WO2024096386A1 - Procédés et appareil de transfert de données ia/ml dans un système de communication sans fil - Google Patents

Procédés et appareil de transfert de données ia/ml dans un système de communication sans fil Download PDF

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WO2024096386A1
WO2024096386A1 PCT/KR2023/016188 KR2023016188W WO2024096386A1 WO 2024096386 A1 WO2024096386 A1 WO 2024096386A1 KR 2023016188 W KR2023016188 W KR 2023016188W WO 2024096386 A1 WO2024096386 A1 WO 2024096386A1
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entity
data
information
mbs
network
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PCT/KR2023/016188
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English (en)
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David GUTIERREZ ESTEVEZ
Chadi KHIRALLAH
Mahmoud Watfa
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Samsung Electronics Co., Ltd.
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/16—Arrangements for providing special services to substations
    • H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
    • H04L12/1886—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast with traffic restrictions for efficiency improvement, e.g. involving subnets or subdomains
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
    • H04L65/60—Network streaming of media packets
    • H04L65/61—Network streaming of media packets for supporting one-way streaming services, e.g. Internet radio
    • H04L65/611—Network streaming of media packets for supporting one-way streaming services, e.g. Internet radio for multicast or broadcast
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/16—Arrangements for providing special services to substations
    • H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
    • H04L65/80—Responding to QoS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/16—Arrangements for providing special services to substations
    • H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
    • H04L12/189—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast in combination with wireless systems

Definitions

  • the present disclosure relates to a field of wireless communication networks. More particularly, the present disclosure relates to methods and apparatus for transferring an Artificial Intelligence (AI) / Machine Learning (ML) model and/or associated information.
  • AI Artificial Intelligence
  • ML Machine Learning
  • 5G mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in “Sub 6GHz” bands such as 3.5GHz, but also in “Above 6GHz” bands referred to as mmWave including 28GHz and 39GHz.
  • 6G mobile communication technologies referred to as Beyond 5G systems
  • THz terahertz
  • IIoT Industrial Internet of Things
  • IAB Integrated Access and Backhaul
  • DAPS Dual Active Protocol Stack
  • 5G baseline architecture for example, service based architecture or service based interface
  • NFV Network Functions Virtualization
  • SDN Software-Defined Networking
  • MEC Mobile Edge Computing
  • multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), array antennas and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of terahertz band signals, high-dimensional space multiplexing technology using OAM (Orbital Angular Momentum), and RIS (Reconfigurable Intelligent Surface), but also full-duplex technology for increasing frequency efficiency of 6G mobile communication technologies and improving system networks, AI-based communication technology for implementing system optimization by utilizing satellites and AI (Artificial Intelligence) from the design stage and internalizing end-to-end AI support functions, and next-generation distributed computing technology for implementing services at levels of complexity exceeding the limit of UE operation capability by utilizing ultra-high-performance communication and computing resources.
  • FD-MIMO Full Dimensional MIMO
  • OAM Organic Angular Momentum
  • RIS Reconfigurable Intelligent Surface
  • a first entity included in a communications network comprising: a transmitter; a receiver; and a controller configured to: receive, from a second entity included in the communications network, first information or signalling associated with transfer of artificial intelligence/machine learning (AI/ML) data; and transmit first AI/ML data to the second entity using multicast-broadcast services (MBS) or a local area data network (LADN), based on the first information or signalling.
  • AI/ML artificial intelligence/machine learning
  • MMS multicast-broadcast services
  • LADN local area data network
  • the first information or signalling comprises an indication that the second entity supports MBS.
  • the controller is configured to: based on receiving the indication, transmit, to the second entity, second information or signalling for configuring the second entity to request the first AI/ML data; and receive, from the second entity, a request for the first AI/ML data; and the first AI/ML data is transmitted using MBS in response to the request.
  • the first information or signalling comprises a request for the first AI/ML data; and the first AI/ML data is transmitted to the second entity in response to the request.
  • the request comprises information identifying one or more AI/ML models and/or information associated with one or more AI/ML models; and the first AI/ML data comprises the identified one or more AI/ML models and/or information associated with one or more AI/ML models.
  • the controller is configured to: transmit, to the second entity, one or more parameters for receiving the first AI/ML data using MBS; and/or transmit, to the second entity, an indication of presence of MBS data for a AI/ML service; and wherein the first AI/ML data is transmitted to the second entity using MBS.
  • the one or more parameters are transmitted using dedicated signalling or system information, or signalling using the application layer; and/or the one or more parameters comprises one or more of: at least one Temporary Mobile Group Identity (TMGI), at least one Internet Protocol (IP) address, or information on an area of multicast.
