Method of collaborative sensing based model operation

Abstract

The present disclosure describes methods of using sensing signal type based AI / ML (artificial intelligence / machine learning) model operation modes 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, model performance can be significantly impacted due to data drift. Therefore, model operation e.g., model training / inferencing / monitoring / updating can be adaptively set up between network and UE by using sensing signal types.

Description

[0001] 202403886

[0002] - 1 -

[0003] TITLE

[0004] Method of collaborative sensing based model operation

[0005] TECHNNICAL FIELD

[0006] The present disclosure relates to AI / ML based model operation with sensing signal types, where techniques for pre-configuring and signaling the specific information about sensing signal type based model operation applicable to radio access network are presented.

[0007] BACKGROUND

[0008] In 3GPP (Third Generation Partnership Project), one of the selected study items as the approved Release 18 package is AI / ML (artificial intelligence / machine learning) as described in the related document (RP-213599) addressed in 3GPP TSG (Technical Specification Group) RAN (Radio Access Network) meeting #94e. The official title of AI / ML study item is “Study on AI / ML for NR Air Interface”. The goal of this study item is to identify a common AI / ML framework and areas of obtaining gains using AI / ML based techniques with use cases. According to 3GPP, the main objective of this study item is to study AI / ML framework for air-interface with target use cases by considering performance, complexity, and potential specification impact. In particular, AI / ML model, terminology and description to identify common and specific characteristics for framework are included as one of key work scopes. Regarding AI / ML framework, various aspects are under consideration for investigation and one of key items is about lifecycle management of AI / ML model where multiple stages are included as mandatory for model training, model deployment, model inference, model monitoring, model updating etc. 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.

[0009] 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 202403886

[0010] - 2 - 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 network-side and UE-side models for various lifecycle management (LCM) operations.

[0011] 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.

[0012] US 2022400373 describes the method of determining neural network functions and configuring models for performing wireless communications management procedures.

[0013] US 2022108214 explains ML model management method for network data analytics function device, and US 2022337487 shows that a network entity determines at least one model parameter of a model for digitally analyzing input data depending on the at least one model parameter of a model, the network entity being configured to receive a model request. 202403886

[0014] - 3 -

[0015] WO 2023277780 contains a method of downloading of a compiled machine code version of a ML model to a wireless communication device.

[0016] WO 2022258149 provides a way for training a model in a server device based on training data in a user device, and WO 2022228666 shows about influencing training of a ML model based on a training policy provided by an actor node.

[0017] WO 2022161624 describes the method of receiving a request for retrieving or executing a ML model or a combination of ML models.

[0018] The present disclosure solves the cited problem above by the proposed embodiments and describes methods of using sensing signal type based AI / ML (artificial intelligence / machine learning) model operation modes 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, model performance can be significantly impacted due to data drift. Therefore, model operation e.g., model training / inferencing / monitoring / updating can be adaptively set up between network and UE by using sensing signal types.

[0019] In the first embodiment, the method of collaborative sensing based model operation by configuring sensing signaling types for split sensing and combined sensing in wireless communication, is characterized by comprising the steps, applying either dedicated model ID or common model ID in association with different sensing signaling types; presetting mapping relation between applicable model IDs and sensing signal types; transmitting indication of specific mode of sensing signal type change and / or model ID.

[0020] In some embodiments of the method according to the first aspect, the method is characterized by, that split sensing is a pair of sensing transmission and sensing reception that are configured separately for available sensing Tx and Rx entities.

[0021] In some embodiments of the method according to the first aspect, the method is characterized by, that combined sensing is a group of sensing transmissions and / or 202403886

[0022] - 4 - sensing receptions that are configured for concatenation of available sensing Tx and / or Rx entities.

[0023] In some embodiments of the method according to the first aspect, the method is characterized by, that sensing signaling type is determined at network side based on feedback message from UE about sensing measurement information.

[0024] In some embodiments of the method according to the first aspect, the method is characterized by, that combined sensing is turned on to increase sensing reliability or sensing data quality for use when split sensing provides weak sensing detection across different links between sensing transmission and reception.

