Method of cross-level model 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 switching or adaptation can be heavily loaded based on dynamic changes of ML applicable conditions. Therefore, model adaptation can be performed between network and UE by reducing model performance degradation with lower signaling overhead.

Description

[0001] TITLE

[0002] Method of cross-level model 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 cross-level model set 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] US2020374711 A1 describes a representation of a local model of the first data endpoint of the radio access network with multiple common models for endpoints of the radio access network, selecting one of multiple common models for the first data endpoint and transmitting the selected common model to the first data endpoint, any other data endpoint or any other external system which utilizes the selected common model.

[0008] US2023093673A1 describes configuration of generating a plurality of network measurements for a corresponding plurality of network functions where the functions are configured as a plurality of ML models forming a multi-level hierarchy.

[0009] US2023275812A1 describes AI / ML model split into several unitary chunks that correspond to sub-parts of the model where aggregation of unitary chunks is made by considering the download time, inference time of unitary chunks, and / or device constraints.

[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, UE-side model can be very unstable due to dynamic changes of ML applicable conditions (e.g., UE mobility, site, on-device ML status). Therefore, the overall ML model operation between network and UE can be interrupted with performance degradation and the related signaling overhead can be highly increased due to model update transfer between base station (BS / gNB) and UE.

[0012] According to a first aspect, the present disclosure relates to a method of configuring cross-level models from source default model in a wireless communication system, comprising configuring a finite set of cross-level models from partition process of any application-specific source default model; setting source default model as the base model for the associated target application; configuring each cross-level models to have the varying model complexity levels by adjusting parameter set of source default model such as number of nodes / layers and / or hyperparameters.

[0013] 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.

[0014] In some embodiments of the method according to the first aspect, the method is characterized by, that any associated functionality and / or feature set of source default model can be adjusted in multiple levels for each cross-level models if configurable based on different implementation use cases.

[0015] In some embodiments of the method according to the first aspect, the method is characterized by, that UE can apply specific cross-level models with different levels of complexity for the same application used with source default model based on ML applicable conditions on device side.

[0016] In some embodiments of the method according to the first aspect, the method is characterized by, that UE can select one or more of cross-level models adaptively depending on dynamic changes of UE ML applicable conditions.

[0017] In some embodiments of the method according to the first aspect, the method is characterized by, that a finite set of cross-level model information can be shared with UE for UE autonomous decision about cross-level model selection. In some embodiments of the method according to the first aspect, the method is characterized by, that cross-level model selection is done via system information or RRC.

[0018] In some embodiments of the method according to the first aspect, the method is characterized by, that cross-level model can be decided by either network side or UE side, comprising, network side can determine specific cross-level model(s) for UE when ML assistance information is sent from UE, in which ML applicable conditions and / or ML capability information are updated to network; UE side can determine specific cross-level model(s) autonomously using the pre-configured threshold value provided by network side with or without mapping relationship information, whereby the mapping relationship information could be done via RRC signaling.

[0019] In some embodiments of the method according to the first aspect, the method is characterized by, that a finite set of cross-level models are indexed in association with threshold configuration.

[0020] In some embodiments of the method according to the first aspect, the method is characterized by, that threshold for ML applicable condition range is configured to estimate ML capability for cross-level model support.

[0021] In some embodiments of the method according to the first aspect, the method is characterized by, that different size of cross-level model set and / or threshold values can be network implementation for different scenarios or application-specific use cases.

[0022] In some embodiments of the method according to the first aspect, the method is characterized by, that the associated cross-level models are updated when source default model is updated or re-configured.

[0023] In some embodiments of the method according to the first aspect, the method is characterized by, that all configured cross-level model information as embedded into mixture of cross-level models is sent to UEs through multicast or broadcast so that no further individual cross-level model transfer to each UEs is needed until source default model is re-configured.

[0024] In some embodiments of the method according to the first aspect, the method is characterized by, that the associated cross-level model information for each indexed cross-level models can contain configuration information about how to obtain the indexed cross-level model from the embedded structure of the mixture of all crosslevel models.

[0025] In some embodiments of the method according to the first aspect, the method is characterized by, that the configured finite set of cross-level models can be superimposed onto unified superposition model so that the superpositioned crosslevel models can be retrieved based on the pre-configured assistance information provided by network side.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[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 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.

[0032] BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is an exemplary block diagram of relationship between source default model and multiple cross-level models.

[0034] Figure 2 is an exemplary block diagram of index-based mapping relationship for multiple cross-level models.