  • TMGI Temporary Mobile Group Identity
  • IP Internet Protocol
  • the first entity is a network function (NF) included in a 5G core (5GC) connected to a radio access network (RAN), and is connected to one or more MBS-related function; and the controller is configured to: collect data for AI/ML related to the RAN, the collected data including the first AI/ML data; and provide the first AI/ML data into a MBS framework for transmission to the second entity.
  • NF network function
  • the controller is configured to: collect data regarding delivery modes of previous MBS traffic and/or status of the RAN; and provide, to the connected one or more MBS-related function, a recommendation on a delivery mode decision for the second entity and/or a recommendation to switch a delivery mode.
  • a protocol data unit (PDU) session is established between the second entity and the first entity for transfer of the AI/ML data.
  • PDU protocol data unit
  • the controller is configured to: deploy the LADN in an area for transferring the AI/ML data to the second entity; and transmit the first AI/ML data to the second entity using the LADN when a protocol data unit (PDU) session is established with the second entity.
  • PDU protocol data unit
  • the first information or signalling comprises an indication that the second entity supports LADN AI/ML connection or a request for LADN information; and the controller is configured to: in response to receiving the first information or signalling, transmit the LADN information to the second entity, where the LADN information is for use in establishing the PDU session or the LADN information is requested for transfer of the first AI/ML data.
  • the LADN information comprises a list of tracking area identifiers (TAIs) indicating where the PDU session can be obtained.
  • TAIs tracking area identifiers
  • a second entity included in a communications network comprising: a transmitter; a receiver; and a controller configured to: transmit, to a first entity included in the communications network, first information or signalling associated with transfer of artificial intelligence/machine learning (AI/ML) data; and receive first AI/ML data to the second entity using multicast-broadcast services (MBS) or a local area data network (LADN), based on the first information or signalling.
  • AI/ML artificial intelligence/machine learning
  • MMS multicast-broadcast services
  • LADN local area data network
  • the first information or signalling comprises an indication that the second entity supports MBS.
  • the controller is configured to: receive, from the first entity, second information or signalling for configuring the second entity to request the first AI/ML data; and transmit, to the first entity, a request for the first AI/ML data based on the second information or signalling; and wherein the first AI/ML data is received using MBS.
  • the first information or signalling comprises a request for the first AI/ML data; and wherein the first AI/ML data is received using MBS.
  • the request comprises information identifying one or more AI/ML models and/or information associated with one or more AI/ML models; and the first AI/ML data comprises the identified one or more AI/ML models and/or information associated with one or more AI/ML models.
  • the controller is configured to: receive, from the first entity, one or more parameters for receiving the first AI/ML data using MBS; and/or receive, from the first entity, an indication of presence of MBS data for a AI/ML service; and the first AI/ML data is received from the first entity using MBS based on the one or more parameters and/or the indication of presence.
  • the one or more parameters are transmitted using dedicated signalling or system information, or signalling using the application layer; and/or the one or more parameters comprises one or more of: at least one Temporary Mobile Group Identity (TMGI), at least one Internet Protocol (IP) address, or information on an area of multicast.
  • TMGI Temporary Mobile Group Identity
  • IP Internet Protocol
  • the controller is configured to join an MBS session for receiving the first AI/ML data based on one or more of: determining the second entity is in an area where MBS is available for transfer of AI/ML data, based on the one or more parameters and/or the indication of presence; determining a current location of the second entity is within an area indicated in the indication of presence; receiving the indication of presence in response to a request, transmitted from the second entity to the first entity, for the first AI/ML data; or determining a current location of the second entity is within a satellite coverage area where the MBS is available, based on satellite ephemeris information, the one or more parameters and/or the indication of presence.
  • the controller is configured to: establish a protocol data unit (PDU) session with the first entity for receiving the first AI/ML data using MBS, based on one or more MBS parameters received from the first entity or from a third entity in the communications network.
  • PDU protocol data unit
  • the first information or signalling comprises an indication that the second entity supports LADN AI/ML connection or a request for LADN information; and wherein the controller is configured to: receive the LADN information from the first entity, where the LADN information is for use in establishing a LADN protocol data unit (PDU) session with the first entity or the LADN information is requested for transfer of the first AI/ML data.
  • PDU protocol data unit
  • the LADN information comprises a list of tracking area identifiers (TAIs) indicating where the LADN PDU session can be obtained; the controller is configured to establish the LAD PDU session based on determining a current location of the second entity is within a TAI included in the list of TAIs; and the first AI/ML data is received via the LADN PDU session.
