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WO2025067885A1 - Procédé de signalisation de modèle pour connectivité multiple - Google Patents

Procédé de signalisation de modèle pour connectivité multiple Download PDF

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Publication number
WO2025067885A1
WO2025067885A1 PCT/EP2024/075470 EP2024075470W WO2025067885A1 WO 2025067885 A1 WO2025067885 A1 WO 2025067885A1 EP 2024075470 W EP2024075470 W EP 2024075470W WO 2025067885 A1 WO2025067885 A1 WO 2025067885A1
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WIPO (PCT)
Prior art keywords
model
previous
model operation
lcm
link
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PCT/EP2024/075470
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English (en)
Inventor
Hojin Kim
Rikin SHAH
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Aumovio Germany GmbH
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Continental Automotive Technologies GmbH
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Publication of WO2025067885A1 publication Critical patent/WO2025067885A1/fr
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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • 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 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.
  • Al artificial intelligence
  • ML machine learning
  • AI/ML artificial intelligence/machine learning
  • RP-213599 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.
  • 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.
  • AI/ML models terminology and description to identify common and specific characteristics for a framework will be one of the key work scopes.
  • 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.
  • LCM lifecycle management
  • UE (user equipment) mobility was also considered as one of the AI/ML use cases.
  • 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.
  • model operations e.g., model training, inference, selection, switching, update, monitoring, etc.
  • MC multi-connectivity
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Figure 1 is an exemplary block diagram of distributed multi-model operation
  • Figure 2 is an exemplary flowchart of a master node behavior for distributed multi-model operation
  • 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
  • Figure 5 is an exemplary signaling flow chart of applying distributed multi-model operation to multiple nodes.
  • Figure 6 is an exemplary signaling flow chart of a setup process for distributed multi-model operation.
  • 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 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).
  • 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.
  • the statistical property of a dataset and the relationship between input and output for the trained model can be changed with drift occurrence.
  • model adaptation is required to support operations such as model switching, re-training, fallback, etc.
  • an 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 (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.
  • model performance such as inferencing and/or training is dependent on different model execution environment with varying configuration parameters.
  • 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.
  • AI/ML model enabled wireless communication network 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.
  • MN master node
  • SN secondary node
  • 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.
  • MCG master cell group
  • SCG secondary cell group
  • 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
  • a separately configured model operation can be activated in the SN-UE link as requested by the MN.
  • 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.
  • L1/L2/L3 signaling such as MAC CE, DCI, or a RRC message.
  • 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.
  • either the NW or the UE side can decide when to stop the distributed multi-model operation.
  • Figure 1 shows an exemplary block diagram of a distributed multi-model operation.
  • the initial LCM-based model operation is set up between a MN and a UE.
  • an AI/ML configuration and model information is sent to one or multiple candidate SNs through the wireline connection (e.g., Xn interface).
  • the wireline connection e.g., Xn interface.
  • split model operation is activated between SN(s) and UE.
  • 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.
  • the network e.g., MN/SN
  • 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.
  • 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.
  • 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.
  • Figure 4 shows an exemplary flowchart of a device behavior for distributed multi-model operation.
  • the UE sided model can communicate with different numbers of network side models through multiple links with MN and SNs.
  • the activated model operation of each link can be deactivated based on an indication message (e.g., L1/L2 signaling) sent by the UE.
  • the network side can decide to deactivate a two-sided model operation for both MN and SN links.
  • 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.
  • 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).
  • wireline e.g., Xn interface
  • 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.
  • Figure 6 shows an exemplary signaling flow of a setup process for distributed multi-model operation.
  • 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.
  • 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.
  • 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.
  • 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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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention concerne des procédés de préconfiguration d'une opération IA/ML lorsque des liens de multiconnectivité sont activés dans un système de communication mobile sans fil comprenant des stations de base (par exemple, gNB) et des stations mobiles (par exemple, UE). Lorsqu'un modèle IA/ML est appliqué à un réseau d'accès radio, les performances du modèle, telles que l'inférence et/ou l'entraînement, dépendent des différents environnements d'exécution du modèle avec des paramètres de configuration variables. Par conséquent, en reconfigurant n'importe quel modèle fonctionnant avec des liens de connexion multiples, la surcharge de signalisation due aux diverses opérations du modèle LCM peut être réduite tout en améliorant les performances du modèle.
PCT/EP2024/075470 2023-09-27 2024-09-12 Procédé de signalisation de modèle pour connectivité multiple Pending WO2025067885A1 (fr)

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DE102023209478.9 2023-09-27
DE102023209478 2023-09-27

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US20200267789A1 (en) * 2019-02-14 2020-08-20 Mediatek Inc. Methods and apparatus to improve mr-dc sn addition procedure
WO2021230712A1 (fr) 2020-05-14 2021-11-18 Samsung Electronics Co., Ltd. Procédés et appareil permettant de gérer des transferts intercellulaires dans une configuration à double connectivité dans un système de communication sans fil
WO2022144582A1 (fr) 2020-12-31 2022-07-07 Telefonaktiebolaget Lm Ericsson (Publ) Gestion de transfert intercellulaire dans un réseau de communication configuré pour prendre en charge une double connectivité multi-rat
WO2022147786A1 (fr) * 2021-01-08 2022-07-14 Lenovo (Beijing) Limited Procédé et appareil pour déterminer une prédiction de l'état d'un réseau sans fil
US20230053572A1 (en) 2021-08-17 2023-02-23 Qualcomm Incorporated Enhancement on mmw scg measurement configuration and adding/switching
US20230145079A1 (en) 2021-11-11 2023-05-11 Qualcomm Incorporated Secondary cell group (scg) failure prediction and traffic redistribution
WO2023148010A1 (fr) * 2022-02-07 2023-08-10 Telefonaktiebolaget Lm Ericsson (Publ) Gestion réseau-centrique du cycle de vie des modèles ai/ml déployés dans un équipement utilisateur (ue)

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Publication number Priority date Publication date Assignee Title
US20200267789A1 (en) * 2019-02-14 2020-08-20 Mediatek Inc. Methods and apparatus to improve mr-dc sn addition procedure
WO2021230712A1 (fr) 2020-05-14 2021-11-18 Samsung Electronics Co., Ltd. Procédés et appareil permettant de gérer des transferts intercellulaires dans une configuration à double connectivité dans un système de communication sans fil
WO2022144582A1 (fr) 2020-12-31 2022-07-07 Telefonaktiebolaget Lm Ericsson (Publ) Gestion de transfert intercellulaire dans un réseau de communication configuré pour prendre en charge une double connectivité multi-rat
WO2022147786A1 (fr) * 2021-01-08 2022-07-14 Lenovo (Beijing) Limited Procédé et appareil pour déterminer une prédiction de l'état d'un réseau sans fil
US20230053572A1 (en) 2021-08-17 2023-02-23 Qualcomm Incorporated Enhancement on mmw scg measurement configuration and adding/switching
US20230145079A1 (en) 2021-11-11 2023-05-11 Qualcomm Incorporated Secondary cell group (scg) failure prediction and traffic redistribution
WO2023148010A1 (fr) * 2022-02-07 2023-08-10 Telefonaktiebolaget Lm Ericsson (Publ) Gestion réseau-centrique du cycle de vie des modèles ai/ml déployés dans un équipement utilisateur (ue)

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