Method of advanced assistance signaling for user equipment of machine learning reporting

Abstract

The application describes methods of data-driven AI / ML model signaling for applicable model update reporting with machine learning model operation in wireless mobile communication systems including base stations (e.g., gNB) and mobile stations (e.g., UE). The assistance information of index information is generated to provide an indication how robust each model reported by the UE is against the measured conditions so that the network side can obtain this assistance information when configuring any of the reported UE models.

Description

[0001] TITLE

[0002] Method of advanced assistance signaling for user equipment of machine learning reporting

[0003] TECHNNICAL FIELD

[0004] The present disclosure relates to AI / ML based applicable model update report signaling, where techniques for pre-configuring and signaling the assistance information about status updates of machine learning model operations 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 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 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 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 the framework will be one key work scope. Regarding AI / ML framework, various aspects are under consideration for investigation and one key item is about lifecycle management 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 mobility was also considered as one of AI / ML use cases and one scenario 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.

[0008] For the above active standardization works, currently there is no specification defined for signaling methods or network (e.g., gNB) / mobile station (e.g., UE) behaviors about supporting UE status update reporting about AI / ML model operation when multiple LCM (lifecycle management) operations are enabled on a device such as model training and inferencing and / or updating, etc. for one or more particular application scenarios with different set of ML features / functionalities. Since applicable conditions to support the enabled LCM operations can dynamically change due to in-device condition change and / or external condition change, the network side also needs to be aware of accurate information about the reported ML status update for those LCM operations. When any condition changes at UE side, it reports an applicable model along with all identified models. Based on such information, it could still be difficult for the network to configure the best reliable model(s) since model information from the UE side might not be sufficient to configure reliable model(s). As a result, if the network configures a model with low reliability, then further reporting from the UE would be required which increases signaling overhead as well as power consumption. For various use cases / scenarios with ML operation, it is expected that applicable condition can be dynamically changed as the associated ML model update could not be reported to the network accurately if all those dynamic changes are not frequently monitored and reported together.

[0009] US 2021 326 701 A1 describes a method of transmitting measurements to other nodes for neural network training, a method of reporting a UE capability to a server, and configuring neural network parameters. US 2022 116764 A1 shows that the base station receives, from each of a number of UEs, a machine learning processing capability report and the base station groups a number of UEs in accordance with the machine learning processing capability reports to receive gradient updates to the machine learning model.

[0010] US 2022 360 973 A1 shows that the UE may transmit to the network, based on the request to report the UE capability, an indication of one or more of an Al capability, an ML capability, a radio capability associated with the at least one of the Al capability or the ML capability , or a core network capability associated with the at least one of the Al capability or the ML capability.

[0011] WO 2022 008 037 A1 shows that a terminal indicates a capability to a network, wherein, in an inability state, the terminal is not able to execute and / or train the machine learning model at least with a predefined performance.

[0012] WO 2023 015 430 A1 shows that a UE may activate a ML model based on an association of an at least one second ML block configured with an at least one second parameter with an at least one first ML block configured with an at least one first parameter.

[0013] When any condition changes at UE side, it reports an applicable model along with all identified models. Based on such information, it could still be difficult for a NW to configure the best reliable model(s) since model information from the UE side might not be sufficient to configure reliable model(s). As a result, if the NW configures low reliable model, then further reporting from the UE would be required which increases signaling overhead as well as power consumption.

[0014] A Method of advanced assistance signaling for user equipment machine learning reporting within a network of a wireless communication system, comprises a step of monitoring an applicable model executed on the user equipment for applicable conditions. Further it comprises a step of creating, if the applicable conditions exceed at least one threshold value, of a mapping relationship between an applicable model update information and an associated index information, whereas the index information is associated with a ranked reliability indication of robustness against an applicable condition change, whereas the applicable model update information is associated with the applicable model. Further, the method comprises a step of reporting of the mapping relationship to a network node inside the network through L1 , L2, or L3 signaling.

[0015] Advantageously, the at least one threshold value is pre-determined by a network configuration of the wireless communication system for reliability ranges and the associated index information is identified for the different reliability ranges.

[0016] Advantageously, the at least one pre-determined threshold value is associated with a quality of experience.