  • TAIs tracking area identifiers
  • the first entity is one of 5G core (5GC), radio access network (RAN) or a network function (NF); and/or the second entity is a user equipment (UE) or a plurality of UEs.
  • 5GC 5G core
  • RAN radio access network
  • NF network function
  • UE user equipment
  • a method of a first entity included in a communications network comprising: receiving, from a second entity included in the communications network, first information or signalling associated with transfer of artificial intelligence/machine learning (AI/ML) data; and transmitting first AI/ML data to the second entity using multicast-broadcast services (MBS) or a local area data network (LADN), based on the first information or signalling.
  • AI/ML artificial intelligence/machine learning
  • a method of a second entity included in a communications network comprising: transmitting, to a first entity included in the communications network, first information or signalling associated with transfer of artificial intelligence/machine learning (AI/ML) data; and receiving first AI/ML data to the second entity using multicast-broadcast services (MBS) or a local area data network (LADN), based on the first information or signalling.
  • AI/ML artificial intelligence/machine learning
  • a network comprising: a first entity according to any aspect or example identified above, and a second entity according to any aspect or example identified above.
  • a computer readable storage medium comprising instructions which, when executed by one or more processor of an electronic device, cause the electronic device to perform a method according to any of the aspects and/or examples identified above.
  • FIG. 1 illustrates delivery methods in accordance with examples of the present disclosure
  • FIG. 2 illustrates 5G system architecture in accordance with examples of the present disclosure
  • FIG. 3 illustrates 5G System architecture in accordance with examples of the present disclosure
  • FIG. 4 illustrates a system architecture in accordance with examples of the present disclosure
  • Figure 5 is a block diagram illustrating an example structure of a network entity in accordance with certain examples of the present disclosure
  • Figure 6 is a block diagram illustrating a structure of a user equipment according to an embodiment of the disclosure.
  • Figure 7 is a block diagram illustrating a structure of a base station according to an embodiment of the disclosure.
  • Figure 8 is a block diagram illustrating a structure of a network entity according to an embodiment of the disclosure.
  • certain examples of the present disclosure relate to methods and apparatus for transferring an AI/ML model and/or AI/ML associated information to one or more devices using multicast/broadcast services (MBS). Further, certain examples of the present disclosure relate to methods and apparatus for providing an architecture to support AI/ML data transfer via MBS. Additionally, certain examples of the present disclosure relate to methods and apparatus for using local area data network (LADN) protocol data unit (PDU) sessions to transfer AI/ML model and/or associated information.
  • LADN local area data network
  • PDU protocol data unit
  • Wireless or mobile (cellular) communications networks in which a mobile terminal (e.g., user equipment (UE), such as a mobile handset) communicates via a radio link with a network of base stations, or other wireless access points or nodes, have undergone rapid development through a number of generations.
  • a mobile terminal e.g., user equipment (UE), such as a mobile handset
  • 3GPP 3 rd Generation Partnership Project
  • 4G Fourth Generation
  • 5G Fifth Generation
  • 3GPP standards for 4G systems include an Evolved Packet Core (EPC) and an Enhanced-UTRAN (E-UTRAN: an Enhanced Universal Terrestrial Radio Access Network).
  • EPC Evolved Packet Core
  • E-UTRAN Enhanced-UTRAN
  • LTE Long Term Evolution
  • LTE is commonly used to refer to the whole system including both the EPC and the E-UTRAN, and LTE is used in this sense in the remainder of this document.
  • LTE should also be taken to include LTE enhancements such as LTE Advanced and LTE Pro, which offer enhanced data rates compared to LTE.
  • 5G New Radio 5G New Radio
  • 5G NR 5G New Radio
  • NR is designed to support the wide variety of services and use case scenarios envisaged for 5G networks, though builds upon established LTE technologies.
  • New frameworks and architectures are also being developed as part of 5G networks in order to increase the range of functionality and use cases available through 5G networks.
  • One such new framework is the use of artificial intelligence / machine learning (AI/ML), which may be used for the optimisation of the operation of 5G networks.
  • AI/ML artificial intelligence / machine learning
  • AI/ML models and/or data might be transferred across the AI/ML applications (e.g., application functions (AFs)), 5GC (5G core), UEs (user equipments) etc.).
  • AI/ML works could be divided into two main phases: model training and inference. During model training and inference, multiple rounds of interaction may be required.
  • the AI/ML operation/model is split into multiple parts according to the current task and environment.
