Method of sensing grouping for model signaling

Abstract

The present disclosure describes methods of using sensing data grouping 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 data grouping.

Description

[0001] 202403889

[0002] - 1 -

[0003] TITLE

[0004] Method of sensing grouping for model signaling

[0005] TECHNICAL FIELD

[0006] The present disclosure relates to AI / ML based model operation with sensing data grouping, where techniques for pre-configuring and signaling the specific information about sensing grouping 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) 202403889

[0010] - 2 - model is defined as an AI / ML model whose inference is performed entirely at the network. Currently, AI / ML specification work is at the stage of work item discussion for Release 19. Earlier, in 3GPP TR 37.817 for Release 17, titled as Study on enhancement for Data Collection for NR and EN-DC, UE (user equipment) mobility was also considered as one of AI / ML use cases and one of scenarios for model training / inference is that both functions are located within RAN node. Followingly, in Release 18 the new work item of “Artificial Intelligence (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, 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] US2022374785A1 describes a machine learning system that performs transfer learning to output a trained model by performing training using a parameter of a pretrained model by using a given dataset and a given pre-trained model.

[0013] WO2022232092A1 shows the paging pattern of different UEs for communication between network access node and UE. 202403889

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[0015] WO2022216209A1 studies wireless device having a quasi-co location (QCL) relation parameter.

[0016] US2019296868A1 and US2023079502A1 shows method and device for representing a QCL parameter configuration, acquiring QCL characteristic parameter set including part or all of characteristic parameters.

[0017] The present disclosure solves the cited problem above by the proposed embodiments and describes methods of using sensing data grouping 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 data grouping.

[0018] In the first embodiment of the method for sensing grouping for model signaling by configuring grouping of sensing data generated across multiple sensory devices in a wireless communication system is characterized by, that it is comprising the steps, grouping sensing data sets; categorizing master sensing and assistant sensing via sensing devices; determining identification of group ID and sensing data set ID; configuring mapping relation for model ID and sensing group ID in association with list of sensing data set IDs.

[0019] In some embodiments of the method according to the first aspect, the method is characterized by that, that the, wherein two or more sensing data sets from different sources is grouped together, which could be quasi-co-sensing based grouping, if they have highly similar or same characteristics of sensing data, which are based on sensing data type and / or statistical measure.

[0020] In some embodiments of the method according to the first aspect, the method is characterized by that, that the sensory devices are co-located at the same UE or located across different UEs for sensing data set grouping. 202403889

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[0022] In some embodiments of the method according to the first aspect, the method is characterized by that, that the on-device sensing indicates sensing data collected from sensory device at UE.

[0023] In some embodiments of the method according to the first aspect, the method is characterized by that, that the off-device sensing indicates sensing data collected from sensory device at neighboring UE.

[0024] In some embodiments of the method according to the first aspect, the method is characterized by that, that the sensing information report indicates sensing functionality which are based on sensing signal type and / or mode.

[0025] In some embodiments of the method according to the first aspect, the method is characterized by that, that the group ID of sensing data sets is configured after grouping of sensing data sets across sensory devices.

[0026] In some embodiments of the method according to the first aspect, the method is characterized by that, that the group ID of sensing data sets is configured for selection of sensory devices that can contribute to input data of any indicated ML model ID for use.

[0027] In some embodiments of the method according to the first aspect, the method is characterized by that, that the different set of sensory devices is grouped for different ML model ID.

[0028] In some embodiments of the method according to the first aspect, the method is characterized by that, that the sensory devices is categorized into master sensing device and assistant sensing device when they are grouped together.

[0029] In some embodiments of the method according to the first aspect, the method is characterized by that, that the master sensing device generates mandatory sensing data set. 202403889

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[0031] In some embodiments of the method according to the first aspect, the method is characterized by that, that the assistant sensing device generates optional or supplemental sensing data set.

[0032] In some embodiments of the method according to the first aspect, the method is characterized by that, that the a number of sensing data sets is configured based on the generated data characteristics of each sensory devices after identifying a list of sensory devices at UE.

[0033] In some embodiments of the method according to the first aspect, the method is characterized by that, that the groups of sensing data sets are generated using the configured sensing data sets based on quasi-co-sensing criteria or similarity measure.

[0034] In some embodiments of the method according to the first aspect, the method is characterized by that, that the sensing grouping is formed based on a list of sensing data sets required for input data of any specific model ID for use.

[0035] In some embodiments of the method according to the first aspect, the method is characterized by that, that the each Sens-ID indicates the pre-configured parameter set such as sensing data type, sensing data source and / or sensory device information, etc.

