Method of model signaling for multi-connectivity

Abstract

Methods of pre-configuring an AI / ML operation when multi-connectivity links are enabled in a wireless mobile communication system including base stations (e.g., gNB) and mobile stations (e.g., UE). When an AI / ML model is applied to a radio access network, model performance such as inferencing and / or training is dependent on different model execution environment with varying configuration parameters. Therefore, by re-configuring any model in operation with multiple connection links, the signaling overhead due to various LCM model operations can be reduced with model performance enhancement.

Description

[0001] TITLE

[0002] Method of model signaling for multi-connectivity

[0003] TECHNNICAL FIELD

[0004] The present disclosure relates to artificial intelligence (Al) / machine learning (ML) operation pre-configuration, where techniques for re-configuring and signaling the specific information to improve the efficient signaling over multiple connectivity links are presented.

[0005] BACKGROUND

[0006] In 3GPP (3rd 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 RAN (Technical Specification Group Radio Access Network) meeting #94e. The official title of the AI / ML study item is “Study on AI / ML for NR Air Interface”, and currently RAN WG1 and WG2 are actively working on a specification. The goal of this study item is to identify a common AI / ML framework and areas of obtaining gains using AI / ML based techniques with use cases.

[0007] According to 3GPP, the main objective of this study item is to study AI / ML frameworks for air-interfaces with target use cases by considering performance, complexity, and potential specification impacts. In particular, AI / ML models, terminology and description to identify common and specific characteristics for a framework will be one of the key work scopes. Regarding AI / ML frameworks, various aspects are under consideration for investigation and one of the key items is about lifecycle management (LCM) of AI / ML models where multiple stages are included as mandatory for model training, model deployment, model inference, model monitoring, model updating etc. Earlier, in 3GPP TR 37.817 for Release 17, titled “Study on enhancement for Data Collection for NR and EN-DC”, UE (user equipment) mobility was also considered as one of the AI / ML use cases. One of the scenarios for model training / inference is that both functions are located within a RAN node. Followingly, in Release 18, the new work item of “Artificial Intelligence (Al) / Machine Learning (ML) for NG-RAN” was initiated to specify data collection enhancements and signaling support within existing NG-RAN interfaces and architecture where mobility optimization is included as one of target use cases.

[0008] For the above active standardization works, UE mobility to support RAN-based AI / ML models can be considered very significant for both gNB (base station or radio access point) and UE to meet any desired model operations (e.g., model training, inference, selection, switching, update, monitoring, etc.) when a UE moves around. Currently, there is no specification defined for signaling methods or gNB-UE behaviors about multi-connectivity (MC) when RAN-based AI / ML model operation proceeds.

[0009] Therefore, it is necessary to investigate any specification impact by considering model operation during MC connection. Any mechanism of additional signaling methods and / or gNB-UE behaviors also need to be addressed to support MC-based model operation between a gNB and a UE so that any potential impact of UE mobility on model operation in RAN should be minimized with service continuity.

[0010] On the other hand, in 3GPP, the terminologies of the working list contain a set of high-level descriptions about AI / ML model training, inference, validation, testing, UE-side model, network-side model, one-sided model, two-sided model, etc. A UE-sided model and network-sided model indicate that an AI / ML model is located for operation in UE and network side, respectively. In the similar context, a one-sided and a two-sided model indicate that an AI / ML model is located in one side and two sides of the network, respectively.

[0011] All signaling aspects to support the above items are currently not specified yet as definitions of terminologies are still under discussion for further modifications. Any potential standards impact with new or enhanced mechanisms of supporting AI / ML models with the above working list items is one of the key areas for investigation in the AI / ML study item.

[0012] US 2023 145 079 A1 discloses a method of wireless communication, by a user equipment (UE), including setting up a secondary cell group (SCG) with a second radio access technology (RAT) that differs from a first RAT associated with a master cell group (MCG). The method also includes communicating wirelessly via the secondary cell group and the master cell group. The method further includes predicting a radio link failure (RLF) for the secondary cell group based on multiple inputs to a machine learning model. The method still further includes routing data transmission from the secondary cell group to the master cell group, after predicting the SCG RLF.

