WO2025168468A1 - Method of advanced ml signaling for ran - Google Patents

Abstract

The present disclosure describes methods of using the pre-configured AI/ML (artificial intelligence/machine learning) based model condition segmentation in wireless mobile communication system including base station (e.g., gNB) and mobile station (e.g., UE). In AI/ML model is applied to radio access network, signaling of model condition information exchange is heavily congested. Therefore, model operation (e.g., model re-training/updating) is set up between network and UE by configuring model condition segmentation.

Description

TITLE

Method of advanced ML signaling for RAN

TECHNNICAL FIELD

The present disclosure relates to AI/ML based model condition segmentation, where techniques for pre-configuring and signaling the specific information about segmenting model condition applicable to radio access network are presented.

BACKGROUND

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. 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 networkside and UE-side models for various lifecycle management (LCM) operations.

However, signaling overhead indicating model information can be very high especially when model based LCM is processed between base station (BS/gNB) and multiple UEs. In LCM, model training is one of the most important parts for model deployment and currently there is no specification defined for signaling methods and network-UE behaviors so as to identify the required dataset when model updating/re- training as any activated model can be also impacted due to model/data drift.

When ML condition changes, the enabled AI/ML model(s) can be impacted for model performance due to data/model drift. In this case, model re-training/updating can be executed. For example, when the trained ML model is deployed in RAN, model performance for inferencing can be easily degraded if target ML condition is not well aligned with real ML condition measured for specific model operation.

WO2021244730A1 describes a method for configuring metrics to be monitored in the communication network with a plurality of possible metrics to be monitored.

US2022101204A1 describes a reporting configuration that indicates one or more reporting conditions, where the client device is to report an update associated with a machine learning component.

US2022116764A1 describes a method of UE reports for a machine learning processing capability with gradient updates or weight updates to the machine learning model.

US2022330012A1 describes methods ML capability of the UE for the ML procedure. The present disclosure solves the cited problem by the proposed embodiments and describes a method of advanced ML signaling for RAN in a wireless communication system, comprising segmenting ML condition into two categories such as common ML condition and dedicated ML condition, supporting the grouped models and/or UE group with groupwise ML condition.

In some embodiments of the method according to the first aspect, the method is characterized by, that that

In some embodiments of the method according to the first aspect, the method is characterized by, that the pre-configured common and dedicated ML conditions is used to UE having the known model, which is a open-format and/or standardized and the unknown model, which is proprietary and/or non-standardized.

In some embodiments of the method according to the first aspect, the method is characterized by, that the pre-configured common and dedicated ML conditions is also defined to indicate condition parameter sets used to all applicable and/or /candidate models and each specific model for ML operation.

In some embodiments of the method according to the first aspect, the method is characterized by, that common ML condition is autonomously measured at UE before any model selection process between network side and UE side.

In some embodiments of the method according to the first aspect, the method is characterized by, that part of dedicated ML condition is measured selectively based on the specific ML application/model/configuration information.

In some embodiments of the method according to the first aspect, the method is characterized by, that the pre-configured ML condition segmentation information is sent via system information or dedicated RRC signaling. In some embodiments of the method according to the first aspect, the method is characterized by, that the indication message of the specific segmented ML condition information can also be sent via L1/L2 or RRC signaling.

In some embodiments of the method according to the first aspect, the method is characterized by, that 1 -bit indication is pre-configured for use to inform whether common or dedicated ML condition is applied.

In some embodiments of the method according to the first aspect, the method is characterized by, that common parameter sets and dedicated parameter sets or subsegmented into smaller sets are associated with common and dedicated ML conditions.

In some embodiments of the method according to the first aspect, the method is characterized by, that common ML condition is considered as preliminary condition for dedicated ML condition measurement.

In some embodiments of the method according to the first aspect, the method is characterized by, that only common ML condition measurement is needed based on ML application/scenario and configuration for UE(s).

According to a second aspect, the present disclosure relates to an apparatus for advanced ML signaling for RAN 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.

According to a second aspect, the present disclosure relates to an user Equipment comprising an apparatus according to any one of the embodiments of the second aspect.

According to a third aspect, the present disclosure relates to a gNB comprising an apparatus according to any one of the embodiments of the second aspect. According to a fourth aspect, the present disclosure relates to a wireless communication system for RRC state-based online training signaling, wherein the wireless communication systems comprises user equipment according to claim 14, gNB according to claim 15, 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 according to the first aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is an exemplary table of ML condition categories.

Figure 2 is an exemplary block diagram of segmenting common and dedicated ML conditions.

Figure 3 is an exemplary block diagram of mapping relation between ML condition categories and UE model types.

Figure 4 is an exemplary flow chart of UE behavior for the segmented ML condition measurements.

Figure 5 is an exemplary block diagram of transmitting ML condition categories to different UEs.

Figure 6 is an exemplary flow chart of configuring ML condition segmentation at network side.