[0025] In some embodiments of the method according to the first aspect, the method is characterized by, that dedicated model ID for split sensing based ML operation with the different model IDs for each pair of available sensing Tx and / or Rx entities.

[0026] In some embodiments of the method according to the first aspect, the method is characterized by, that common model ID for combined sensing based ML operation with the same model ID across available sensing Tx and / or Rx entities.

[0027] In some embodiments of the method according to the first aspect, the method is characterized by, that any single sensing Tx or RX entity is capable of supporting both split sensing and combined sensing by having both dedicated / common model IDs for ML operation.

[0028] In some embodiments of the method according to the first aspect, the method is characterized by, that dedicated model ID is assigned for each pair of sensing Tx and / or Rx entities when there are multiple sensing transmitters and receivers at each entities with ML capability for split sensing.

[0029] In some embodiments of the method according to the first aspect, the method is characterized by, that different model IDs is used for different pair of sensing Tx and / or Rx entities so that the sensing data of each pair is used for their own model ID such as model training, inferencing, updating. 202403886

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[0031] In some embodiments of the method according to the first aspect, the method is characterized by, that the same model ID is applied to those sensing Tx or Rx if applicable when there are two or more sensing Tx or Rx at some entity.

[0032] In some embodiments of the method according to the first aspect, the method is characterized by, that common model ID is assigned across different sensing transmitters paired with a sensing receiver when there are multiple sensing transmitters for a single sensing receiver with ML capability for combined sensing.

[0033] In some embodiments of the method according to the first aspect, the method is characterized by, that the same model ID is applied for sensing Tx and / or Rx entities so that the combined sensing data is used for common model ID such as model training, inferencing, updating.

[0034] In some embodiments of the method according to the first aspect, the method is characterized by, that multiple sensing transmitters with a single sensing receiver or a single sensing transmitter with multiple sensing receivers having the same model ID is configured for support.

[0035] In some embodiments of the method according to the first aspect, the method is characterized by, that sensing signal types and the associated ML model ID information is configured at network side based on UE ML capability and sensing capability feedback message.

[0036] In some embodiments of the method according to the first aspect, the method is characterized by, that any pre-configurable information such as mapping relation between applicable model IDs and sensing signal types is sent via system information or dedicated RRC signaling.

[0037] In some embodiments of the method according to the first aspect, the method is characterized by, that dynamic switching or semi-static switching is applied such that L1 / L2 or RRC signaling is used for indication of specific mode of sensing signal type change and / or model ID during active sensing signal type with model ID in operation. 202403886

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[0039] In some embodiments of the method according to the first aspect, the method is characterized by, that any specific combination of sensing signal type and model ID is applied and active with indication message or triggering after monitoring of sensing signal type and / or model ID information for activation.

[0040] In some embodiments of the method according to the first aspect, the method is characterized by, that either network-based indication signaling or UE autonomous decision is applied for operation switching for switching of sensing signal type and / or model ID.

[0041] In some embodiments of the method according to the first aspect, the method is characterized by, that UE is requested to provide measurement feedback in periodic or non-periodic way so that the related assistance information is obtained on network side for switching or re-configuration of sensing signal types where measurement feedback need to be specified for different sensing and / or ML applications.

[0042] In some embodiments of the method according to the first aspect, the method is characterized by, that information about ML capability including available model IDs and sensing capability such as sensing Tx / Rx with sensory modalities is exchanged between entities.

[0043] In some embodiments of the method according to the first aspect, the method is characterized by, that sensing signal type (e.g., split / combined sensing) and the applicable model ID is determined between entities.

[0044] In some embodiments of the method according to the first aspect, the method is characterized by, that indication message about the determined sensing signal type and model ID is sent to UE for activation where L1 / L2 signaling is used for indication message.

[0045] In some embodiments of the method according to the first aspect, the method is characterized by, that mode switching information is sent to network side by 202403886

[0046] - 7 - providing performance monitoring status and / or specific mode priority of sensing signal type / model ID (e.g., if the activated sensing signal type and / or model ID need to be changed).

[0047] According to a second aspect, the present disclosure relates to a apparatus for , 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 24

[0048] According to a third aspect, the present disclosure relates to a an user equipment (UE) comprising an apparatus according to the second aspect.