[0013] WO 2022 144 582 A1 discloses a system, method and non-transitory computer readable media for facilitating adaptive anchor layer mobility in a 5G NSA implementation. In one arrangement, a handover modulation criterion involving relevant trigger parametrics of an anchor cell node serving a UE and a target cell node may be compared against a tunable threshold parameter indicative of the effect of the target cell on the anchor cell quality. If the handover modulation criterion does not exceed the tunable threshold value, a quality degradation prediction with respect to the UE may be executed to estimate a likelihood of service failure for the UE. Responsive to determining that the likelihood of service failure does not exceed a probability threshold, handover of the UE to the target cell may be suppressed.

[0014] WO 2021 230 712 A1 relates to dual connectivity configuration that discloses methods and systems for managing a handover, which may include managing throughput loss occurring before / after / during handovers in a dual connectivity configuration. One solution performed before handover includes managing throughput loss by temporarily boosting Buffer Status Report (BSR) of a stack not undergoing handover and transmitting all data packets using the Uplink resource grant received for the boosted BSR. Another solution performed after handover includes determining split ratio before handover itself based on nature and bandwidth information of target cell, and applying it after the handover, to mitigate throughput loss that occurs after handover. Another solution performed during handover is a combination of first solution of boosting BSR and second solution of determining split ratio. Another solution enables prediction of split ratio of potential target cells for an estimated handover using an artificial intelligence model.

[0015] US 2023 053 572 A1 discloses systems, devices, apparatus, and methods, including computer programs encoded on storage media, for an SCG measurement configuration and adding / switching enhancement. A UE may receive information associated with an MCG and training, based on at least one of the information associated with the MCG or historical information of the UE for an SCG, an ML model of UE, of network and interworking in between that indicates whether a location of the UE is within a coverage area of the SCG. The UE may communicate with a base station based on an indication of the ML model. The indication of the ML model may be indicative of whether the UE is within the coverage area of the SCG.

[0016] BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The disclosed invention will be further discussed in the following based on preferred embodiments presented in the attached drawings. However, the disclosed invention may be embodied in many different forms and should not be construed as limited to said preferred embodiments. Rather, said preferred embodiments are provided for thoroughness and completeness, and fully convey the scope of the invention to the skilled person. The following detailed description refers to the attached drawings, in which:

[0018] Figure 1 is an exemplary block diagram of distributed multi-model operation;

[0019] Figure 2 is an exemplary flowchart of a master node behavior for distributed multi-model operation;

[0020] Figure 3 is an exemplary flowchart of a secondary node behavior for distributed multi-model operation; Figure 4 is an exemplary flowchart of a device behavior for distributed multi-model operation;

[0021] Figure 5 is an exemplary signaling flow chart of applying distributed multi-model operation to multiple nodes; and

[0022] Figure 6 is an exemplary signaling flow chart of a setup process for distributed multi-model operation.

[0023] DETAILED DESCRIPTION

[0024] The detailed description set forth below, with reference to the 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.

[0025] 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.

[0026] 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.

[0027] 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), another UE, etc.

[0028] 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 in particular do 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.

[0029] As will be appreciated by one skilled in the art, aspects of the embodiments may be embodied as a system, apparatus, method, or computer program product.

[0030] 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.

[0031] 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.

[0032] Furthermore, embodiments may take the form of a computer 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.

[0033] 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.

[0034] 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 read-only 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.

[0035] 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”)).

[0036] 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.

[0037] Aspects of the embodiments are described below with reference to schematic flowchart diagrams and / or schematic block diagrams of methods, apparatuses, systems, and computer 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 are executed 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.

[0038] 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.

[0039] 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.

[0040] 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).

[0041] 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.

[0042] 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.

[0043] 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.

[0044] The following explanation will provide the detailed description of the mechanism about data-driven AI / ML model signaling for ML model operation in wireless mobile communication systems including base stations (e.g., gNB) and mobile stations (e.g., UE).