Figure 7 is an exemplary flow chart of measuring the indicated ML condition category at UE side. DETAILED DESCRIPTION

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

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.

Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description. 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.

In some embodiments, the non-limiting term user equipment (UE) or wireless device may be used and may refer to any type of wireless device communicating with a network node and/or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communication, PDA, PAD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, UE category Ml, UE category M2, ProSe UE, V2V UE, V2X UE, etc.

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.

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.

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.

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.

Any combination of one or more computer readable medium may be utilized. The computer readable medium may be a computer readable storage medium. The computer readable storage medium may be a storage device storing the code. The storage device may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.

More specific examples (a non-exhaustive list) of the storage device would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random-access memory (“RAM”), a read-only memory (“ROM”), an erasable programmable read-only memory (“EPROM” or Flash memory), a portable compact disc readonly memory (“CD-ROM”), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

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”)).

Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments. One skilled in the relevant art will recognize, however, that embodiments may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of an embodiment. Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment, but mean “one or more but not all embodiments” unless expressly specified otherwise. The terms “including,” “comprising,” “having,” and variations thereof mean “including but not limited to,” unless expressly specified otherwise. An enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms “a,” “an,” and “the” also refer to “one or more” unless expressly specified otherwise.

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 functions/acts specified in the flowchart diagrams and/or block diagrams.

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. 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).

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.

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

The description of elements in each figure may refer to elements of proceeding figures. Like numbers refer to like elements in all figures, including alternate embodiments of like elements.

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.

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.

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.

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.

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.

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.

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.

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. Note is 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. Differently from AI/ML model validation, testing does not assume subsequent tuning of the model.

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 signaling, 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. 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.

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

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

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.

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.

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.

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 firstly performed by UE and then the remaining part is performed by gNB, or vice versa. UE-side (AI/ML) model is an AI/ML Model whose inference is performed entirely at the UE.

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 following explanation will provide the detailed description of the mechanism about pre-configuring and signaling the specific information about model online training by configuring a set of UE behaviors. AI/ML based techniques are currently applied to many different applications and 3GPP also started to work on its technical investigation to apply to multiple use cases based on the observed potential gains. AI/ML lifecycle can be split into several stages such as data collection/pre- processing, model training, model testing/validation, model deployment/update, model monitoring etc., where each stage is equally important to achieve target performance with any specific model(s). In applying AI/ML model for any use case or application, one of the challenging issues is to manage the lifecycle of AI/ML model. It is mainly because the data/model drift occurs during model deployment/inference and it results in performance degradation of AI/ML model. Fundamentally, the dataset statistical changes occur after model is deployed and model inference capability is also impacted with unseen data as input. In a similar aspect, the statistical property of dataset and the relationship between input and output for the trained model can be changed with drift occurrence. In this context, model training or re-training is one of key issues for model performance maintenance as model performance such as inferencing and/or training is dependent on different model execution environment with varying configuration parameters. To handle this issue, collaboration between UE and gNB is highly important to track model performance and re-configure model corresponding to different environments. AI/ML model needs model monitoring after deployment because model performance cannot be maintained continuously due to drift and update feedback is then provided to re- train/update the model or select alternative model.

When AI/ML model enabled wireless communication network is deployed, it is then important to consider how to handle AI/ML model in activation with re-configuration for wireless devices under operations such as model training, inference, updating, etc. In this method, a set of ML condition is segmented into two categories such as common ML condition and dedicated ML condition, where ML condition can have information about ML scenario/application, ML configuration, ML model/functionality, dataset, LCM, environmental status (e.g., site, location), and/or device ML support status. Additionally, groupwise ML condition can be also pre-configured to support the grouped models and/or UE group. Specifically, groupwise ML condition refers to a set of ML conditions that are pre-configured to apply collectively to a group of models or a group of UEs so that efficient management and application of ML functionalities can be performed by addressing common requirements or behaviors across grouped entities. In a wireless network, a groupwise ML condition may define shared environmental parameters, dataset characteristics, or operational configurations for a set of UEs performing federated learning. For example, the preconfigured common and dedicated ML conditions can be used to UE having the known model (e.g., open-format or standardized) and the unknown model (e.g., proprietary or non-standardized), respectively (or vice versa). In other aspect, the pre-configured common and dedicated ML conditions can be also defined to indicate condition parameter sets used to all applicable/candidate models and each specific model for ML operation, respectively. Based on the segmented common and dedicated ML conditions, the associated parameter sets can be configured to indicate the specified parameter information for measurement and model identification. Common ML condition is autonomously measured at UE before any model selection process between network side and UE side and part of dedicated ML condition is then further measured selectively based on the specific ML application/model/configuration information. The pre-configured ML condition segmentation refers to the division of ML conditions into predefined categories, such as common ML conditions and dedicated ML conditions where common ML conditions are universally applicable across all ML models or devices in a system and dedicated ML conditions are specific to individual ML models, applications, or configurations, selectively applied based on the ML scenario or device-specific requirements. For example, a common ML condition may include basic environmental parameters such as network load or location data and a dedicated ML condition could involve specific dataset attributes or model configuration details for a proprietary ML model operating in a unique use case. The pre-configured ML condition segmentation information can be sent via system information or dedicated RRC signaling. The indication message of the specific segmented ML condition information can also be sent via L1/L2 or RRC signaling. In addition, 1 -bit indication can be pre-configured for use to inform whether common or dedicated ML condition is applied. For implementation, either common or dedicated ML condition can be used or the combined (common and dedicated) ML condition can be applied if necessary.