[0049] According to a fourth aspect, the present disclosure relates to a base station (gNB) comprising an apparatus according to the second aspect.

[0050] According to a fifth aspect, the present disclosure relates to a wireless communication system for collaborative sensing based model operation by configuring sensing signaling types for split sensing and combined sensing, wherein the wireless communication systems comprises user equipment according to a third aspect, gNB According to a fourth aspect, whereby the user Equipment and the gNB each comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the first aspect.

[0051] BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is an exemplary block diagram of assigning dedicated model ID for split sensing. 202403886

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[0054] Figure 2 is an exemplary block diagram of assigning common model ID for combined sensing.

[0055] Figure 3 is an exemplary flow chart of configuring sensing signal types with the associated model IDs at network side.

[0056] Figure 4 is an exemplary flow chart of performing ML operation with sensing signal type at UE side.

[0057] Figure 5 is an exemplary signaling flow of activating sensing signal type and model ID.

[0058] DETAILED DESCRIPTION

[0059] 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.

[0060] 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.

[0061] 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 202403886

[0062] - 9 - 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.

[0063] 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.

[0064] 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 202403886

[0065] - 10 - equipment (LME), USB dongles, UE category Ml, UE category M2, ProSe UE, V2V UE, V2X UE, etc.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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 202403886

[0070] - 11 -

[0071] 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.

[0072] 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.

[0073] 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”)).

[0074] Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description, 202403886

[0075] - 12 - 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.

[0076] 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 202403886

[0077] - 13 -

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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 202403886

[0083] - 14 - 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.

[0084] 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.

[0085] 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.

[0086] AI / ML Model is a data driven algorithm that applies AI / ML techniques to generate set of outputs based on set of inputs.

[0087] AI / ML model delivery is a generic term referring to delivery of an AI / ML model from one entity to another entity in any manner. Note is An entity could mean network node / function (e.g., gNB, LMF, etc.), UE, proprietary server, etc.

[0088] AI / ML model Inference is a process of using trained AI / ML model to produce set of outputs based on set of inputs.

[0089] AI / ML model testing is a subprocess of training, to evaluate the performance of final AI / ML model using dataset different from one used for model training and validation. 202403886

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[0091] Differently from AI / ML model validation, testing does not assume subsequent tuning of the model.

[0092] AI / ML model training is a process to train an AI / ML Model [by learning the input / output relationship] in data driven manner and obtain the trained AI / ML Model for inference.

[0093] AI / ML model transfer is a delivery of an AI / ML model over the air interface in manner that is not transparent to 3GPP signalling, either parameters of model structure known at the receiving end or new model with parameters. Delivery may contain full model or partial model.

[0094] AI / ML model validation is a subprocess of training, to evaluate the quality of an AI / ML model using dataset different from one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training.

[0095] Data collection is a process of collecting data by the network nodes, management entity, or UE for the purpose of AI / ML model training, data analytics and inference.

[0096] Federated learning I federated training is a machine learning technique that trains an AI / ML model across multiple decentralized edge nodes e.g., UEs, gNBs each performing local model training using local data samples. The technique requires multiple interactions of the model, but no exchange of local data samples.

[0097] Functionality identification is a process / method of identifying an AI / ML functionality for the common understanding between the NW and the UE. Note is Information regarding the AI / ML functionality may be shared during functionality identification. Where AI / ML functionality resides depends on the specific use cases and sub use cases.

[0098] Model activation means enable an AI / ML model for specific AI / ML-enabled feature. 202403886

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[0100] Model deactivation means disable an AI / ML model for specific AI / ML-enabled feature.

[0101] Model download means Model transfer from the network to UE.

[0102] Model identification is A process / method of identifying an AI / ML model for the common understanding between the NW and the UE. The process / method of model identification may or may not be applicable and regarding the AI / ML model may be shared during model identification.

[0103] Model monitoring is A procedure that monitors the inference performance of the AI / ML model.

[0104] Model parameter update is Process of updating the model parameters of model. Model selection is the process of selecting an AI / ML model for activation among multiple models for the same AI / ML enabled feature. Model selection may or may not be carried out simultaneously with model activation.

