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WO2025016856A1 - Procédé de signalisation d'assistance avancée pour équipement utilisateur de rapport d'apprentissage automatique - Google Patents

Procédé de signalisation d'assistance avancée pour équipement utilisateur de rapport d'apprentissage automatique Download PDF

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Publication number
WO2025016856A1
WO2025016856A1 PCT/EP2024/069639 EP2024069639W WO2025016856A1 WO 2025016856 A1 WO2025016856 A1 WO 2025016856A1 EP 2024069639 W EP2024069639 W EP 2024069639W WO 2025016856 A1 WO2025016856 A1 WO 2025016856A1
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WO
WIPO (PCT)
Prior art keywords
applicable
model
index information
network
reporting
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PCT/EP2024/069639
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English (en)
Inventor
Hojin Kim
Rikin SHAH
Andreas Andrae
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Aumovio Germany GmbH
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Continental Automotive Technologies GmbH
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Publication of WO2025016856A1 publication Critical patent/WO2025016856A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

Definitions

  • 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.
  • 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.
  • the main objective of this study item is to study AI/ML framework for air-interface with target use cases by considering performance, complexity, and potential specification impact.
  • AI/ML model terminology and description to identify common and specific characteristics for the framework will be one key work scope.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the at least one pre-determined threshold value is associated with a quality of experience.
  • the determination of the index information is based on the at least one pre-determined threshold value.
  • the determination of the index information is based on an autonomous decision of the user equipment.
  • 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.
  • 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.
  • at least one of the reported applicable models from the mapping relationship is selected for machine learning configuration to create a configured model.
  • the network node sends a confirmation of the configured model to the user equipment through L1 , L2, or L3 signaling.
  • the network node is another user equipment or a base station.
  • 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.
  • index information is used for each type of machine learning related update.
  • 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.
  • a user equipment comprises the above-described apparatus which proceeds the steps of measuring the applicable conditions, creating and reporting the mapping relationship.
  • 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.
  • 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.
  • 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).
  • 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
  • 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
  • Figure 3 is an exemplary block diagram of mapping relationship between applicable models and index information with the pre-configured threshold values
  • Figure 4 is a flowchart of a procedure to report index information on the network side
  • Figure 5 is a flowchart of a procedure to report index information on the UE side
  • Figure 6 is a flowchart of a procedure of pre-configuring threshold values
  • Figure 7 is a signaling flow of index information indication reporting.
  • a more general term “network node” may be used and may correspond to any type of radio network node or any network node, which communicates with a UE (directly or via another node) and/or with another network node.
  • network nodes are NodeB, MeNB, ENB, a network node belonging to MCG or SCG, base station (BS), multi-standard radio (MSR) radio node such as MSR BS, eNodeB, gNodeB, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base transceiver station (BTS), access point (AP), transmission points, transmission nodes, RRU, RRH, nodes in distributed antenna system (DAS), core network node (e.g.
  • MSC Mobile Switching Center
  • MME Mobility Management Entity
  • O&M Operations & Maintenance
  • OSS Operations Support System
  • SON Self Optimized Network
  • positioning node e.g. Evolved- Serving Mobile Location Centre (E-SMLC)
  • E-SMLC Evolved- Serving Mobile Location Centre
  • MDT Minimization of Drive Tests
  • test equipment physical node or software
  • another UE etc.
  • the non-limiting term user equipment (UE) or wireless device may be used and may refer to any type of wireless device communicating with a network node and/or with another UE in a cellular or mobile communication system.
  • UE are target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communication, PDA, PAD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, UE category Ml, UE category M2, ProSe UE, V2V UE, V2X UE, etc.
  • terminologies such as base station/gNodeB and UE should be considered non-limiting and 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.
  • gNB gNodeB
  • aspects of the embodiments may be embodied as a system, apparatus, method, or computer program product.
  • embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects.
  • the disclosed embodiments may be implemented as a hardware circuit comprising custom very-large-scale integration (“VLSI”) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components.
  • VLSI very-large-scale integration
  • the disclosed embodiments may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, or the like.
  • the disclosed embodiments may include one or more physical or logical blocks of executable code which may, for instance, be organized as an object, procedure, or function.
  • embodiments may take the form of a 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.
  • the computer readable medium may be a computer readable storage medium.
  • the computer readable storage medium may be a storage device storing the code.
  • the storage device may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • a storage device More specific examples (a non-exhaustive list) of the storage device would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random-access memory (“RAM”), a read-only memory (“ROM”), an erasable programmable read-only memory (“EPROM” or Flash memory), a portable compact disc read-only memory (“CD-ROM”), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
  • a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Code for carrying out operations for embodiments may be any number of lines and may be written in any combination of one or more programming languages including an object-oriented programming language such as Python, Ruby, Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the “C” programming language, or the like, and/or machine languages such as assembly languages.
  • the code may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user’s computer through any type of network, including a local area network (“LAN”), wireless LAN (“WLAN”), or a wide area network (“WAN”), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider (“ISP”)).
  • LAN local area network
  • WLAN wireless LAN
  • WAN wide area network
  • ISP Internet Service Provider
  • the described features, structures, or characteristics of the embodiments may be combined in any suitable manner.
  • 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.
  • the code may also be stored in a storage device that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the storage device produce an article of manufacture including instructions which implement the function/act specified in the flowchart diagrams and/or block diagrams.
  • the code may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer implemented process such that the code which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart diagrams and/or block diagrams.
  • each block in the flowchart diagrams and/or block diagrams may represent a module, segment, or portion of code, which includes one or more executable instructions of the code for implementing the specified logical function(s).
  • an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted embodiment.
  • 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.
  • base stations e.g., gNB
  • mobile stations e.g., UE
  • 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).
  • 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.
  • model adaptation is required to support operations such as model switching, re-training, fallback, etc.
  • AI/ML model enabled wireless communication network 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.
  • ML applicable conditions for LCM operations can be significantly changed with different mobility ranges over time by degrading any activated LCM operations.
  • 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).
  • 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.
  • the UE reports index information together with model update information.
  • the index information indicates how robust each reported model is against applicable condition change.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • the index information can be that Model A is highest reliable and Model D is lowest reliable when considering applicable condition change.
  • 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.
  • Figure 2 shows an exemplary block diagram of determining index information for applicable models.
  • 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.
  • 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.
  • Figure 3 shows an exemplary block diagram of a mapping relationship between applicable models and index information with pre-configured threshold values.
  • 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.
  • 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.
  • Figure 6 shows a flowchart of a procedure of pre-configuring threshold values.
  • the UE autonomously selects an applicable model(s), which can be the option by default.
  • the network sends the pre-configured threshold(s) and the UE determines the index information based on the threshold values.
  • the pre-configured threshold is sent, it can be broadcasted or multicasted or unicasted depending on the implementation.
  • 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).
  • 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.
  • the network can send re-configuration information for model operation.
  • 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.
  • gNB-UE behaviors for supporting AI/ML operation for wireless communication with joint ML operation can be greatly improved with the potential scenarios.
  • 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

