WO2026032812A1 - Procédé de segmentation d'identification de modèle - Google Patents

Procédé de segmentation d'identification de modèle

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Publication number
WO2026032812A1
WO2026032812A1 PCT/EP2025/071896 EP2025071896W WO2026032812A1 WO 2026032812 A1 WO2026032812 A1 WO 2026032812A1 EP 2025071896 W EP2025071896 W EP 2025071896W WO 2026032812 A1 WO2026032812 A1 WO 2026032812A1
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model
ids
sub
previous
data
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Inventor
Hojin Kim
Andreas Andrae
Rikin SHAH
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Aumovio Germany GmbH
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Aumovio Germany GmbH
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • the present disclosure relates to AI/ML based model operation with version data based model identification signaling, where techniques for pre-configuring and signaling the specific information about model identification with version data applicable to radio access network are presented.
  • AI/ML artificial intelligence/machine learning
  • RP-213599 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.
  • 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.
  • AI/ML model terminology and description to identify common and specific characteristics for framework are included as one of key work scopes.
  • 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.
  • 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.
  • joint inference comprises AI/ML Inference whose inference is performed jointly across the UE and the network.
  • UE-side (AI/ML) model is defined as an AI/ML model 202404608
  • AI/ML network-side
  • 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.
  • LCM lifecycle management
  • 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.
  • 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.
  • US 2022400373 describes the method of determining neural network functions and configuring models for performing wireless communications management procedures.
  • 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 202404608
  • WO 2023277780 contains a method of downloading of a compiled machine code version of a ML model to a wireless communication device.
  • 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.
  • WO 2022161624 describes the method of receiving a request for retrieving or executing a ML model or a combination of ML models.
  • the present disclosure solves the cited problem above by the proposed embodiments and describes methods of segmentation of model identification by using the pre-configured AI/ML (artificial intelligence/machine learning) based model identification with version data in wireless mobile communication system including base station e.g., gNB, TN, NTN and mobile station e.g., UE.
  • AI/ML model is applied to radio access network, signaling overhead can be significantly increased for model transfer without specific model identification. Therefore, model operation e.g., model training/inferencing/monitoring/updating can be set up between network and UE by using version data based model identification information.
  • the method of segmentation of model identification by configuring model ID data format in a wireless communication system comprising the steps setting a set of model sub-IDs with the associated version data information; generating a list of model sub-IDs having mapping relationship with the associated version values; indicating the specific representative model ID related to a set of model sub-IDs.
  • the method is characterized by that each model sub-IDs indicates the associated identifier(s) 202404608
  • the method is characterized by that ML models with model sub-IDs is updated with different version values and the associated ML conditions.
  • the method is characterized by that the configured model ID data format information is sent to UE so that any matched on-device model is identified for activation for specific model ID to be enabled.
  • the method is characterized by that UE can send the supported model ID data format information to network side so that network side can determine any matched on-device model for activation.
  • the method is characterized by that one or more number of specific model IDs with the configured data format information is sent via system information or dedicated RRC signaling for unicast, multicast or broadcast messaging.
  • the method is characterized by that any updates about model ID data format is indicated via L1/L2 or RRC signaling if applicable.
  • the method is characterized by that the associated model sub-IDs is detected to identify matching version data between target model ID and the available model ID (e.g., when any specific model ID is identified).
  • the method is characterized by that model sub-ID(s) with mismatched version data due to version 202404608
  • 5 values is further updated or transferred for matching condition with the matched model sub-ID(s) and the associated identifiers.
  • the method is characterized by that there is multiple different mapping relation tables for use depending on deployment scenarios or applications.
  • the method is characterized by that the size (N) of model sub-IDs can vary according to different model representative IDs for use.
  • the method is characterized by that the associated version data information for each model sub-IDs is used to support partial model delivery/transfer and/or model updating/re-training.
  • Apparatus for 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
  • the present disclosure relates to an user equipment comprising an apparatus according second aspect.
  • the present disclosure relates to a gNB comprising an apparatus according second aspect.
  • the present disclosure relates to a wireless communication system for segmentation of model identification by using the pre-configured AI/ML, 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. 202404608
  • Figure 1 is an exemplary mapping relation table of model sub-IDs and version data.
  • Figure 2 is an exemplary data format of model ID.
  • Figure 3 is an exemplary flow chart of setting the associated model for activation.
  • Figure 4 is an exemplary flow chart of identifying available model with matching version.
  • 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.
  • 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.
  • MSC Mobile Switching Center
  • MME Mobility Management Entity
  • O&M Operations & Maintenance
  • OSS Operations Support System
  • SON Self Optimized Network
  • positioning node e.g. Evolved- Serving Mobile Location Centre (E-SMLC)
  • E-SMLC Evolved- Serving Mobile Location Centre
  • MDT Minimization of Drive Tests
  • test equipment physical node or software
  • 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.