Figure 1 shows an exemplary table of ML condition categories. In this example, ML condition category is associated with parameter sets. The parameter sets can be represented as index or ID information so that the related condition measurement information can be signaled together with the associated ML condition category. There can be multiple number of tables about ML condition category based parameter sets according to varying ML application/scenarios.

Figure 2 shows an exemplary block diagram of segmenting common and dedicated ML conditions. In this example, ML condition is segmented as common and dedicated ML conditions so that common parameter sets and dedicated parameter sets are associated with them, respectively. Those parameter sets can be also subsegmented into smaller sets if necessary.

Figure 3 shows an exemplary block diagram of mapping relation between ML condition categories and UE model types. In this example, the pre-configured common and dedicated ML conditions can be used to UE having the known model (e.g., open-format or standardized) and the unknown model (e.g., proprietary or nonstandardized), respectively (or vice versa). In other words, the known model at UE measures common ML condition to be used for model training/inferencing/monitoring and the unknown model at UE measures dedicated ML condition for any specific LCM operation.

Figure 4 shows an exemplary flow chart of UE behavior for the segmented ML condition measurements. In this example, common ML condition is considered as preliminary condition for dedicated ML condition measurement. In other words, dedicated ML condition cannot be measured if common ML condition is not met (e.g., below threshold value). When common ML condition measurement is above threshold, 2^nd measurement of dedicated ML condition is allowed to proceed. For some UEs, only common ML condition measurement is needed based on ML application/scenario and configuration.

Figure 5 shows an exemplary block diagram of transmitting ML condition categories to different UEs. In this example, UE1 receives common ML condition configuration information when UE1 has the known model type and/or applicable/candidate models. UE2 receives dedicated ML condition configuration information when UE1 has the unknown model type and/or specific model only.

Figure 6 shows an exemplary flow chart of configuring ML condition segmentation at network side. In this example, ML condition segmentation information is configured at network side and transmitted to UE.

Figure 7 shows an exemplary flow chart of measuring the indicated ML condition category at UE side. In this example, the specific segmented ML condition indicated by network side is measured so that it can be reported to network.

Claims

1. A method of advanced ML signaling for RAN in a wireless communication system, comprising:

• Segmenting ML condition into two categories such as common ML condition and dedicated ML condition;

• Supporting the grouped models and/or UE group with groupwise ML condition.

2. The method according to claim 1 , wherein the pre-configured common and dedicated ML conditions is used to UE having the known model, which is a openformat and/or standardized and the unknown model, which is proprietary and/or non-standardized.

3. The method according to one of the previous claims, wherein the pre-configured common and dedicated ML conditions is also defined to indicate condition parameter sets used to all applicable and/or /candidate models and each specific model for ML operation.

4. The method according to one of the previous claims, wherein the associated parameter sets is configured to indicate the specified parameter information for measurement and model identification based on the segmented common and dedicated ML conditions.

5. The method according to one of the previous claims, wherein common ML condition is autonomously measured at UE before any model selection process between network side and UE side.

6. The method according to one of the previous claims, wherein part of dedicated ML condition is measured selectively based on the specific ML application/model/configuration information.

7. The method according to one of the previous claims, wherein the pre-configured ML condition segmentation information is sent via system information or dedicated RRC signaling.

8. The method according to one of the previous claims, wherein the indication message of the specific segmented ML condition information can also be sent via L1/L2 or RRC signaling.

9. The method according to one of the previous claims, wherein 1 -bit indication is pre-configured for use to inform whether common or dedicated ML condition is applied.

10. The method according to one of the previous claims, wherein common parameter sets and dedicated parameter sets or sub-segmented into smaller sets are associated with common and dedicated ML conditions.

11 . The method according to one of the previous claims, wherein common ML condition is considered as preliminary condition for dedicated ML condition measurement.

12. The method according to one of the previous claims, wherein only common ML condition measurement is needed based on ML application/scenario and configuration for UE(s).

13. Apparatus for advanced ML signaling for RAN 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 12.

14. User Equipment comprising an apparatus according to claim 13.

15. gNB comprising an apparatus according to claim 13.

16. Wireless communication system for RRC state-based online training signaling, wherein the wireless communication systems comprises user equipment according to claim 14, gNB according to claim 15, 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 12.