  • the intention is to offload the computation-intensive, energy-intensive parts to network endpoints, whereas leave the privacy-sensitive and delay-sensitive parts at the end device.
  • the device executes the operation/model up to a specific part/layer and then sends the intermediate data to the network endpoint.
  • the network endpoint executes the remaining parts/layers and feeds the inference results back to the device.
  • Multi-functional mobile terminals might need to switch the AI/ML model in response to task and environment variations.
  • the condition of adaptive model selection is that the models to be selected are available for the mobile device.
  • it can be determined to not pre-load all candidate AI/ML models on-board.
  • Online model distribution i.e. new model downloading
  • NW network
  • the model performance at the UE needs to be monitored constantly.
  • the cloud server trains a global model by aggregating local models partially-trained by each end devices.
  • a UE performs the training based on the model downloaded from the AI server using the local training data. Then the UE reports the interim training results to the cloud server via 5G UL channels.
  • the server aggregates the interim training results from the UEs and updates the global model. The updated global model is then distributed back to the UEs and the UEs can perform the training for the next iteration.
  • 3GPP agreed Rel-18 Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface (referring to 3GPP technical specification group (TSG) RAN meeting #94-3, RP-213599).
  • AI Artificial Intelligence
  • ML Machine Learning
  • RAN1 concluded the following assumptions and agreements:
  • Table 1 is related to RAN1#109.
  • Level y includes cases without model delivery.
  • - R2 assumes that a model is identified by a model ID. Its usage is FFS.
  • AIML Model delivery to the UE may have different options, Control-plane (multiple subvariants), User Plane, can be discussed case by case.
  • AI/ML model delivery from the network to a device/devices may depend on the assumed collaboration level between the network and the device. For example, for network-UE collaboration level z (referring to the above: ‘Signaling-based collaboration with model transfer’), the network would need to transfer the model (fully or partially) and any model associated information to the UE, using Control plane (i.e. RRC signalling or Non-Access Stratum (NAS) signalling) or User Plane options.
  • Control plane i.e. RRC signalling or Non-Access Stratum (NAS) signalling
  • NAS Non-Access Stratum
  • AI/ML model size could be large, depending for example on model use case, functionality, scenario, or configuration and/or model structure/format, and/or model input and output data set size, then transferring the model and any associated model information may result in high increase in signalling overhead and radio interface resources. This may be especially so if the network needs to deliver the model to more than one UE at a given time and/or location.
  • control plane becomes inefficient due to a large number of UEs and/or a large amount of data to be shared - this may overwhelm the signalling plane of the system and is hence inefficient.
  • UP user plane
  • the common CP or UP methods may not be efficient for AI/ML especially when the data needs to be shared as quickly as possible with minimal (or at least reduced) latency, where the latency if high may negatively impact the AI/ML application and/or objective.
  • the expression “at least one of A, B and/or C” (or the like) and the expression “one or more of A, B and/or C” (or the like) should be seen to separately include all possible combinations, for example: A, B, C, A and B, A and C, A and B and C.
  • X for Y (where Y is some action, process, operation, function, activity or step and X is some means for carrying out that action, process, operation, function, activity or step) encompasses means X adapted, configured or arranged specifically, but not necessarily exclusively, to do Y.
  • Certain examples of the present disclosure relate to methods, apparatus and/or systems etc. for transferring an Artificial Intelligence (AI) / Machine Learning (ML) model and/or associated information. Further, certain examples of the present disclosure relate to methods and apparatus for transferring an AI/ML model and/or AI/ML associated information to one or more devices using multicast/broadcast services (MBS). Further, certain examples of the present disclosure relate to methods and apparatus for providing an architecture to support AI/ML data transfer via MBS. Additionally, certain examples of the present disclosure relate to methods and apparatus for using local area data network (LADN) protocol data unit (PDU) sessions to transfer AI/ML model and/or associated information.
  • LADN local area data network
  • PDU protocol data unit
  • 3GPP 5G 3rd Generation Partnership Project
  • the techniques disclosed herein are not limited to these examples or to 3GPP 5G, and may be applied in any suitable system or standard, for example one or more existing and/or future generation wireless communication systems or standards.
  • the techniques disclosed herein may be applied in any existing or future releases of 3GPP 5G NR or any other relevant standard.
  • the functionality of the various network entities and other features disclosed herein may be applied to corresponding or equivalent entities or features in other communication systems or standards.
  • Corresponding or equivalent entities or features may be regarded as entities or features that perform the same or similar role, function, operation or purpose within the network.