[0036] In some embodiments of the method according to the first aspect, the method is characterized by that, that the Sens_Group-ID is configured dynamically or statically depending on implementation scenarios.

[0037] In some embodiments of the method according to the first aspect, the method is characterized by that, that the based on sensing data characteristics, Sens-ID is specified and pre-configured so that each sensing data set associated with each sensory device is identified with the relevant Sens-ID via either offline or online. 202403889

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[0039] In some embodiments of the method according to the first aspect, the method is characterized by that, that the configuration of identification measure is sent by network side so that UE can identify available sensory devices and their associated sensing data set characteristics.

[0040] In some embodiments of the method according to the first aspect, the method is characterized by that, that the UE can provide on-device sensing data set and / or sensory device information so that network side help identify them for grouping.

[0041] In some embodiments of the method according to the first aspect, the method is characterized by that, that the for group of UEs mapping relation information is sent through multicast message by using different mapping relation information for different UE groups.

[0042] In some embodiments of the method according to the first aspect, the method is characterized by that, that the for sensing group identification network side can provide configuration information about identification measurement of sensory devices and their associated sensing data set characteristics.

[0043] In some embodiments of the method according to the first aspect, the method is characterized by that, that the any specific criteria of grouping sensing data sets or sensory devices is implementation-specific and similarity measure is used for quasi- co-sensing.

[0044] In some embodiments of the method according to the first aspect, the method is characterized by that, that the on network side the available ML model IDs and sensory device types at UE is obtained via UE sensing capability report and / or UE ML capability report.

[0045] In some embodiments of the method according to the first aspect, the method is characterized by that, that the UE can activate applicable sensing group(s) after detection of available sensing data sets and the associated sensory devices. 202403889

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[0047] In some embodiments of the method according to the first aspect, the method is characterized by that, that the for identification of group ID and sensing data set ID, UE can provide ID-based sensing group information based on configuration information sent by network side.

[0048] In some embodiments of the method according to the first aspect, the method is characterized by that, that the with the pre-configured mapping relation information for model ID and sensing group ID available at UE side, UE can activate model ID based ML operation with the associated sensing group(s) autonomously.

[0049] According to a second aspect, the present disclosure relates to an apparatus for sensing grouping for model signaling by configuring grouping of sensing data generated across multiple sensory devices in a wireless communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps according to the first aspect.

[0050] According to a third aspect, the present disclosure relates to an user uquipment comprising an apparatus according to the second aspect.

[0051] According to a fourth aspect, the present disclosure relates to a base station (gNB) comprising an apparatus according to the second aspect.

[0052] According to a fourth aspect, the present disclosure relates to a wireless communication system for sensing grouping for model signaling by configuring grouping of sensing data generated across multiple sensory devices, wherein the wireless communication systems comprises user equipment according to the third aspect, gNB according to the fourth aspect, whereby the user Equipment and the gNB each comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the according to the first aspect.

[0053] BRIEF DESCRIPTION OF THE DRAWINGS 202403889

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[0055] Figure 1 is an exemplary block diagram of quasi-co-sensing based grouping.

[0056] Figure 2 is an exemplary block diagram of identification of sensing groups.

[0057] Figure 3 is an exemplary mapping relation table of model ID and sensing group ID.

[0058] Figure 4 is an exemplary flow chart of configuring quasi-co-sensing based grouping at network side.

[0059] Figure 5 is an exemplary flow chart of activating sensing group at UE side.

[0060] DETAILED DESCRIPTION

[0061] 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.

[0062] 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.

[0063] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given 202403889

[0064] - 9 - 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.

[0065] 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.

[0066] 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 202403889

[0067] - 10 - equipment (LME), USB dongles, UE category Ml, UE category M2, ProSe UE, V2V UE, V2X UE, etc.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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 202403889

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[0073] 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.

[0074] 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.

[0075] 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”)).

[0076] Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description, 202403889

[0077] - 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.

[0078] 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 202403889

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[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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 202403889

[0085] - 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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. 202403889

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[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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. 202403889

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[0096] AI / ML Model is a data driven algorithm that applies AI / ML techniques to generate set of outputs based on set of inputs.

[0097] 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.

[0098] AI / ML model Inference is a process of using trained AI / ML model to produce set of outputs based on set of inputs.

[0099] AI / ML model testing is a subprocess of training, to evaluate the performance of final AI / ML model using dataset different from one used for model training and validation. Differently from AI / ML model validation, testing does not assume subsequent tuning of the model.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 202403889

[0105] - 17 - performing local model training using local data samples. The technique requires multiple interactions of the model, but no exchange of local data samples.

