The method according to any one of the preceding claims, wherein either unicast or multicast / broadcast can be enabled to signal model information about each component-wise models across multiple UEs and / or applications depending on model states of each component-wise models.

16. The method according to any one of the preceding claims, wherein each component-wise models of AI / ML model can be switched between separate model state and joint model state for multiple UEs and / or applications.

17. The method according to any one of the preceding claims, wherein joint / separate model states can be switched dynamically where model switching decision can be executed on network side or UE side depending on implementation scenarios.

18. A wireless device comprising at least one memory and at least one processor configured to carry out a method according to any one of the preceding claims.

19. A user equipment, UE, comprising a wireless device according to claim 16.

20. A base station, BS, comprising at least one memory and at least one processor configured to carry out a method according to any one of claims 1 to 15.

21. A wireless communication system comprising at least one base station according to claim 18 and at least one user equipment according to claim 17.

22. 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 any one of claims 1 to 14.

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