A method of configuring a set of the supported operation modes for ML functionality in a wireless communication system

Abstract

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based operation modes with ML functionality 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 operation using model activation for LCM phases can be degraded due to ML conditions. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring applicable operation modes with ML functionality.

Description

[0001] TITLE

[0002] A method of configuring a set of the supported operation modes for ML functionality in a wireless communication system

[0003] TECHNNICAL FIELD

[0004] The present disclosure relates to a set of ML based and non-ML based operation modes, where techniques for pre-configuring and signaling the specific information about operation modes and ML functionalities 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.

[0008] US 2023069342 describes how to assist determination of the model update time in consideration of cost for the update of a model.

[0009] US 2023022737 explains supporting generation of machine learning model when a certain machine learning model is changed.

[0010] US 2019012876 provides projections, predictions, and recommendations for computing system. US 2019332895 shows that the monitored states are to decide to change a trained ML model as currently used.

[0011] 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.

[0012] 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.

[0013] The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based operation modes with ML functionality 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 operation using model activation for LCM phases can be degraded due to ML conditions. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring applicable operation modes with ML functionality.

[0014] BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is an exemplary table of mapping relation between operation modes and ML functionalities.

[0016] Figure 2 is an exemplary table of operation mode options.

[0017] Figure 3 is an exemplary flow chart of configuring mapping relation at network side.

[0018] Figure 4 is an exemplary flow chart of operation mode switching at UE side. Figure 5 is an exemplary signaling flow of UE's autonomous decision of operation mode switching.

[0019] Figure 6 is an exemplary signaling flow of network's decision of operation mode switching.

[0020] DETAILED DESCRIPTION

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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

[0030] Any combination of one or more computer readable medium may be utilized. The computer readable medium may be a computer readable storage medium. The computer readable storage medium may be a storage device storing the code. The storage device may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the storage device would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random-access memory (“RAM”), a read-only memory (“ROM”), an erasable programmable read-only memory (“EPROM” or Flash memory), a portable compact disc readonly memory (“CD-ROM”), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0031] 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”)).

[0032] 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.

[0033] 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

[0034] 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.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] Although various arrow types and line types may be employed in the flowchart and / or block diagrams, they are understood not to limit the scope of the corresponding embodiments. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the depicted embodiment. For instance, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted embodiment. It will also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and code. The description of elements in each figure may refer to elements of proceeding figures. Like numbers refer to like elements in all figures, including alternate embodiments of like elements.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] In this context, model training or re-training is one of key issues for model performance maintenance as model performance such as inferencing and / or training is dependent on different model execution environment with varying configuration parameters. To handle this issue, collaboration between UE and gNB is highly important to track model performance and re-configure model corresponding to different environments. AI / ML model needs model monitoring after deployment because model performance cannot be maintained continuously due to drift and update feedback is then provided to re-train / update the model or select alternative model. When AI / ML model enabled wireless communication network is deployed, it is then important to consider how to handle AI / ML model in activation with reconfiguration for wireless devices under operations such as model training, inference, updating, etc. ML operation using model activation for LCM phases can be degraded due to dynamic ML conditions, and hence fallback operation such as non-ML mode can be even better depending on channel condition and / or ML capability status. In this method, a set of the supported operation modes for ML functionality can be configured so that each ML functionality can be supported with one or multiple ML models or model IDs. List of operation modes can include ML mode, non-ML mode and mixed mode (e.g., ML aided non-ML mode, non-ML aided ML mode). Mapping relation between operation modes and ML functionalities can be sent via system information or dedicated RRC signaling. One or multiple ML models or model IDs can be also associated with the mapping relation information together. Frequency level of operation mode switching can be event-triggered or periodic based on varying implementation conditions (e.g., ML application, LCM phase, environmental conditions, device ML conditions, model specifics, etc.). Operation mode switching can support ML throttling or threshold-based model complexity adjustment if applicable (e.g., ML mode to mixed mode switching scenario). Initial operation mode setting for activation can be managed by network side. UE ML capability or ML condition updates can be fed back to network side for decision of any specific operation mode activation.

[0048] For example, dataset / model ID are part of requirements to be ready to activate ML mode. Operation mode selection for any specific ML functionality and / or model can be prioritized among multiple modes available if applicable in advance. In non-ML mode or mixed mode operation, any available collected dataset for model input can be used for online training of specific model(s) as well. Mapping relation between operation modes and ML functionalities can be pre-configured. Mode_Set #i can contain one or more operation modes available that can be also prioritized with indication. A set of models for MF #i can be also associated in mapping relation information if applicable. Any change / update of mapping relation information can be provided via L1 / L2 or RRC signaling. For ML aided non-ML mode in mixed mode, non-ML mode is activated with support of the throttled ML operation such as model complexity adjustment and / or smaller dataset collection for use. For non-ML aided ML mode in mixed mode, ML mode is activated with support of non-ML operation in parallel. Any available collected dataset for model input can be used for online training of specific model(s).