[0106] 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.

[0107] Model activation means enable an AI / ML model for specific AI / ML-enabled feature.

[0108] Model deactivation means disable an AI / ML model for specific AI / ML-enabled feature.

[0109] Model download means Model transfer from the network to UE.

[0110] 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.

[0111] Model monitoring is A procedure that monitors the inference performance of the AI / ML model.

[0112] 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.

[0113] Model switching is deactivating currently active AI / ML model and activating different AI / ML model for specific AI / ML-enabled feature. 202403889

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[0115] Model update is Process of updating the model parameters and / or model structure of model.

[0116] Model upload is Model transfer from UE to the network.

[0117] Network-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the network.

[0118] Offline field data is the data collected from field and used for offline training of the AI / ML model.

[0119] 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.

[0120] Online field data is the data collected from field and used for online training of the AI / ML model.

[0121] 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.

[0122] 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.

[0123] 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. 202403889

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[0125] Semi-supervised learning is a process of training model with mix of labelled data and unlabelled data.

[0126] Supervised learning is a process of training model from input and its corresponding labels.

[0127] Two-sided (AI / ML) model is a paired AI / ML Model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e, the first part of inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa.

[0128] UE-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the UE.

[0129] 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).

[0130] In applying AI / ML model for any use case or application, one of the challenging issues is to manage the lifecycle of AI / ML model. It is mainly because the data / model drift occurs during model deployment / inference and it results in performance degradation of AI / ML model. Fundamentally, the dataset statistical changes occur after model is deployed and model inference capability is also impacted with unseen data as input. In a similar aspect, the statistical property of dataset and the relationship between input and output for the trained model can be changed with drift occurrence. In this context, model training or re-training is one of key issues for 202403889

[0131] - 20 - model performance maintenance as model performance such as inferencing and / or training is dependent on different model execution environment with varying configuration parameters.

[0132] 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. 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.

[0133] Without any coordination, there could be waste of data collection from sensing devices with signaling overhead and / or computing power increase. In this method, grouping of sensing data generated across multiple sensory devices can be configured where two or more sensing data sets from different sources can be grouped together if they have highly similar or same characteristics of sensing data (e.g., based on sensing data type and / or statistical measure). It can be called as “quasi-co-sensing based grouping”.

[0134] For sensing data set grouping, sensory devices can be co-located at the same UE or located across different UEs. On-device sensing indicates sensing data collected from sensory device at UE and off-device sensing indicates sensing data collected from sensory device at neighboring UE. Sensing information report indicates sensing functionality (e.g., sensing signal type, mode). After grouping of sensing data sets across sensory devices, group ID of sensing data sets can be configured.

[0135] Alternatively, group ID of sensing data sets can be configured for selection of sensory devices that can contribute to input data of any indicated ML model ID for use. In this case, different set of sensory devices can be grouped for different ML model ID. Sensory devices can be categorized into master sensing device and 202403889

[0136] - 21 - assistant sensing device when they are grouped together. Master sensing device generates mandatory sensing data set and assistant sensing device generates optional or supplemental sensing data set. After identifying a list of sensory devices at UE, a number of sensing data sets can be configured based on the generated data characteristics of each sensory devices. Groups of sensing data sets can be generated using the configured sensing data sets based on quai-co-sensing criteria or similarity measure. For sensing grouping, it can be also based on a list of sensing data sets required for input data of any specific model ID for use, alternatively. Group ID of sensing data sets (such as Sens_Group-ID#) is configured for each group of sensing data sets (in ID format such as Sens-ID#) that have highly similar or same characteristics of sensing data (e.g., based on data type and / or statistical measure) or for a group of sensory devices (in ID format) if applicable.

[0137] For similarity measures and static / dynamic configurations, the similarity measures may be used (e.g., on how “quasi-co-sensing” grouping can be performed) based on Euclidean distance between normalized feature vectors (e.g., each sensing data set is mapped to an n-dimensional feature vector).