[0045] An 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, model switching / selection etc., where each stage is equally important to achieve target performance with any specific model(s). In applying an AI / ML model for any use case or application, one of the challenging issues is to manage the lifecycle of AI / ML model. This is mainly because a data / model drift occurs during model deployment / inference which results in performance degradation of the AI / ML model. Fundamentally, statistical changes of datasets occur after the model is deployed and the model inference capability is impacted while using unseen data as input.

[0046] In a similar aspect, the statistical property of a dataset and the relationship between input and output for the trained model can be changed with drift occurrence. Then, model adaptation is required to support operations such as model switching, re-training, fallback, etc. When an AI / ML model enabled wireless communication network is deployed, it is then important to consider how to handle the adaptation of the AI / ML model under operations such as model training, inference, monitoring, updating, etc. Based on specific network-UE ML collaboration in deployment scenarios (e.g., UE mobility case), ML applicable conditions for LCM (lifecycle management) operations can be significantly changed with different use cases and environmental properties. 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.

[0047] 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 performance such as inferencing and / or training is dependent on different model execution environment with varying configuration parameters.

[0048] To handle this issue, a collaboration between UE and gNB is highly important to track model performance and re-configure models corresponding to different environments between UE and different gNBs. When a AI / ML model enabled wireless communication network is deployed, it is then important to consider how to handle AI / ML models in activation with re-configuration for wireless devices under operations such as model training, inference, updating, etc. In other words, by re-configuring any model in operation with different gNBs such as a master node (MN) and a secondary node (SN) in advance, the performance impact can be minimized compared with when any single connection link is used for model operation.

[0049] In a method disclosed herein, UE connections to multiple gNBs such as MNs and SNs based on a master cell group (MCG) and a secondary cell group (SCG), respectively, are enabled to support distributed multi-model operation that is activated across MNs and / or SNs so that an AI / ML model signaling exchange due to LCM-based model operation can be distributed to one or multiple SNs. Firstly, an ML configuration and a LCM-based model information is transmitted to candidate SN(s) by a MN. Distributed multi-model operation can be triggered (e.g., pre-configured metrics or threshold values) by multiple conditions such as when actual traffic load gets high in a current cell condition and / or additional model operation is needed through separate links (e.g., SN-UE) in parallel with the activated link (e.g., MN-UE). The configured model requested by the MN (e.g., LCM operation in MN-UE link) can be duplicated for activation in a SN-UE link after a SN connection setup. Alternatively, a separately configured model operation can be activated in the SN-UE link as requested by the MN.

[0050] An indication message from the network (NW) to the UE is sent to indicate activation / deactivation of the configured model operation with the SN(s) using L1 / L2 / L3 signaling such as MAC CE, DCI, or a RRC message. After model operation in the SN-UE link is activated, model operation in the MN-UE link can be offloaded and / or an additional SN-UE link can be set up for model operation in parallel with the MN-UE link. Depending on a two-sided model type, either the NW or the UE side can decide when to stop the distributed multi-model operation.

[0051] Figure 1 shows an exemplary block diagram of a distributed multi-model operation. In this example, the initial LCM-based model operation is set up between a MN and a UE. Before / after triggered for distributed multi-model operation, an AI / ML configuration and model information is sent to one or multiple candidate SNs through the wireline connection (e.g., Xn interface). After the SN connection setup is completed with information about the AI / ML configuration and model, split model operation is activated between SN(s) and UE.

[0052] The MN can aggregate / duplicate / switch AI / ML model operation to SN(s) fully or partially based on different use cases and LCM phases. If the LCM process is configured for model training, UE-side model training proceeds. When model training is completed, the UE sends an indication message to the network (e.g., MN / SN) to release the model operation.

[0053] Figure 2 shows an exemplary flowchart of a master node behavior for distributed multi-model operation. Activation of SN links for distributed multi-model operation can be triggered based on implementation-specific conditions such as traffic load level of a current cell condition, necessity of additional model operation through separate links (e.g., SN-UE) in parallel with the activated link (e.g., MN-UE), etc. An RRC re-configuration is sent to the UE after confirming the activation of SN link(s) for model operation.