[0105] Model switching is deactivating currently active AI / ML model and activating different AI / ML model for specific AI / ML-enabled feature.

[0106] Model update is process of updating the model parameters and / or model structure of model.

[0107] Model upload is Model transfer from UE to the network.

[0108] Network-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the network.

[0109] Off line field data is the data collected from field and used for offline training of the AI / ML model. 202403886

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[0111] Offline training is an AI / ML training process where the model is trained based on collected dataset, and where the trained model is later used or delivered for inference. Note is This definition only serves as guidance. There may be cases that may not exactly conform to this definition but could still be categorized as offline training by commonly accepted conventions.

[0112] Online field data is the data collected from field and used for online training of the AI / ML model.

[0113] Online training is an AI / ML training process where the model being used for inference) is (typically continuously) trained in (near) real-time with the arrival of new training samples. Note is the notion of (near) real-time vs. non real-time is context- dependent and is relative to the inference time-scale. This definition only serves as guidance.

[0114] There may be cases that may not exactly conform to this definition but could still be categorized as online training by commonly accepted conventions. Note is Fine- tuning / re-training may be done via online or offline training. This note could be removed when we define the term fine-tuning.

[0115] Reinforcement Learning (RL) is a process of training an AI / ML model from input (a.k.a. state) and feedback signal (a.k.a. reward) resulting from the model’s output (a.k.a. action) in an environment the model is interacting with.

[0116] Semi-supervised learning is a process of training model with mix of labelled data and unlabelled data.

[0117] Supervised learning is a process of training model from input and its corresponding labels.

[0118] Two-sided (AI / ML) model is a paired AI / ML Model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e, the first part of inference is 202403886

[0119] - 18 - firstly performed by UE and then the remaining part is performed by gNB, or vice versa.

[0120] UE-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the UE.

[0121] Unsupervised learning is a process of training model without labelled data.

[0122] Proprietary-format models is ML models of vendor-Zdevice-specific proprietary format, from 3GPP perspective. They are not mutually recognizable across vendors and hide model design information from other vendors when shared.

[0123] Open-format models is ML models of specified format that are mutually recognizable across vendors and allow interoperability, from 3GPP perspective. They are mutually recognizable between vendors and do not hide model design information from other vendors when shared.

[0124] 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.

[0125] 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.

[0126] 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. 202403886

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[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 202403886

[0132] - 20 - 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.

[0133] 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.

[0134] 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.

[0135] 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 202403886

[0136] - 21 - 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.

[0137] 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.

[0138] 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.

[0139] 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. 202403886

[0140] - 22 -

[0141] 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.

[0142] 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

[0143] 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.

[0144] 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. 202403886

[0145] - 23 -

[0146] 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”)).

[0147] 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 202403886

[0148] - 24 - 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.

[0149] 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

[0150] 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.

[0151] 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.

[0152] 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 202403886

[0153] - 25 - module, segment, or portion of code, which includes one or more executable instructions of the code for implementing the specified logical function(s).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 202403886

[0158] - 26 - disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the present disclosure.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The wireless device comprises one or more processors and one or more memories. 202403886

[0164] - 27 -

[0165] 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.

[0166] 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.

[0167] The following explanation will provide the detailed description of the mechanism about pre-configuring and signaling the specific information about model online training by configuring a set of UE behaviors. AI / ML based techniques are currently applied to many different applications and 3GPP also started to work on its technical investigation to apply to multiple use cases based on the observed potential gains. AI / ML lifecycle can be split into several stages such as data collection / pre- processing, model training, model testing / validation, model deployment / update, model monitoring etc., where each stage is equally important to achieve target performance with any specific model(s). In applying AI / ML model for any use case or application, one of the challenging issues is to manage the lifecycle of AI / ML model. It is mainly because the data / model drift occurs during model deployment / inference and it results in performance degradation of AI / ML model. Fundamentally, the dataset statistical changes occur after model is deployed and model inference capability is also impacted with unseen data as input. In a similar aspect, the statistical property of dataset and the relationship between input and output for the trained model can be changed with drift occurrence. In this context, model training or 202403886

[0168] - 28 - 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.