[0138] Each Sens-ID indicates the pre-configured parameter set such as sensing data type, sensing data source and / or sensory device information, etc. Sens_Group-ID can be configured dynamically or statically depending on implementation scenarios. Based on sensing data characteristics, Sens-ID can be specified and pre-configured so that each sensing data set associated with each sensory device can be identified with the relevant Sens-ID via either offline or online. There are two ways of determining identification of group ID and sensing data set ID. Firstly, configuration of identification measure can be sent by network side so that UE can identify available sensory devices and their associated sensing data set characteristics. Secondly, UE provides on-device sensing data set and / or sensory device information so that network side help identify them for grouping. Mapping relation can be configured for model ID and sensing group ID that can be also associated with list of sensing data set IDs. Mapping relation information can be sent via system information or dedicated RRC signaling. Or for any updates about mapping relation information can be also sent via L1 / L2 or L3 signaling. 202403889

[0139] - 22 -

[0140] For dynamic Sens_Group-ID configuration, the gNB sends an RRC Reconfiguration message containing a list of Sens-IDs reported by UE, new Sens_Group-ID set along with confidence thresholds for grouping. The UE applies these dynamic set, overwriting or augmenting any static tables. Subsequent reporting and AI / ML model activation use the updated Sens_Group-IDs until a further RRC update or until session release.

[0141] For group of UEs, mapping relation information can be sent through multicast message by using different mapping relation information for different UE groups. For sensing group identification, network side can provide configuration information about identification measurement of sensory devices and their associated sensing data set characteristics. Any specific criteria of grouping sensing data sets or sensory devices can be implementation-specific and similarity measure can be used for quasi-co- sensing. On network side, the available ML model IDs and sensory device types at UE can be obtained via UE sensing capability report and / or UE ML capability report. UE can activate applicable sensing group(s) after detection of available sensing data sets and the associated sensory devices.

[0142] For identification of group ID and sensing data set ID, UE can provide ID-based sensing group information based on configuration information sent by network side. With the pre-configured mapping relation information for model ID and sensing group ID available at UE side, UE can activate model ID based ML operation with the associated sensing group(s) autonomously.

[0143] Figure 1 shows an exemplary block diagram of quasi-co-sensing based grouping. In this example, after identifying a list of sensory devices at UE, a number of sensing data sets can be configured based on the generated data characteristics of each sensory devices. Groups of sensing data sets can be generated using the configured sensing data sets based on quai-co-sensing criteria or similarity measure. For sensing grouping, it can be also based on a list of sensing data sets required for input data of any specific model ID for use, alternatively. 202403889

[0144] - 23 -

[0145] Figure 2 shows an exemplary block diagram of identification of sensing groups. In this example, group ID of sensing data sets (such as Sens_Group-ID#) is configured for each group of sensing data sets (in ID format such as Sens-ID#) that have highly similar or same characteristics of sensing data (e.g., based on data type and / or statistical measure) or for a group of sensory devices (in ID format) if applicable.

[0146] Each Sens-ID indicates the pre-configured parameter set such as sensing data type, sensing data source and / or sensory device information, etc. Sens_Group-ID can be configured dynamically or statically depending on implementation scenarios. Based on sensing data characteristics, Sens-ID can be specified and pre-configured so that each sensing data set associated with each sensory device can be identified with the relevant Sens-ID via either offline or online. There are two ways of determining identification of group ID and sensing data set ID. In the first way, configuration of identification measure can be sent by network side so that UE can identify available sensory devices and their associated sensing data set characteristics. In the second way, UE provides on-device sensing data set and / or sensory device information so that network side help identify them for grouping.

[0147] Figure 3 shows an exemplary mapping relation table of model ID and sensing group ID. In this example, Mapping relation can be configured for model ID and sensing group ID that can be also associated with list of sensing data set IDs. Mapping relation information can be sent via system information or dedicated RRC signaling. Or for any updates about mapping relation information can be also sent via L1 / L2 or L3 signaling. For group of UEs, mapping relation information can be sent through multicast message by using different mapping relation information for different UE groups.

[0148] Figure 4 shows an exemplary flow chart of configuring quasi-co-sensing based grouping at network side. In this example, for sensing group identification, network side can provide configuration information about identification measurement of sensory devices and their associated sensing data set characteristics. Any specific criteria of grouping sensing data sets or sensory devices can be implementationspecific and similarity measure can be used for quasi-co-sensing. On network side, 202403889

[0149] - 24 - the available ML model IDs and sensory device types at UE can be obtained via UE sensing capability report and / or UE ML capability report.

[0150] Figure 5 shows an exemplary flow chart of activating sensing group at UE side. In this example, UE can activate applicable sensing group(s) after detection of available sensing data sets and the associated sensory devices. For identification of group ID and sensing data set ID, UE can provide ID-based sensing group information based on configuration information sent by network side. With the pre-configured mapping relation information for model ID and sensing group ID available at UE side, UE can activate model ID based ML operation with the associated sensing group(s) autonomously.