[0017] Advantageously, the determination of the index information is based on the at least one pre-determined threshold value.

[0018] Advantageously, the determination of the index information is based on an autonomous decision of the user equipment.

[0019] Advantageously, the criteria for determining the index information is based on a marginal level of a measured applicable condition compared with a target applicable condition for each applicable model update information, wherein the applicable condition contains varying parameter sets related to radio conditions, machine learning conditions, environmental conditions, or device conditions, and / or the measured applicable condition is pre-defined depending on specific applicable condition scenarios.

[0020] Advantageously, the index information can be reported in a sequential way and the applicable model update information is reported to the network node inside the network without the associated index information. Advantageously, at least one of the reported applicable models from the mapping relationship is selected for machine learning configuration to create a configured model.

[0021] Advantageously, the network node sends a confirmation of the configured model to the user equipment through L1 , L2, or L3 signaling.

[0022] Advantageously, the network node is another user equipment or a base station.

[0023] Advantageously, the index information is associated with a ranked reliability of a property other than robustness against an applicable condition change related to machine learning updates in machine learning reporting.

[0024] Advantageously, separate index information is used for each type of machine learning related update.

[0025] An apparatus for advanced assistance signaling for user equipment machine learning reporting within a network of a wireless communication system, comprises a wireless transceiver and a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of an above-described method.

[0026] A user equipment comprises the above-described apparatus which proceeds the steps of measuring the applicable conditions, creating and reporting the mapping relationship.

[0027] A base station comprises the above-described apparatus, characterized in that the steps receiving UE reporting with index information, determination the highest reliable model(s), sending confirmation message about the selected UE model(s) information are performed.

[0028] A wireless communication system, characterized in that the above-described base station comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of an above-described method, wherein the above-described user equipment comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of an above-described method.

[0029] For UE behavior, update of applicable models and associated index information is sent to the network or update of applicable models without index information is sent to network (when the ordered robustness based applicable models can be reported in a sequential way).

[0030] For network behavior, after receiving UE reporting with index information, the indexed single model with the most robustness is selected for ML configuration OR One or more models with associated index(s) are selected for ML configuration and confirmation message is sent to UE

[0031] Beneficially, by this application a signaling overhead reduction achieved and UE power consumption saving is obtained.

[0032] BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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:

[0034] Figure 1 is an exemplary table of a mapping relationship between applicable model update information and index information; Figure 2 is an exemplary block diagram of determining index information for applicable models;

[0035] Figure 3 is an exemplary block diagram of mapping relationship between applicable models and index information with the pre-configured threshold values;

[0036] Figure 4 is a flowchart of a procedure to report index information on the network side;

[0037] Figure 5 is a flowchart of a procedure to report index information on the UE side;

[0038] Figure 6 is a flowchart of a procedure of pre-configuring threshold values; and

[0039] Figure 7 is a signaling flow of index information indication reporting.

[0040] DETAILED DESCRIPTION

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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).

[0057] 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.

[0058] 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.

[0059] 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).

[0060] 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.

[0061] 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.

[0062] Based on specific network-UE ML collaboration in deployment scenarios (e.g., UE mobility case), ML applicable conditions for LCM operations can be significantly changed with different mobility ranges over time by degrading any activated LCM operations. When ML applicable conditions are changed at UE side and applicable models along with all or subset of identified models are reported, it could still be difficult for the network to configure best reliable model(s) since model information from the UE side might not be sufficient to configure reliable model(s). As a result, if the network configures a model with low reliability, further reporting from the UE would be required which increases signaling overhead as well as power consumption.