  • a particular network entity may be implemented as a network element on a dedicated hardware, as a software instance running on a dedicated hardware, and/or as a virtualised function instantiated on an appropriate platform, e.g. on a cloud infrastructure.
  • One or more of the messages in the examples disclosed herein may be replaced with one or more alternative messages, signals or other type of information carriers that communicate equivalent or corresponding information.
  • One or more non-essential elements, entities and/or messages may be omitted in certain examples.
  • the transmission of information between network entities is not limited to the specific form, type and/or order of messages described in relation to the examples disclosed herein.
  • an apparatus/device/network entity configured to perform one or more defined network functions and/or a method therefor.
  • Such an apparatus/device/network entity may comprise one or more elements, for example one or more of receivers, transmitters, transceivers, processors, controllers, modules, units, and the like, each element configured to perform one or more corresponding processes, operations and/or method steps for implementing the techniques described herein.
  • an operation/function of X may be performed by a module configured to perform X (or an X-module).
  • Certain examples of the present disclosure may be provided in the form of a system (e.g., a network) comprising one or more such apparatuses/devices/network entities, and/or a method therefor.
  • examples of the present disclosure may be realized in the form of hardware, software or a combination of hardware and software.
  • Certain examples of the present disclosure may provide a computer program comprising instructions or code which, when executed, implement a method, system and/or apparatus in accordance with any aspect, example and/or embodiment disclosed herein.
  • Certain embodiments of the present disclosure provide a machine-readable storage storing such a program.
  • 5G CN receives a single copy of MBS data packets and delivers separate copies of those MBS data packets to individual UEs via per-UE PDU sessions, hence for each such UE one PDU session is required to be associated with a Multicast MBS Session.
  • 5G CN receives a single copy of MBS data packets and delivers a single copy of those MBS data packets to a RAN node.
  • Area Session Identifier A unique identifier within an MBS Session used for an MBS session with location dependent content. When present, the Area Session ID, together with the TMGI, is used to uniquely identify the data flow of an MBS Session in a specific MBS service area.
  • Associated PDU Session A PDU Session associated to a multicast MBS session that is used for 5GC Individual MBS traffic delivery method and for signalling related to a user's participation in a multicast MBS session such as join and leave requests.
  • Associated QoS Flow A unicast QoS Flow that belongs to the associated PDU Session and is used for 5GC Individual MBS traffic delivery method.
  • the associated QoS Flow is mapped from a multicast QoS Flow in a multicast MBS session.
  • Broadcast communication service A 5GS communication service in which the same service and the same specific content data are provided simultaneously to all UEs in a geographical area (i.e. all UEs in the broadcast coverage area are authorized to receive the data).
  • the content provider and network may not be aware whether the authorized UEs are actually receiving the data being delivered.
  • Broadcast MBS session An MBS session to deliver the broadcast communication service.
  • a broadcast MBS session is characterised by the content to send and the geographical area where to distribute it.
  • Broadcast service area The area within which data of one or multiple Broadcast MBS session(s) are sent.
  • MBS QoS Flow The finest granularity for QoS forwarding treatment for MBS data. Providing different QoS forwarding treatment requires separate MBS QoS Flows in 5GS supporting MBS.
  • MBS Service Announcement Mechanism to allow users to be informed about the available MBS services.
  • MBS session A multicast MBS session or a broadcast MBS session.
  • MBS service area The area within which data of one Multicast or Broadcast MBS session may be sent.
  • an Area Session ID which is unique per MBS Session ID, is allocated and the same location dependent content data for an MBS session is delivered to the UE(s) within an MBS service area.
  • Multicast communication service A 5GS communication service in which the same service and the same specific content data are provided simultaneously to a dedicated set of UEs (i.e. not all UEs in the coverage of the MBS service area are authorized to receive the data).
  • Multicast MBS session An MBS session to deliver the multicast communication service.
  • a multicast MBS session is characterised by the content to send, by the list of UEs that may receive the service and optionally by a geographical area where to distribute it.
  • the common CP or UP methods may not be efficient for use with AI/ML especially when the data needs to be shared as quickly as possible with minimal (or at least reduced) latency, where the latency if high may negatively impact the AI/ML application and/or objective.
  • Certain examples of the present invention provide methods, systems, apparatus etc. which aim to address this problem and/or related issues.
  • Certain examples of the present disclosure provide methods, systems, apparatus etc. for efficiently sharing AI/ML data with a (potentially large) set of devices (e.g., UEs).
  • a (potentially large) set of devices e.g., UEs.