  • UE user equipment
  • 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 202404608
  • LME 8 equipment
  • USB dongles UE category Ml
  • UE category M2 ProSe UE
  • V2V UE V2X UE
  • 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.
  • gNB gNodeB
  • 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.
  • 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.
  • VLSI very-large-scale integration
  • 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.
  • 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.
  • 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 202404608
  • 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.
  • a storage device 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.
  • 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.
  • 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.
  • 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”)).
  • LAN local area network
  • WLAN wireless LAN
  • WAN wide area network
  • ISP Internet Service Provider
  • 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.
  • 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.
  • 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).
  • 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.
  • AI/ML Model is a data driven algorithm that applies AI/ML techniques to generate set of outputs based on set of inputs.
  • 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.
  • An entity could mean network node/function (e.g., gNB, LMF, etc.), UE, proprietary server, etc.
  • AI/ML model Inference is a process of using trained AI/ML model to produce set of outputs based on set of inputs.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Model activation means enable an AI/ML model for specific AI/ML-enabled feature. 202404608
  • Model deactivation means disable an AI/ML model for specific AI/ML-enabled feature.
  • Model download means Model transfer from the network to UE.
  • 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.
  • Model monitoring is A procedure that monitors the inference performance of the AI/ML model.
  • 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.
  • Model switching is deactivating currently active AI/ML model and activating different AI/ML model for specific AI/ML-enabled feature.
  • Model update is process of updating the model parameters and/or model structure of model.
  • Model upload is Model transfer from UE to the network.
  • AI/ML Network-side
  • Offline field data is the data collected from field and used for offline training of the AI/ML model. 202404608
  • 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.
  • Online field data is the data collected from field and used for online training of the AI/ML model.
  • 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.
  • Fine-tuning/re-training may be done via online or offline training. This note could be removed when we define the term fine-tuning.
  • Reinforcement Learning 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.
  • Semi-supervised learning is a process of training model with mix of labelled data and unlabelled data.
  • Supervised learning is a process of training model from input and its corresponding labels.
  • 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 202404608
  • AI/ML UE-side
  • Unsupervised learning is a process of training model without labelled data.
  • 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.
  • 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.
  • 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.
  • DL downlink
  • CN core network
  • uplink, UL uplink
  • the RAN comprises one base station, BS.
  • 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.
  • 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.
  • 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.
  • LoT Internet of Things
  • the wireless device comprises one or more processors and one or more memories.
  • the one or more processors may include for instance a central processing unit (CPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.
  • the one or more memories may include any type of computer readable volatile and non-volatile memories (magnetic hard disk, solid-state disk, optical disk, electronic memory, etc.).
  • the one or more memories may store a computer program product, in the form of a set of program-code instructions to be executed by the one or more processors to implement all or part of the steps of a method for exchanging data, performed at a UE’s side, according to any one of the embodiments disclosed herein.
  • the wireless device can comprise also a main radio, MR, unit.
  • the MR unit corresponds to a main wireless communication unit of the wireless device, used for exchanging data with BSs of the RAN using radio signals.
  • the MR unit may implement one or more wireless communication protocols, and may for instance be a 3G, 4G, 5G, NR, WiFi, WiMax, etc. transceiver or the like.
  • the MR unit corresponds to a 5G NR wireless communication unit. 202404608
  • network node may be used and may correspond to any type of radio network node or any network node, which 202404608
  • 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.
  • MSC Mobile Switching Center
  • MME Mobility Management Entity
  • O&M Operations & Maintenance
  • OSS Operations Support System
  • SON Self Optimized Network
  • positioning node e.g. Evolved- Serving Mobile Location Centre (E-SMLC)
  • E-SMLC Evolved- Serving Mobile Location Centre
  • MDT Minimization of Drive Tests
  • test equipment physical node or software
  • 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.
  • 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.
  • 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.
  • gNB gNodeB
  • 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 202404608
  • 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.
  • VLSI very-large-scale integration
  • 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.
  • 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.
  • 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
  • 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.
  • the storage device 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 202404608
  • 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.
  • 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.
  • 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”)).
  • LAN local area network
  • WLAN wireless LAN
  • WAN wide area network
  • ISP Internet Service Provider
  • 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.
  • 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. 202404608
  • 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).
  • 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).
  • 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.
  • model training or re-training is one of key issues for model performance maintenance as model performance such as inferencing and/or training is dependent on different model execution environment with varying configuration parameters.
  • 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 202404608
  • 25 update feedback is then provided to re-train/update the model or select alternative model.
  • model-ID based LCM 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.
  • model-ID based LCM any matched model ID(s) need to be valid between entities (e.g., NW-UE, UE-UE). If the requested model ID is not available, model itself need to be transferred and hence the related signaling overhead can heavily increase depending on model type/structure.