[0063] In the method disclosed herein, the UE reports index information together with model update information. The index information indicates how robust each reported model is against applicable condition change. On UE side, an update of applicable models and associated index information can be sent to the network or update of applicable models without index information can be sent to the network when the ordered robustness based applicable models can be reported in a sequential way. On the network side, after receiving UE reporting with index information, one of the reported models with the most robustness can be selected for ML configuration or multiple models with associated index information can be selected for ML configuration by sending a confirmation message to the UE. Also, a sidelink-based UE-to-UE network can apply the same signaling mechanism of index information reporting along with model updates from one UE to other UE(s). In ML reporting, index information indicating reliability of any kind of ML related updates can be sent to the network side as well by using separate index information configurations for each type of ML related updates to report, which is implementation-specific. Figure 1 shows an exemplary table of a mapping relationship between applicable models and index information. When the UE determines that applicable conditions have changed, it will report the mapping relationship between applicable model update information and index information through L1 , L2, or L3 signaling where index information is the indication of robustness against applicable condition change. The network side will then configure the reliable model(s) based on the UE reporting. Also, confirmation of the configured model(s) to the UE can be sent through L1 , L2, or L3 signaling. For example, in the table, the index information can be that Model A is highest reliable and Model D is lowest reliable when considering applicable condition change. Depending on implementation, a subset of the mapping relationship between applicable models and index information can be also sent to the network. The number of applicable models can be flexibly configured and the mapping relationship between applicable models and index information can be also formed in different ways. For example, lower index information indicates higher reliability of the mapped model and higher index information indicates lower reliability of the mapped model. Or higher index information indicates lower reliability of the mapped model and lower index information indicates higher reliability of the mapped model. Model information can be a model ID and / or other representation that contains applicable model indication. The UE reporting can also contain other ML status information as well along with the mapping relationship between applicable model update information and index information.

[0064] Figure 2 shows an exemplary block diagram of determining index information for applicable models. In this example, it is shown that the highest reliable model for applicability is Model B and the lowest reliable model for applicability is Model C. The criteria of determining the applicable model index information can be based on a marginal level of the measured applicable conditions compared with target applicable conditions for each applicable models. Applicable conditions can contain varying parameter sets related to radio conditions, ML conditions, environmental conditions, device conditions, etc. For an applicable condition measurement, pre-defined specific applicable condition scenarios are to be applied. Threshold values are pre-determined by the network configuration for reliability ranges and the associated index information for each model is identified for different reliability levels. There can be multiple types of threshold value sets to reflect different applicable condition categories and those setting is configured by the network.

[0065] Figure 3 shows an exemplary block diagram of a mapping relationship between applicable models and index information with pre-configured threshold values. This block diagram is a generalization of Figure 2, where AAC is defined as the difference value between measured applicable conditions (ACi ieasure) and target applicable conditions (AOarget), which is expressed as AAC = ACivieasure - ACTarget and 7} (j=1 , ...,R) is a threshold value pre-determined by the network configuration and SMi (j=1, ...,R) is index information identified for different reliability levels.

[0066] Figure 4 shows a flowchart of a procedure of index information reporting for the network side. After receiving a UE reporting about applicable model update information with index information of the ranked reliability indication, the highest reliable model(s) can be determined for configuration, or multiple models can be selected among indexed models. Also, optionally, a confirmation message about the selected UE model(s) can be sent to the UE as well.

[0067] Figure 5 shows a flowchart of a procedure of index information reporting for the UE side. After measuring applicable conditions for applicable models, the ranked index information of applicable models with reliability indication is determined so that it is reported to the network side.

[0068] Figure 6 shows a flowchart of a procedure of pre-configuring threshold values. For determining index information indicating reliability of each reported models, the UE autonomously selects an applicable model(s), which can be the option by default. Another way is that the network sends the pre-configured threshold(s) and the UE determines the index information based on the threshold values. When the pre-configured threshold is sent, it can be broadcasted or multicasted or unicasted depending on the implementation. In another aspect, different numbers / types of the pre-configured thresholds for various ML applications / functionalities can be generated and sent to the UE for different implementation scenarios where the associated metrics for thresholds can vary as well (e.g., QOE as quality of experience).

[0069] Figure 7 shows a signaling flow of index information reporting. After measuring applicable conditions for each applicable models on the UE side, the ranked index information of applicable model information is identified so that it can be reported to the network.

[0070] Depending on the reported index information for the associated model(s), the network can send re-configuration information for model operation.

[0071] This application is intended to provide fundamental mechanisms of interworking and data information flow in radio access network collaboration for AI / ML support, especially with reliable model update reporting methods in ML operation aspect.

[0072] Based on the proposed application, gNB-UE behaviors for supporting AI / ML operation for wireless communication with joint ML operation can be greatly improved with the potential scenarios.