  • certain embodiments include the delivering (or transmitting, providing, sending etc.) of AI/ML model(s) to device(s) based on multicast/broadcast service (MBS).
  • MBS multicast/broadcast service
  • Figure 1 The figure referred to in the final paragraph above is shown in Figure 1, which corresponds to Fig. 4.1-1 (‘Delivery Methods’) from TS 23.247 [2] as indicated previously.
  • TS 23.247 also discloses the architecture of Multicast and Broadcast Service, as follows and with reference to Figure 2 and Figure 3.
  • Fig. 2 corresponds to Fig. 5.1-1 (“5G System architecture for Multicast and Broadcast Service”) from TS 23.247 [2] as indicated previously
  • Fig. 3 corresponds to Fig. 5.1-2 (“5G System architecture for Multicast and Broadcast Service in reference point representation”) from TS 23.247 [2] as indicated previously; accordingly, in the following excerpt of TS 23.247 [2], references to “ Figure 5.1-1” may be understood with reference to Fig. 2 and references to “Figure 5.1-2” may be understood with reference to Fig. 3. From TS 23.247:
  • the actual data to be shared for the MBS service is provided by the AF using the Nmb8 reference point. Furthermore, the AF is assumed to be an external entity which interacts with the 3GPP system via the NEF (Network Exposure Function).
  • NEF Network Exposure Function
  • a system, method or apparatus etc. for delivering a AI/ML model(s) (and/or associated information) to devices or network entities based on MBS architecture.
  • Certain embodiments may reduce signalling overhead resulting from model delivery to device(s) by benefiting from the efficient usage of network resources of the MBS systems.
  • Certain embodiments may also improve on the latency as the data may be close to the network (e.g., radio access network (RAN)) and hence requires less time to be transported to the device.
  • RAN radio access network
  • Certain embodiments of the present disclosure relate to AI/ML model transfer from the network (e.g., one or more network entities (e.g., a network node, network function, virtual entity, logical entity etc.)) to the device(s).
  • a network e.g., one or more network entities (e.g., a network node, network function, virtual entity, logical entity etc.)
  • the present disclosure is not limited to a UE but may refer to a terminal or another device (e.g., a generic device) instead - the reference to a UE is merely to more-clearly illustrate examples of the present disclosure.
  • Certain examples of the present disclosure include the network (for example, RAN node, CN entity, and/or any other network entity) delivering/transferring to the UE(s) an AI/ML model using multicast-broadcast services.
  • a network e.g., any one or more network entities
  • MBS Mobility Management Entity
  • the network (e.g. RAN node, CN entity, and/or any other network entity) delivers/transfers to the UE(s) an AI/ML model and any associated model information using multicast-broadcast services.
  • the network delivers to the UE(s) information associated with an AI/ML model using multicast-broadcast services.
  • the network may configure the device(s), with capability to support multicast-broadcast services, to request the transfer/delivery of an AI/ML model and/or any associated model information via multicast-broadcast services.
  • the network may transmit information or signalling to the device(s) to configure the device(s) to request the transfer/delivery of an AI/ML model and/or any associated model information via MBS.
  • the network may deliver an AI/ML model and/or any associated model information, to the UE(s) located in a given area via multicast-broadcast services.
  • the network may identify location information or a region to which an AI/ML model(s) is to be delivered, and deliver the model to one or more UE(s) located in the location/region.
  • the UE indicates to the network (e.g. a network entity, as per the examples above) whether it supports delivery/transfer of an AI/ML model and/or any associated model information via multicast-broadcast services.
  • the UE may transmit data, information, a signal etc. to the network (e.g., to a RAN node, CN entity, gNB and/or any network entity) to inform the network that delivery/transfer of an AI/ML model and/or any associated model information via multicast-broadcast services is supported by the UE.
  • the UE may request the network to deliver/transfer more than one AI/ML model and/or associated model(s) information.
  • the UE may transmit information identifying one or more AI/ML models and/or associated model(s) information to be delivered to the UE.
  • the existing MBS framework may be reused and the network may feed the AI/ML data (e.g., AI/ML model(s), AI/ML model associated information, AI/ML related information etc.) into the MBS framework.
  • the device e.g., UE
  • the device may be provided (e.g., by the network or one or more network entities) with the relevant parameters to receive this data, e.g., Temporary Mobile Group Identity (TMGI) or Internet Protocol (IP) addresses (e.g. Source specific IP multicast address for IPv4, or Source specific IP multicast address for IPv6), area of multicast (e.g. service area, list of TAIs, etc).