  • model ID data format can be configured as a set of model sub-IDs with the associated version data information where any specific representative model ID is segmented into multiple model sub-IDs. However, it does not always mean that model structure is segmented but identification information itself.
  • Each model sub-IDs indicates the associated identifier(s) related to ML conditions such as dataset, site, functionality, application, collaboration level, etc. (e.g., one-to-one mapping, one-to-many mapping).
  • ML models with model sub-IDs can be updated with different version values and the associated ML conditions.
  • the configured model ID data format information can be sent to UE so that any matched on-device model can be identified for activation. Or UE can send the supported model ID data format information to network side so that network side can determine any matched on-device model for activation.
  • One or more number of specific model IDs with the configured data format information can be sent via system information or dedicated RRC signaling for unicast, multicast or broadcast messaging. Any updates about model ID data format can be indicated via L1/L2 or RRC signaling if applicable.
  • the associated model sub-IDs are also detected to identify matching versions between target model ID and the available model ID. In case that there is any mismatched version(s) with the associated model sub-ID(s) due to version values, those model sub-ID(s) can be further updated or transferred for matching condition so that they can be replaced with the matched 202404608
  • Model ID is configured to contain a list of model sub-IDs that have mapping relationship with the associated version values. There can be multiple different mapping relation tables for use depending on deployment scenarios or applications.
  • model ID is used to indicate the specific representative model ID that comprises a set of model sub-IDs.
  • the size (N) of model sub-IDs can vary according to different model representative IDs for use.
  • Data size of model ID data format is implementation-specific.
  • the associated version data information for each model sub-IDs can be used to support partial model delivery/transfer and/or model updating/re-training.
  • the exemplary field information of model ID data format includes at least one or more of representative model ID (e.g., encodes the high-level model family), dataset sub-ID (e.g., identifies the dataset used), site sub-ID (e.g., indicates deployment site or region), functionality sub-ID (e.g., specifies functional capability, collaboration-level sub-ID (e.g., denotes model operation type between nodes), version value (e.g., version number for the sub-IDs).
  • representative model ID e.g., encodes the high-level model family
  • dataset sub-ID e.g., identifies the dataset used
  • site sub-ID e.g., indicates deployment site or region
  • functionality sub-ID e.g., specifies functional capability
  • collaboration-level sub-ID e.g., denotes model operation type between nodes
  • version value e.g., version number for the sub-IDs.
  • Figure 1 shows an exemplary mapping relation table of model sub-IDs and version data.
  • model ID is configured to contain a list of model sub-IDs that have mapping relationship with the associated version values.
  • Each model sub-IDs indicates the associated identifiers related to ML conditions such as dataset, site, functionality, application, collaboration level, etc. (e.g., one-to-one mapping, one-to-many mapping).
  • model sub-IDs can be updated with different version values.
  • Model ID data format can be configured to contain a set of model sub-IDs with the associated version data information.
  • model ID is used to indicate the specific representative model ID that comprises a set of model sub-IDs.
  • the size (N) of model sub-IDs can vary according to different model representative IDs for use. 202404608
  • Model ID data format Data size of model ID data format is implementation-specific.
  • the associated version data information for each model sub-IDs can be used to support partial model delivery/transfer and/or model updating/re-training.
  • Model ID data format can be configured to contain a set of model sub-IDs with the associated version data information.
  • the configured model ID data format information can be sent to UE so that any matched on-device model can be identified for activation.
  • UE can send the supported model ID data format information to network side so that network side can determine any matched on-device model for activation.
  • Figure 4 shows an exemplary flow chart of identifying available model with matching version.
  • one or more number of specific model IDs with the configured data format information can be sent via system information or dedicated RRC signaling for unicast, multicast or broadcast messaging.
  • the associated model sub-IDs are also detected to identify matching versions between target model ID and the available model ID.
  • those model sub-ID(s) can be further updated or transferred for matching condition so that they can be replaced with the matched model sub-ID(s) and the associated identifiers.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente divulgation concerne des procédés de segmentation d'identification de modèle au moyen d'une identification de modèle basée sur l'IA/ML (intelligence artificielle/apprentissage automatique) préconfigurée et associée à des données de version, dans un système de communication mobile sans fil comprenant une station de base (par exemple, gNB, TN, NTN) et une station mobile (par exemple, UE). Lorsque le modèle IA/ML est appliqué au réseau d'accès radio, le surdébit de signalisation est significativement augmenté lors du transfert de modèle en l'absence d'une identification spécifique du modèle. Par conséquent, une opération sur un modèle (par exemple, l'entraînement/l'inférence/la surveillance/la mise à jour d'un modèle) est établie entre le réseau et l'UE en utilisant des informations d'identification de modèle basées sur les données de version.
PCT/EP2025/071896 2024-08-07 2025-07-30 Procédé de segmentation d'identification de modèle Pending WO2026032812A1 (fr)

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