[0169] 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. When there are multiple sensing devices generating data collection information for ML model operation, to coordinate them for different ML models or functionalities is important. Without any coordination, there could be waste of data collection from sensing devices with signaling overhead and / or computing power increase. In this method, sensing signaling types such as split sensing and combined sensing can be configured. Specifically, split sensing is defined as a pair of sensing transmission and sensing reception that are configured separately for available sensing Tx and Rx entities. And combined sensing is defined as a group of sensing transmissions and / or sensing receptions that are configured for concatenation of available sensing Tx and / or Rx entities, respectively. Sensing signaling type can be determined at network side based on feedback message from UE about sensing measurement information.

[0170] For example, when split sensing provides weak sensing detection across different links between sensing transmission and reception, combined sensing can be turned on to increase sensing reliability or sensing data quality for use. Model ID can be applied with configuration such as dedicated model ID for split sensing based ML operation with the different model IDs for each pair of available sensing Tx and / or Rx entities and common model ID for combined sensing based ML operation with the same model ID across available sensing Tx and / or Rx entities. Depending on deployment scenarios, any single sensing Tx or RX entity can be capable of supporting both split sensing and combined sensing by having both 202403886

[0171] - 29 - dedicated / common model IDs for ML operation. When there are multiple sensing transmitters and receivers at each entities with ML capability for split sensing, dedicated model ID can be assigned for each pair of sensing Tx and / or Rx entities.

[0172] In other words, different model IDs are used for different pair of sensing Tx and / or Rx entities so that the sensing data of each pair can be used for their own model ID such as model training, inferencing, updating, etc. However, when there are two or more sensing Tx or Rx at some entity, the same model ID can be applied to those sensing Tx or Rx if applicable. When there are multiple sensing transmitters for a single sensing receiver with ML capability for combined sensing, common model ID can be assigned across different sensing transmitters paired with a sensing receiver. In other words, the same model ID is applied for sensing Tx and / or Rx entities so that the combined sensing data can be used for common model ID such as model training, inferencing, updating, etc.

[0173] There can be multiple sensing transmitters with a single sensing receiver or a single sensing transmitter with multiple sensing receivers having the same model ID. Sensing signal types and the associated ML model ID information can be configured at network side based on UE ML capability and sensing capability feedback message. Any pre-configurable information such as mapping relation between applicable model IDs and sensing signal types can be also sent via system information or dedicated RRC signaling. During active sensing signal type with model ID in operation, dynamic switching or semi-static switching can be applied such that L1 / L2 or RRC signaling can be used for indication of specific mode of sensing signal type change and / or model ID. After monitoring of sensing signal type and / or model ID information for activation, any specific combination of sensing signal type and model ID can be applied and active with indication message or triggering. For switching of sensing signal type and / or model ID, either network-based indication signaling or UE autonomous decision can be applied for operation switching. UE can be requested to provide measurement feedback in periodic or non-periodic way so that the related assistance information can be obtained on network side for switching or reconfiguration of sensing signal types where measurement feedback need to be specified for different sensing and / or ML applications. Both entities can exchange 202403886

[0174] - 30 - information about ML capability including available model IDs and sensing capability such as sensing Tx / Rx with sensory modalities. Depending on any specific use case or deployment scenario given, sensing signal type (e.g., split / combined sensing) and the applicable model ID can be determined between entities. Indication message about the determined sensing signal type and model ID is sent to UE for activation where L1 / L2 signaling can be used for indication message. If the activated sensing signal type and / or model ID need to be changed, mode switching information can be sent to network side by providing performance monitoring status and / or specific mode priority of sensing signal type / model ID.