  • TMGI Temporary Mobile Group Identity
  • IP Internet Protocol
  • the TMGI, and/or any other parameter that is required by the UE to receive this data may be configured in the UE or provided to the UE by the network, e.g., using dedicated signaling (NAS and/or RRC signaling/messages) or system information (e.g. broadcast periodically or on-demand) or signaling using the application layer.
  • NAS and/or RRC signaling/messages dedicated signaling
  • system information e.g. broadcast periodically or on-demand
  • a UE may attempt to join an MBS session when any one (or more) of the following occurs:
  • the UE is in an area where MBS service is available for AI/ML, where the UE may make this determination. For example, the UE may determine, based on the current tracking area identity (TAI) and the knowledge of where an MBS session is available in an area or a TAI.
  • TAI current tracking area identity
  • the UE receives (e.g., from the network) an indication of presence of MBS data for AI/ML in the current UE location (e.g. current TAI or MBS service area, etc). This indication may be received via RRC or NAS signaling or system information.
  • the UE receives (e.g., from the network) an indication of availability or presence of MBS data for AI/ML following a previous UE request for this data.
  • This indication may be received via RRC or NAS signaling or system information, while the UE request may be provided using RRC or NAS signaling.
  • the UE is in a satellite coverage area where MBS service is available for AI/ML, where the UE may make this determination. For example, the UE may determine it is in a satellite coverage area based on knowledge of satellite ephemeris or other information.
  • Certain examples of the present disclosure relate to new architecture to support AI/ML data transfer via MBS.
  • a network function is defined to be within the 5G core (5GC) which may be connected to the RAN (e.g. more than one radio network entity such as gNB) and, optionally, may also connect to any one or more of a/the Multicast/Broadcast Service Function (MBSF), Multicast/Broadcast Service Transport Function (MBSTF), Multicast/Broadcast Session Management Function (MB-SMF) and Multicast/Broadcast User Plane Function (MB-UPF).
  • MBSF Multicast/Broadcast Service Function
  • MMSTF Multicast/Broadcast Service Transport Function
  • MB-SMF Multicast/Broadcast Session Management Function
  • MB-UPF Multicast/Broadcast User Plane Function
  • the NF (which may be a new NF) may be referred to as an AI/ML NF or RAN AI/ML NF.
  • AI/ML NF or RAN AI/ML NF.
  • RAN AI/ML NF RAN AI/ML NF.
  • This terminology is used at times below to assist in illustrating features of the present disclosure, however the present disclosure should not be seen as limited thereto and the term NF (or even ‘network entity’) may be considered in place of AI/ML NF and RAN AI/ML NF (or the like).
  • the NF is configured to communicate with any network entity such as but not limited to the RAN, Access and Mobility Management Function (AMF), SMF, UPF, Unified Data Manager (UDM), PCF, NEF, etc, or any of the entities listed above and/or below.
  • AMF Access and Mobility Management Function
  • SMF Serving Mobility Management Function
  • UPF User Plane Function
  • UDM Unified Data Manager
  • PCF PCF
  • NEF Network Element Function
  • AI/ML NF 100 a new entity 100 (AI/ML NF 100) and connections 111, 113, 115, 117, 119 to other network entities are shown (for instance, compare with Fig. 2).
  • AI/ML NF 100 may be connected to other nodes/entities as described above, although this is not shown in the figure for brevity.
  • AI/ML NF 100 may be connected to fewer nodes and/or entities than illustrated in the figure - essentially the AI/ML NF 100 may be connected to any of the illustrated network entities as desired and as appropriate, and also to other, non-illustrated network entities.
  • one function of the NF 100 may be to collect data for AI/ML which is related to the RAN and, optionally, process this data and, optionally, feed it (e.g., deliver, transmit etc.) into the MBS framework for sharing with the UE(s).
  • the NF 100 may take any one or more of the actions and/or responsibilities of the AF/AS node (see Fig. 4) and hence may mimic, at least to some extent, the AF/AS node.
  • this NF 100 may be local to the 5GC and the NF 100 may feed the AI/ML data to the MBSTF, or any other MBS node, so that it is shared to UEs with reduced latency; where the reduced latency may arise, for example, because this node (AI/ML NF 100) may be residing closer to the RAN whilst also connected to the MBS framework.
  • a UE may establish, with this AI/ML NF 100, a PDU session, where a specific Data Network Name (DNN) and/or slice (e.g., Single Network Slice Selection Assistance Information (S-NSSAI)) may be reserved for this purpose, such that the RAN AI/ML NF 100 may appear to be the endpoint AF/AS for AI/ML application.