[0049] Figure 1 shows an exemplary table of mapping relation between operation modes and ML functionalities. In this example, mapping relation between operation modes and ML functionalities can be pre-configured. Mode_Set #i can contain one or more operation modes available that can be also prioritized with indication. A set of models for MF #i can be also associated in mapping relation information if applicable. A set of the supported operation modes for ML functionality can be configured so that each ML functionality can be supported with one or multiple ML models or model IDs. Figure 2 shows an exemplary table of operation mode options. In this example, list of operation modes can include ML mode, non-ML mode and mixed mode (e.g., ML aided non-ML mode, non-ML aided ML mode). For ML aided non-ML mode in mixed mode, non-ML mode is activated with support of the throttled ML operation such as model complexity adjustment and / or smaller dataset collection for use. For non-ML aided ML mode in mixed mode, ML mode is activated with support of non-ML operation in parallel. Any available collected dataset for model input can be used for online training of specific model(s). In non-ML mode or mixed mode operation, any available collected dataset for model input can be used for online training of specific model(s) as well.

[0050] Figure 3 shows an exemplary flow chart of configuring mapping relation at network side. In this example, A set of the supported operation modes for ML functionality can be configured at network side so that each ML functionality can be supported with one or multiple ML models or model IDs.

[0051] Figure 4 shows an exemplary flow chart of operation mode switching at UE side. In this example, applicable operation mode can be switched based on the preconfigured mapping relation information provided by network side. Operation mode switching can support ML throttling or threshold-based model complexity adjustment if applicable (e.g., ML mode to mixed mode switching scenario). Initial operation mode setting for activation can be managed by network side. UE ML capability or ML condition updates can be fed back to network side for decision of any specific operation mode activation.

[0052] Figure 5 shows an exemplary signaling flow of UE's autonomous decision of operation mode switching. In this example, any specific operation mode can be activated and also switching of operation modes can be executed by UE side autonomously after mapping relation information is provided by network side.

[0053] Figure 6 shows an exemplary signaling flow of network's decision of operation mode switching. In this example, operation mode switching is indicated by network side when the activated operation mode status is reported so that UE can execute operation mode switching based on indication message.

Claims

CLAIMS1 . A method of configuring a set of the supported operation modes for ML functionality in a wireless communication system, comprising:• Determining mapping relation between operation modes and ML functionalities;• Defining operation modes;• Associating a set of model IDs with mapping relation information if applicable;• Providing mapping relation information to UEs.

2. The method according to previous claim 1 , wherein each ML functionality can be supported with one or multiple ML models or model IDs.

3. The method according to one of the previous claims, wherein list of operation modes can include ML mode, non-ML mode and mixed mode which could be ML aided non-ML mode and / or, non-ML aided ML mode.

4. The method according to one of the previous claims, wherein mapping relation between operation modes and ML functionalities can be sent via system information or dedicated RRC signaling.

5. The method according to one of the previous claims, wherein one or multiple ML models or model IDs can be associated with the mapping relation information together.

6. The method according to one of the previous claims, wherein frequency level of operation mode switching can be event-triggered or periodic based on varying implementation conditions, which could be ML application and / or LCM phase, environmental conditions and / or device ML conditions and / or model specifics.

7. The method according to one of the previous claims, wherein operation mode switching can support ML throttling or threshold-based model complexity adjustment if applicable like ML mode to mixed mode switching scenario.

8. The method according to one of the previous claims, wherein initial operation mode setting for activation can be managed by network side.

9. The method according to one of the previous claims, wherein UE ML capability or ML condition updates can be fed back to network side for decision of any specific operation mode activation.

10. The method according to one of the previous claims, wherein dataset / model ID are part of requirements to be ready to activate ML mode.11 . The method according to one of the previous claims, wherein operation mode selection for any specific ML functionality and / or model can be prioritized among multiple modes available if applicable in advance.

12. The method according to one of the previous claims, wherein any available collected dataset for model input can be used for online training of specific model(s) in non-ML mode or mixed mode operation.

13. The method according to one of the previous claims, wherein one or more operation modes available belonging to a specific operation mode set can be prioritized with indication.

14. The method according to one of the previous claims, wherein a set of models for specific ML functionality can be associated in mapping relation information if applicable.

15. The method according to one of the previous claims, wherein any change / update of mapping relation information can be provided via L1 / L2 or RRC signaling.

16. The method according to one of the previous claims, where non-ML mode (ML aided non-ML mode in mixed mode) is activated with support of the throttled ML operation such as model complexity adjustment and / or smaller dataset collection for use.

17. The method according to one of the previous claims, wherein ML mode (non-ML aided ML mode in mixed mode) is activated with support of non-ML operation in parallel.

18. The method according to one of the previous claims, wherein any available collected dataset for model input can be used for online training of specific model(s).

19. Apparatus for configuring a set of the supported operation modes for ML functionality in a wireless communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 1 to 18.

20. User Equipment comprising an apparatus according to claim 19.

21. gNB comprising an apparatus according to claim 19.

22. Wireless communication system for configuring a set of the supported operation modes for ML functionality, wherein the wireless communication systems comprises at least a user equipment according to claim 20 at least a gNB according to claim 21 , 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 18.

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