[0054] Figure 3 shows an exemplary flowchart of a secondary node behavior for distributed multi-model operation. One or multiple SNs can be activated to support distributed multi-model operation so that the configured AI / ML model can be enabled through SN link(s) after receiving the required information about AI / ML configuration and model. Regarding specific models to be activated in SN link(s), the same model for both MN and SN(s) can be deployed through all links or different models can be applied to each links and / or different LCM phases can be activated for each link (e.g., model training in MN-UE link, model inferencing in SN-UE, etc.) if necessary.

[0055] Figure 4 shows an exemplary flowchart of a device behavior for distributed multi-model operation. After distributed multi-model operation is set up, the UE sided model can communicate with different numbers of network side models through multiple links with MN and SNs. On the UE side, the activated model operation of each link can be deactivated based on an indication message (e.g., L1 / L2 signaling) sent by the UE. Alternatively, depending on different use cases, the network side can decide to deactivate a two-sided model operation for both MN and SN links.

[0056] Figure 5 shows an exemplary signaling flow of applying distributed multi-model operation to multiple nodes. The indication message from the MN to the UE is sent to indicate activation / deactivation of the configured model operation with SN(s) using L1 / L2 / L3 signaling such as MAC CE, DCI, or a RRC message. MN-SN communication for ML model configuration and / or model transfer is exchanged for distributed multi-model operation through wireline (e.g., Xn interface). Regarding selection criteria of SNs, there can be multiple combinations of conditions such as link quality, ML applicable condition status, and / or model execution environment, etc.

[0057] Figure 6 shows an exemplary signaling flow of a setup process for distributed multi-model operation. Together with ML capability information from UE, the ML applicable condition update is reported to the network so that the network can decide whether distributed multi-model operation can be performed. A list of candidate SNs is also provided by the MN so that the configured ML operation can be deployed with the model(s) across multiple links in SNs.

[0058] In addition, a group of UEs connected with a MN can be offloaded to SN(s) for distributed multi-model operation so that the configured ML operation with the corresponding model(s) between the MN and the UEs can be aggregated / duplicated / switched to links between SN and UEs.

[0059] Candidate inactive models to be validated to support the activated model can be deployed in links between SN(s) and UEs so that any inactive model can be activated to replace the activated model with minimum switching time.

[0060] ABBREVIATIONS

[0061] Al Artificial intelligence

[0062] BWP Bandwidth part

[0063] CBG Code block group

[0064] CLI Cross Link Interference

[0065] CP Cyclic prefix

[0066] CQI Channel quality indicator

[0067] CPU CSI processing unit

[0068] CRB Common resource block

[0069] CRC Cyclic redundancy check

[0070] CRI CSI-RS Resource Indicator

[0071] CSI Channel state information

[0072] CSI-RS Channel state information reference signal

[0073] CSI-RSRP CSI reference signal received power

[0074] CSI-RSRQ CSI reference signal received quality

[0075] CSI-SINR CSI signal-to-noise and interference ratio

[0076] CW Codeword

[0077] DCI Downlink control information

[0078] DL Downlink

[0079] DM-RS Demodulation reference signals

[0080] DRX Discontinuous Reception

[0081] EPRE Energy per resource element

[0082] IAB-MT Integrated Access and Backhaul - Mobile Terminal

[0083] ID Identificator

[0084] L1 -RSRP Layer 1 reference signal received power

[0085] LI Layer Indicator

[0086] LCM Life cycle management

[0087] MCS Modulation and coding scheme

[0088] ML Machine learning

[0089] NW Network

[0090] PDCCH Physical downlink control channel

[0091] PDSCH Physical downlink shared channel PSS Primary Synchronisation signal PUCCH Physical uplink control channel QCL Quasi co-location PMI Precoding Matrix Indicator PRB Physical resource block PRG Precoding resource block group PRS Positioning reference signal PT-RS Phase-tracking reference signal RAN Radio Access Network RB Resource block RBG Resource block group Rl Rank Indicator RIV Resource indicator value RS Reference signal SCI Sidelink control information SLIV Start and length indicator value SR Scheduling Request SRS Sounding reference signal SS Synchronisation signal SSS Secondary Synchronisation signal SS-RSRP SS reference signal received power