[0175] In model ID management aspect, each AI / ML operation uses an appropriate, uniquely identifiable model according to the sensing topology. For split sensing, a dedicated model ID is assigned to each unique pair of sensing Tx and Rx entities. This creates a one-to-one mapping, enabling individualized model training, inference, and updating for each Tx / Rx link. When multiple Tx or Rx capability exists within a single entity, the same model ID may be re-used for those entities. The mapping relation between each Tx / Rx pair and its dedicated model ID is established and maintained at the network side and can be communicated to the UE via system information or dedicated signaling. For combined sensing, a common model ID is assigned to a logical group of sensing transmitters and / or receivers that participate in concatenated or aggregated sensing operations. This group shares sensing data for joint ML operations. This common model ID enables collective model training and inference across all combined sensing devices, which is particularly beneficial when sensor fusion or joint decision-making is required. The network determines and maintains the mapping of combined sensing groups and associated common model IDs, distributing this information to UEs and relevant nodes as part of the system configuration procedures. The mapping between applicable model IDs and sensing signaling types (split or combined) is pre-configured or dynamically updated by the network. This information is delivered to UEs via dedicated RRC signaling or system information broadcasts, ensuring each entity operates with the correct model ID for the given sensing mode. 202403886

[0176] - 31 -

[0177] Figure 1 shows an exemplary block diagram of assigning dedicated model ID for split sensing. In this example, when there are multiple sensing transmitters and receivers at each entities with ML capability for split sensing, dedicated model ID can be assigned for each pair of sensing Tx and / or Rx entities. In other words, different model IDs are used for different pair of sensing Tx and / or Rx entities so that the sensing data of each pair can be used for their own model ID such as model training, inferencing, updating, etc. However, when there are two or more sensing Tx or Rx at some entity, the same model ID can be applied to those sensing Tx or Rx if applicable.

[0178] Figure 2 shows an exemplary block diagram of assigning common model ID for combined sensing. In this example, when there are multiple sensing transmitters for a single sensing receiver with ML capability for combined sensing, common model ID can be assigned across different sensing transmitters paired with a sensing receiver. In other words, the same model ID is applied for sensing Tx and / or Rx entities so that the combined sensing data can be used for common model ID such as model training, inferencing, updating, etc. There can be multiple sensing transmitters with a single sensing receiver or a single sensing transmitter with multiple sensing receivers having the same model ID.

[0179] Figure 3 shows an exemplary flow chart of configuring sensing signal types with the associated model IDs at network side. In this example, sensing signal types and the associated ML model ID information can be configured at network side based on UE ML capability and sensing capability feedback message. Any pre-configurable information such as mapping relation between applicable model IDs and sensing signal types can be also sent via system information or dedicated RRC signaling. During active sensing signal type with model ID in operation, dynamic switching or semi-static switching can be applied such that L1 / L2 or RRC signaling can be used for indication of specific mode of sensing signal type change and / or model ID.

[0180] Figure 4 shows an exemplary flow chart of performing ML operation with sensing signal type at UE side. In this example, after monitoring of sensing signal type and / or model ID information for activation, any specific combination of sensing signal type 202403886

[0181] - 32 - and model ID can be applied and active with indication message or triggering. For switching of sensing signal type and / or model ID, either network-based indication signaling or UE autonomous decision can be applied for operation switching. UE can be requested to provide measurement feedback in periodic or non-periodic way so that the related assistance information can be obtained on network side for switching or re-configuration of sensing signal types where measurement feedback need to be specified for different sensing and / or ML applications.

[0182] Figure 5 shows an exemplary signaling flow of activating sensing signal type and model ID. In this example, both entities can exchange information about ML capability including available model IDs and sensing capability such as sensing Tx / Rx with sensory modalities. Depending on any specific use case or deployment scenario given, sensing signal type (e.g., split / combined sensing) and the applicable model ID can be determined between entities. Indication message about the determined sensing signal type and model ID is sent to UE for activation where L1 / L2 signaling can be used for indication message. If the activated sensing signal type and / or model ID need to be changed, mode switching information can be sent to network side by providing performance monitoring status and / or specific mode priority of sensing signal type / model ID.

Claims

202403886- 33 -CLAIMS1 . A method of collaborative sensing based model operation by configuring sensing signaling types for split sensing and combined sensing in wireless communication, comprising:• Applying either dedicated model ID or common model ID in association with different sensing signaling types;• Presetting mapping relation between applicable model IDs and sensing signal types;• Transmitting indication of specific mode of sensing signal type change and / or model ID.

2. The method according to previous claim 1 , wherein split sensing is a pair of sensing transmission and sensing reception that are configured separately for available sensing Tx and Rx entities.