[0073] ABBREVIATIONS

[0074] Al Artificial intelligence

[0075] BWP Bandwidth part

[0076] CBG Code block group

[0077] CLI Cross Link Interference

[0078] CP Cyclic prefix

[0079] CQI Channel quality indicator

[0080] CPU CSI processing unit

[0081] CRB Common resource block

[0082] CRC Cyclic redundancy check

[0083] CRI CSI-RS Resource Indicator

[0084] CSI Channel state information

[0085] CSI-RS Channel state information reference signal

[0086] CSI-RSRP CSI reference signal received power

[0087] CSI-RSRQ CSI reference signal received quality

[0088] CSI-SINR CSI signal-to-noise and interference ratio

[0089] CW Codeword

[0090] DCI Downlink control information

[0091] DL Downlink

[0092] DM-RS Demodulation reference signals

[0093] DRX Discontinuous Reception

[0094] EPRE Energy per resource element

[0095] IAB-MT Integrated Access and Backhaul - Mobile Terminal

[0096] ID Identificator

[0097] L1 -RSRP Layer 1 reference signal received power

[0098] LI Layer Indicator

[0099] LCM Life cycle management

[0100] MCS Modulation and coding scheme

[0101] ML Machine learning

[0102] NW Network

[0103] PDCCH Physical downlink control channel

[0104] 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

[0105] 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 . Method of advanced assistance signaling for user equipment machine learning reporting within a network of a wireless communication system, comprising the steps:• monitoring an applicable model executed on the user equipment for applicable conditions,• creating, if the applicable conditions exceed at least one threshold value, of a mapping relationship between an applicable model update information and an associated index information, whereas the index information is associated with a ranked reliability indication of robustness against an applicable condition change, whereas the applicable model update information is associated with the applicable model, and• reporting of the mapping relationship to a network node inside the network through L1 , L2, or L3 signaling.

2. The method according to claim 1 , characterized in that the at least one threshold value is pre-determined by a network configuration of the wireless communication system for reliability ranges and the associated index information is identified for the different reliability ranges.

3. The method according to claim 2, characterized in that the at least one pre-determined threshold value is associated with a quality of experience.

4. The method according to claim 2, characterized in that the determination of the index information is based on the at least one pre-determined threshold value.

5. The method according to any of the previous claims, characterized in that the determination of the index information is based on an autonomous decision of the user equipment.

6. The method according to any of the previous claims, characterized in that the criteria for determining the index information is based on a marginal level of ameasured applicable condition compared with a target applicable condition for each applicable model update information, wherein• the applicable condition contains varying parameter sets related to radio conditions, machine learning conditions, environmental conditions, or device conditions, and / or• the measured applicable condition is pre-defined depending on specific applicable condition scenarios.

7. The method according to any of the previous claims, characterized in that the index information can be reported in a sequential way and the applicable model update information is reported to the network node inside the network without the associated index information.

8. The method according to any of the previous claims, characterized in that at least one of the reported applicable models from the mapping relationship is selected for machine learning configuration to create a configured model.

9. The method according to claim 7, characterized in that the network node sends a confirmation of the configured model to the user equipment through L1 , L2, or L3 signaling.

10. The method according to any of the previous claims, characterized in that the network node is another user equipment or a base station.11 . The method according to any of the previous claims, characterized in that the index information is associated with a ranked reliability of a property other than robustness against an applicable condition change related to machine learning updates in machine learning reporting.

12. The method according to any of the previous claims, characterized in that separate index information is used for each type of machine learning related update.

13. Apparatus for advanced assistance signaling for user equipment machine learning reporting within a network of a wireless communication system, comprising• a wireless transceiver, and• 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, characterized in that the steps of measuring the applicable conditions, creating and reporting the mapping relationship are proceeded.

15. Base station comprising an apparatus according to claim 11 , characterized in that the steps receiving UE reporting with index information, determination the highest reliable model(s), sending confirmation message about the selected UE model(s) information are performed.

16. Wireless communication system, characterized in that the base station according to claim 15 comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of claims 1 to 12, wherein the user equipment according to claim 14 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.

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