[0092] SS-RSRQ SS reference signal received quality SS-SINR SS signal-to-noise and interference ratio TB Transport Block TCI Transmission Configuration Indicator TDM Time division multiplexing UE User equipment UL Uplink WG Work group

Claims

CLAIMS1 . A method of applying a distributed multi-model operation for an AI / ML LCM process in a network, the network comprising at least one master node (MN), at least one secondary node (SN), and a user equipment (UE), the method comprising the steps of:• Configuring a list of candidate SNs based on SN selection criteria,• Upon receiving a trigger, sending an AI / ML configuration and an LCM-based model information to the candidate SNs,• Receiving a SN indication message from at least one of the candidate SNs, the SN indication message comprising a SN setup requirement for the AI / ML LCM process and / or a SN setup acknowledgement,• Activating at least one SN link based on the received SN indication message and further based on a condition, and• Sending a re-configuration message via L1 / L2 / L3 signaling to the UE, re-configuring the UE to establish the multi-model operation of the AI / ML LCM process between the UE and the at least one of the candidate SNs, and2. Method according to claim 1 , characterized in that the trigger corresponds to a pre-configured metric or a threshold value for one of: traffic load level of a current cell condition and / or additional model operation requirements.

3. Method according to claim 1 or 2, characterized in that activating the at least one SN link comprises performing an aggregation, a duplication, or a switching of the configured multi-model operation based on different use cases and LCM phases.

4. Method according to one of the previous claims, characterized in that a further step comprises sending a UE indication message to the UE via L1 / L2 / L3 signaling, the UE indication message indicating activation or deactivation of the configured multi-model operation between the UE and the at least one of thecandidate SNs.

5. Method according to one of the previous claims, characterized in that a group of UEs linked with the MN are linked to the at least one of the candidate SNs for multi-model operation of the AI / ML LCM process.

6. Method according to one of the previous claims, characterized in that inactive AI / ML models are validated for support of the multi-model operation, the inactive AI / ML models being deployed in links between the UE and the at least one of the candidate SNs, wherein any of the inactive AI / ML model can be activated to replace or support a current model of the multi-model operation.

7. Method according to one of the previous claims, characterized in that it is decided by the network whether the distributed multi-model operation is performed and the list of candidate SNs is provided by the at least one MN.

8. Method according to one of the previous claims, characterized in that the SN selection criteria is at least one of: a link quality, an AI / ML applicable condition status, and / or model execution environment.

9. Method according to one of the previous claims, characterized in that an identical model is applied for the at least one MN and the at least one of the candidate SNs.

10. Method according to one of the previous claims, characterized in that a different model is applied for the at least one MN and the at least one of the candidate SNs11 . Method according to one of the previous claims, characterized in that different LCM phases are activated for each of the at least one SN link.

12. Method according to claim 11 , characterized in that the LCM phases are one of: model training in a MN-UE link and / or model training in a SN-UE link.

13. Method according to one of the previous claims, characterized in that a UE side model communicates with a different number of network side models through multiple links with the MN and the at least one of the candidate SNs, comprising:• The distributed model operation can be deactivated based on an indication message sent by the UE, or• The network can decide to deactivate the distributed model operation for the at least one MN and the at least one of the candidate SNs.

14. A wireless device, comprising at least one memory and at least one processor configured to carry out a method according to any one of the previous claims.

15. A user equipment (UE), comprising a wireless device according to claim 14.

16. A base station (BS), comprising at least one memory and at least one processor configured to carry out a method according to any one of claim 1 to 13.

17. A wireless communication system, comprising at least one base station according to claim 16 and at least one user equipment according to claim 15.

18. A computer program product comprising instructions which, when executed by at least one processor, configure said at least one processor to carry out a method according to any one of claims 1 to 13.

19. A computer-readable storage medium comprising instructions which, when executed by at least one processor, configure said at least one processor to carry out a method according to any one of claims 1 to 13.

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