[0035] Figure 3 is an exemplary signaling flow of deciding cross-level model selection on UE side.

[0036] Figure 4 is an exemplary signaling flow of deciding cross-level model selection on network side.

[0037] Figure 5 is an exemplary block diagram of multi-UE based cross-level model selection.

[0038] Figure 6 is an exemplary block diagram of threshold-based cross-level model index. Figure 7 is an exemplary flow chart of network side behavior for applying cross-level models.

[0039] Figure 8 is an exemplary flow chart of UE side behavior for applying cross-level models.

[0040] DETAILED DESCRIPTION The detailed description set forth below, with reference to annexed drawings, is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. In particular, although terminology from 3GPP 5G NR may be used in this disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the invention.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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

[0049] 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.

[0050] 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.

[0051] 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”)).

[0052] 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.

[0053] 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

[0054] 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.

[0055] The code may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer implemented process such that the code which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart diagrams and / or block diagrams. The flowchart diagrams and / or block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, systems, methods, and program products according to various embodiments. In this regard, each block in the flowchart diagrams and / or block diagrams may represent a module, segment, or portion of code, which includes one or more executable instructions of the code for implementing the specified logical function(s).

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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). 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.

[0068] 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.

[0069] 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.

[0070] However, the overall ML model operation between network and UE can be interrupted with performance degradation and the related signaling overhead can be highly increased due to model update transfer between gNB and UE when UE-side model is very unstable due to dynamic changes of ML applicable conditions (e.g., UE mobility, site, on-device ML status).

[0071] In this method, a finite set of cross-level models can be configured from partition process of any application-specific source default model. Source default model can be the base model for the associated target application and each cross-level models can have the varying model complexity levels by adjusting parameter set of source default model such as number of nodes / layers and / or hyperparameters, etc. Also any associated functionality and / or feature set of source default model can be adjusted in multiple levels for each cross-level models if configurable based on different implementation use cases. UE can apply specific cross-level models with different levels of complexity for the same application used with source default model based on ML applicable conditions on device side. To apply cross-level models, UE can select one or more of cross-level models adaptively depending on dynamic changes of UE ML applicable conditions. A finite set of cross-level model information can be shared with UE for UE autonomous decision about cross-level model selection (e.g., via system information or RRC). For decision of selecting specific cross-level models, cross-level model can be decided by either network side or UE side.

[0072] For example, network side can determine specific cross-level model(s) for UE when ML assistance information is sent from UE, in which ML applicable conditions and / or ML capability information are updated to network. On the other hand, UE side can determine specific cross-level model(s) autonomously using the pre-configured threshold value (provided by network side) with or without mapping relationship information (e.g., via RRC signaling) where a finite set of cross-level models are indexed in association with threshold configuration and threshold for ML applicable condition range is configured to estimate ML capability for cross-level model support. Different size of cross-level model set and / or threshold values can be network implementation for different scenarios or application-specific use cases. In addition, cross-level models can be applied to both multiple UEs and a single UE device with or without multiple ML model activations. Figure 1 shows an exemplary block diagram of relationship between source default model and multiple cross-level models. A finite set of cross-level models can be configured from partition process of any application-specific source default model. Source default model can be the base model for the associated target application and each cross-level models can have the varying model complexity levels by adjusting parameter set of source default model such as number of nodes / layers and / or hyperparameters, etc. Also any associated functionality and / or feature set of source default model can be adjusted in multiple levels for each cross-level models if configurable based on different implementation use cases.

[0073] Figure 2 shows an exemplary block diagram of index-based mapping relationship for multiple cross-level models. Mapping relationship information (e.g., via RRC signaling) can be configured where a finite set of cross-level models are indexed in association with threshold configuration and threshold for ML applicable condition range is configured to estimate ML capability for cross-level model support. For each indexed cross-level models, the associated cross-level model information can contain configuration information about how to obtain the indexed cross-level model from the embedded structure of the mixture of all cross-level models. For example, when cross-level models are transferred from network side to UE side, the configured finite set of cross-level models can be superimposed onto unified superposition model so that the superpositioned cross-level models can be retrieved based on the preconfigured assistance information provided by network side.

[0074] Figure 3 shows an exemplary signaling flow of deciding cross-level model selection on UE side. In this example, UE side can determine specific cross-level model(s) autonomously using the pre-configured threshold value (provided by network side) with or without mapping relationship information.