Claims

202403889- 25 -CLAIMS1. A method of sensing grouping for model signaling by configuring grouping of sensing data generated across multiple sensory devices in a wireless communication system, comprising:• Grouping sensing data sets;•• Categorizing master sensing and assistant sensing via sensing devices;• Determining identification of group ID and sensing data set ID;• Configuring mapping relation for model ID and sensing group ID in association with list of sensing data set IDs.

2. The method according to previous claim 1 , wherein two or more sensing data sets from different sources is grouped together, which could be quasi-co-sensing based grouping, if they have highly similar or same characteristics of sensing data, which are based on sensing data type and / or statistical measure.

3. The method according to one of the previous claims, wherein sensory devices is co-located at the same UE or located across different UEs for sensing data set grouping.

4. The method according to one of the previous claims, wherein on-device sensing indicates sensing data collected from sensory device at UE.

5. The method according to one of the previous claims, wherein off-device sensing indicates sensing data collected from sensory device at neighboring UE.

6. The method according to one of the previous claims, wherein sensing information report indicates sensing functionality which are based on sensing signal type and / or mode.202403889- 26 -7. The method according to one of the previous claims, wherein group ID of sensing data sets is configured after grouping of sensing data sets across sensory devices.

8. The method according to one of the previous claims, wherein group ID of sensing data sets is configured for selection of sensory devices that can contribute to input data of any indicated ML model ID for use.

9. The method according to one of the previous claims, wherein different set of sensory devices is grouped for different ML model ID.

10. The method according to one of the previous claims, wherein sensory devices is categorized into master sensing device and assistant sensing device when they are grouped together.11 . The method according to one of the previous claims, wherein master sensing device generates mandatory sensing data set.

12. The method according to one of the previous claims, wherein assistant sensing device generates optional or supplemental sensing data set.

13. The method according to one of the previous claims, wherein a number of sensing data sets is configured based on the generated data characteristics of each sensory devices after identifying a list of sensory devices at UE.

14. The method according to one of the previous claims, wherein groups of sensing data sets are generated using the configured sensing data sets based on quasi- co-sensing criteria or similarity measure.

15. The method according to one of the previous claims, wherein sensing grouping is formed based on a list of sensing data sets required for input data of any specific model ID for use.202403889- 27 -16. The method according to one of the previous claims, wherein each Sens-ID indicates the pre-configured parameter set such as sensing data type, sensing data source and / or sensory device information, etc.

17. The method according to one of the previous claims, wherein Sens_Group-ID is configured dynamically or statically depending on implementation scenarios.

18. The method according to one of the previous claims, wherein based on sensing data characteristics, Sens-ID is specified and pre-configured so that each sensing data set associated with each sensory device is identified with the relevant Sens- ID via either offline or online.

19. The method according to one of the previous claims, wherein configuration of identification measure is sent by network side so that UE can identify available sensory devices and their associated sensing data set characteristics.

20. The method according to one of the previous claims, wherein UE can provide on- device sensing data set and / or sensory device information so that network side help identify them for grouping.21 . The method according to one of the previous claims, wherein for group of UEs mapping relation information is sent through multicast message by using different mapping relation information for different UE groups.

22. The method according to one of the previous claims, wherein for sensing group identification network side can provide configuration information about identification measurement of sensory devices and their associated sensing data set characteristics.

23. The method according to one of the previous claims, wherein any specific criteria of grouping sensing data sets or sensory devices is implementation-specific and similarity measure is used for quasi-co-sensing.202403889- 28 -24. The method according to one of the previous claims, wherein on network side the available ML model IDs and sensory device types at UE is obtained via UE sensing capability report and / or UE ML capability report.

25. The method according to one of the previous claims, wherein UE can activate applicable sensing group(s) after detection of available sensing data sets and the associated sensory devices.

26. The method according to one of the previous claims, wherein for identification of group ID and sensing data set ID, UE can provide ID-based sensing group information based on configuration information sent by network side.

27. The method according to one of the previous claims, wherein with the preconfigured mapping relation information for model ID and sensing group ID available at UE side, UE can activate model ID based ML operation with the associated sensing group(s) autonomously.

28. Apparatus for sensing grouping for model signaling by configuring grouping of sensing data generated across multiple sensory devices in a wireless communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 1 to 2729. User Equipment comprising an apparatus according to the claim 28.

30. gNB comprising an apparatus according to the claim 28.

31. Wireless communication system, wherein the wireless communication systems comprises user equipment according to claim 29, gNB according to claim 30, 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 27.

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