3. The method according to one of the previous claims, wherein combined sensing is a group of sensing transmissions and / or sensing receptions that are configured for concatenation of available sensing Tx and / or Rx entities.

4. The method according to one of the previous claims, wherein sensing signaling type is determined at network side based on feedback message from UE about sensing measurement information.

5. The method according to one of the previous claims, wherein combined sensing is turned on to increase sensing reliability or sensing data quality for use when split sensing provides weak sensing detection across different links between sensing transmission and reception.

6. The method according to one of the previous claims, wherein dedicated model ID for split sensing based ML operation with the different model IDs for each pair of available sensing Tx and / or Rx entities.202403886- 34 -7. The method according to one of the previous claims, wherein common model ID for combined sensing based ML operation with the same model ID across available sensing Tx and / or Rx entities.

8. The method according to one of the previous claims, wherein any single sensing Tx or RX entity is capable of supporting both split sensing and combined sensing by having both dedicated / common model IDs for ML operation.

9. The method according to one of the previous claims, wherein dedicated model ID is assigned for each pair of sensing Tx and / or Rx entities when there are multiple sensing transmitters and receivers at each entities with ML capability for split sensing.

10. The method according to one of the previous claims, wherein different model IDs is used for different pair of sensing Tx and / or Rx entities so that the sensing data of each pair is used for their own model ID such as model training, inferencing, updating.11 . The method according to one of the previous claims, wherein the same model ID is applied to those sensing Tx or Rx if applicable when there are two or more sensing Tx or Rx at some entity.

12. The method according to one of the previous claims, wherein common model ID is assigned across different sensing transmitters paired with a sensing receiver when there are multiple sensing transmitters for a single sensing receiver with ML capability for combined sensing.

13. The method according to one of the previous claims, wherein the same model ID is applied for sensing Tx and / or Rx entities so that the combined sensing data is used for common model ID such as model training, inferencing, updating.202403886- 35 -14. The method according to one of the previous claims, wherein multiple sensing transmitters with a single sensing receiver or a single sensing transmitter with multiple sensing receivers having the same model ID is configured for support.

15. The method according to one of the previous claims, wherein sensing signal types and the associated ML model ID information is configured at network side based on UE ML capability and sensing capability feedback message.

16. The method according to one of the previous claims, wherein any pre- configurable information such as mapping relation between applicable model IDs and sensing signal types is sent via system information or dedicated RRC signaling.

17. The method according to one of the previous claims, wherein dynamic switching or semi-static switching is applied such that L1 / L2 or RRC signaling is used for indication of specific mode of sensing signal type change and / or model ID during active sensing signal type with model ID in operation.

18. The method according to one of the previous claims, wherein any specific combination of sensing signal type and model ID is applied and active with indication message or triggering after monitoring of sensing signal type and / or model ID information for activation.

19. The method according to one of the previous claims, wherein either networkbased indication signaling or UE autonomous decision is applied for operation switching for switching of sensing signal type and / or model ID.

20. The method according to one of the previous claims, wherein UE is requested to provide measurement feedback in periodic or non-periodic way so that the related assistance information is obtained on network side for switching or reconfiguration of sensing signal types where measurement feedback need to be specified for different sensing and / or ML applications.202403886- 36 -21. The method according to one of the previous claims, wherein information about ML capability including available model IDs and sensing capability such as sensing Tx / Rx with sensory modalities is exchanged between entities.

22. The method according to one of the previous claims, wherein sensing signal type (e.g., split / combined sensing) and the applicable model ID is determined between entities.

23. The method according to one of the previous claims, wherein indication message about the determined sensing signal type and model ID is sent to UE for activation where L1 / L2 signaling is used for indication message.

24. The method according to one of the previous claims, wherein mode switching information is sent to network side by providing performance monitoring status and / or specific mode priority of sensing signal type / model ID (e.g., if the activated sensing signal type and / or model ID need to be changed).

25. Apparatus for, 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 24.

26. User Equipment (UE) comprising an apparatus according to claim 25.

27. Base station (gNB) comprising an apparatus according to claim 25.

28. Wireless communication system, wherein the wireless communication systems comprises user equipment according to claim 17, gNB according to claim 18, 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 24.

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