[0075] Figure 4 shows an exemplary signaling flow of deciding cross-level model selection on network side. In this example, Network side can determine specific cross-level model(s) for UE when ML assistance information is sent from UE, in which ML applicable conditions and / or ML capability information are updated to network. Figure 5 shows an exemplary block diagram of multi-UE based cross-level model selection. In this example, a finite set of cross-level models (e.g., {V}) is configured on network side and different cross-level models are selected across multiple UEs individually where selection decision is made by UEs. Once all configured cross-level model information (e.g., as embedded into mixture of cross-level models) is sent to UEs through multicast or broadcast, no further individual cross-level model transfer to each UEs is needed so that signaling overhead can be saved. However, if source default model is updated or re-configured, then the associated cross-level models might need to be updated as well.

[0076] Figure 6 shows an exemplary block diagram of threshold-based cross-level model index. In this example, threshold values are configured to determine applicable cross-level models based on different thresholds. Measurement of threshold values is implementation-specific for different ML applications or use cases. However, mapping relationship between configured thresholds and indexed cross-level models can be generated.

[0077] Figure 7 shows an exemplary flow chart of network side behavior for applying crosslevel models. In this example, network side configures ML configuration information with cross-level model set so that different cross-level models can be applied to multiple UEs based on ML applicable conditions. When two-sided model is configured between UE and network side, model re-configuration might be needed on network side when cross-level model is updated on UE side.

[0078] Figure 8 shows an exemplary flow chart of UE side behavior for applying cross-level models. In this example, UE can autonomously select the relevant cross-level model(s) based on the received ML configuration information including cross-level model information so that UE side can determine the specific cross-level model selection.

Claims

CLAIMS1 . A method of configuring cross-level models from source default model in a wireless communication system, comprising:• Configuring a finite set of cross-level models from partition process of any application-specific source default model;• Setting source default model as the base model for the associated target application;• Configuring each cross-level models to have the varying model complexity levels by adjusting parameter set of source default model such as number of nodes / layers and / or hyperparameters.

2. The method according to previous claim 1 , wherein any associated functionality and / or feature set of source default model can be adjusted in multiple levels for each cross-level models if configurable based on different implementation use cases.

3. The method according to any one of the preceding claims, wherein UE can apply specific cross-level models with different levels of complexity for the same application used with source default model based on ML applicable conditions on device side.

4. The method according to any one of the preceding claims, wherein UE can select one or more of cross-level models adaptively depending on dynamic changes of UE ML applicable conditions.

5. The method according to any one of the preceding claims, wherein a finite set of cross-level model information can be shared with UE for UE autonomous decision about cross-level model selection.

6. The method according to any one of the preceding claims, wherein cross-level model selection is done via system information or RRC.

7. The method according to any one of the preceding claims, wherein cross-level model can be decided by either network side or UE side, comprising:• Network side can determine specific cross-level model(s) for UE when ML assistance information is sent from UE, in which ML applicable conditions and / or ML capability information are updated to network;• UE side can determine specific cross-level model(s) autonomously using the pre-configured threshold value provided by network side with or without mapping relationship information, whereby the mapping relationship information could be done via RRC signaling.

8. The method according to any one of the preceding claims, wherein a finite set of cross-level models are indexed in association with threshold configuration.

9. The method according to any one of the preceding claims, wherein threshold for ML applicable condition range is configured to estimate ML capability for crosslevel model support.

10. The method according to any one of the preceding claims, wherein different size of cross-level model set and / or threshold values can be network implementation for different scenarios or application-specific use cases.11 . The method according to any one of the preceding claims, wherein the associated cross-level models are updated when source default model is updated or reconfigured.

12. The method according to any one of the preceding claims, wherein all configured cross-level model information as embedded into mixture of cross-level models is sent to UEs through multicast or broadcast so that no further individual cross-level model transfer to each UEs is needed until source default model is re-configured.

13. The method according to any one of the preceding claims, wherein the associated cross-level model information for each indexed cross-level models can containconfiguration information about how to obtain the indexed cross-level model from the embedded structure of the mixture of all cross-level models.

14. The method according to any one of the preceding claims, wherein the configured finite set of cross-level models can be superimposed onto unified superposition model so that the superpositioned cross-level models can be retrieved based on the pre-configured assistance information provided by network side.

15. 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.

16. A user equipment, UE, comprising a wireless device according to claim 15.

17. 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 14.

18. A wireless communication system comprising at least one base station according to claim 17 and at least one user equipment according to claim 16.

19. 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.

20. 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 (40) according to any one of claims 1 to 14.

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