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Abstract

La demande décrit des procédés de signalisation de modèle IA/ML commandé par des données pour un rapport de mise à jour de modèle applicable avec un fonctionnement de modèle d'apprentissage automatique dans des systèmes de communication mobile sans fil comprenant des stations de base (par exemple, gNB) et des stations mobiles (par exemple, UE). Les informations d'assistance d'informations d'indice sont générées pour fournir une indication de la manière dont chaque modèle robuste rapporté par l'UE est contre les conditions mesurées de telle sorte que le côté réseau peut obtenir ces informations d'assistance lors de la configuration de l'un quelconque des modèles d'UE rapportés.
PCT/EP2024/069639 2023-07-17 2024-07-11 Procédé de signalisation d'assistance avancée pour équipement utilisateur de rapport d'apprentissage automatique Pending WO2025016856A1 (fr)

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DE102023206770 2023-07-17

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Citations (5)

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Publication number Priority date Publication date Assignee Title
US20210326701A1 (en) 2020-04-16 2021-10-21 Qualcomm Incorporated Architecture for machine learning (ml) assisted communications networks
WO2022008037A1 (fr) 2020-07-07 2022-01-13 Nokia Technologies Oy Aptitude et incapacité d'ue ml
US20220116764A1 (en) 2020-10-09 2022-04-14 Qualcomm Incorporated User equipment (ue) capability report for machine learning applications
US20220360973A1 (en) 2021-05-05 2022-11-10 Qualcomm Incorporated Ue capability for ai/ml
WO2023015430A1 (fr) 2021-08-10 2023-02-16 Qualcomm Incorporated Configuration de paramètres de structure ml combinée

Patent Citations (5)

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Publication number Priority date Publication date Assignee Title
US20210326701A1 (en) 2020-04-16 2021-10-21 Qualcomm Incorporated Architecture for machine learning (ml) assisted communications networks
WO2022008037A1 (fr) 2020-07-07 2022-01-13 Nokia Technologies Oy Aptitude et incapacité d'ue ml
US20220116764A1 (en) 2020-10-09 2022-04-14 Qualcomm Incorporated User equipment (ue) capability report for machine learning applications
US20220360973A1 (en) 2021-05-05 2022-11-10 Qualcomm Incorporated Ue capability for ai/ml
WO2023015430A1 (fr) 2021-08-10 2023-02-16 Qualcomm Incorporated Configuration de paramètres de structure ml combinée

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"Study on enhancement for Data Collection for NR and EN-DC", 3GPP TR 37.817
PATRICK MERIAS ET AL: "Summary#4 of General Aspects of AI/ML Framework", vol. RAN WG1, no. Online; 20230417 - 20230426, 27 April 2023 (2023-04-27), XP052307727, Retrieved from the Internet <URL:https://www.3gpp.org/ftp/TSG_RAN/WG1_RL1/TSGR1_112b-e/Docs/R1-2304052.zip R1-2304052 Summary-6-9.2.1-v009_CATT_Mod.docx> [retrieved on 20230427] *
YAN CHENG ET AL: "Discussion on AI/ML for CSI feedback enhancement", vol. RAN WG1, no. Online; 20230417 - 20230426, 7 April 2023 (2023-04-07), XP052292938, Retrieved from the Internet <URL:https://www.3gpp.org/ftp/TSG_RAN/WG1_RL1/TSGR1_112b-e/Docs/R1-2302359.zip R1-2302359.docx> [retrieved on 20230407] *

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