  • DNN Data Network Name
  • S-NSSAI Single Network Slice Selection Assistance Information
  • the NF 100 may also share the TMGI or any other MBS parameter with the UE, where the parameter(s) may be used for joining a session to receive AI/ML data.
  • the MBS parameter(s) may be provided by the 5GC to the UE; e.g., the TMGI may be provided by the RAN (using any RRC message and/or information element (IE), which may be new or existing, or using system information, e.g., new or existing system information block(s) (SIB(s))), or by the Access and Mobility Management Function (AMF) (for example, using any existing or new Non-Access Stratum (NAS) message and/or IE), or by the Session Management Function (SMF) (for example, using any existing or new NAS message and/or IE), or by any other entity.
  • IE resource control
  • the 5GC may behave as described as in the examples above (i.e., to provide the MBS parameter(s) to the UE) when the subscription information requires so.
  • the 5GC entities may share this information amongst themselves.
  • the AMF may obtain this indication from the subscription information and either share the information with the UE using any NAS message, or may provide it to the RAN (for example, via NG interface signalling, for instance, part of the UE context procedures, INITIAL CONTEXT SETUP REQUEST message and/or UE CONTEXT MODIFICATION REQUEST message, or AMF CP RELOCATION INDICATION message, UE INFORMATION TRANSFER message , HANDOVER REQUEST message and/or PATH SWITCH REQUEST ACKNOWLEDGE message) where the RAN may in turn provide it to the UE using any RRC message (or system information).
  • the 5GC may behave as described above optionally for a UE which indicates capability to perform AI/ML (e.g. for the RAN). For instance, receiving an indication that the UE has capability to perform AI/ML (e.g., model training, inference etc.) triggers the 5GC to behave in accordance with one of the examples, embodiments, aspects etc. described above/herein.
  • AI/ML e.g., model training, inference etc.
  • the UE may establish a new PDU session for this purpose (e.g., for AI/ML), or may join an MBS session for AI/ML (optionally, when the UE is in the MBS service area e.g., based on the TAI), and receive the AI/ML data associated with a TMGI of interest.
  • a new PDU session for this purpose e.g., for AI/ML
  • the UE may join an MBS session for AI/ML (optionally, when the UE is in the MBS service area e.g., based on the TAI), and receive the AI/ML data associated with a TMGI of interest.
  • the UE may locally save the received data and use it for AI/ML accordingly.
  • the 5GC may leverage the AI/ML NF 100 to determine the best, or optimal, traffic delivery mode for the AI/ML traffic over the 5GC, namely 5GC Individual MBS traffic delivery or 5GC Shared MBS traffic delivery. As explained herein, these delivery modes differ in whether copies of each traffic packets are individually transmitted to each UE, or just a single copy is sent to RAN and shared with all UEs.
  • the AI/ML NF 100 may collect data regarding the delivery modes of past MBS traffic as well as the status on the network and provide a recommendation to the MBS system (e.g.
  • the AI/ML NF 100 may also indicate to the MBS system (e.g. MBSF and/or MBSTF and/or MB-SMF and/or MB-UPF) that a delivery mode switch is recommended (e.g. 5GC Individual MBS traffic delivery to 5GC Shared MBS traffic delivery or vice versa).
  • a delivery mode switch e.g. 5GC Individual MBS traffic delivery to 5GC Shared MBS traffic delivery or vice versa).
  • Certain examples of the present disclosure relate to using Local Area Data Network (LADN) PDU sessions to transfer AI/ML model (and/or associated AI/ML model information).
  • LADN Local Area Data Network
  • the network e.g., any network entity such as one of the non-limiting examples described above/herein deploys LADN in an area which can then transfer AI/ML to the UE when the UE establishes a PDU session for LADN.
  • the UE may explicitly request LADN information for AI/ML, where the request may contain an explicit indication (e.g., a new indication) that the LADN information requested is for AI/ML, or the UE may use a well-known AI/ML DNN to request LADN information for an LADN PDU session which may be used for AI/ML. For instance, the UE may request this information using NAS messages or RRC messages.
  • an explicit indication e.g., a new indication
  • the UE may use a well-known AI/ML DNN to request LADN information for an LADN PDU session which may be used for AI/ML. For instance, the UE may request this information using NAS messages or RRC messages.
  • the UE may receive LADN information for AI/ML, which may include a list of TAIs indicating where the LADN PDU session can be obtained for AI/ML. Once in this TAI, the UE may establish a PDU session for LADN for AI/ML and then obtain AI/ML data.
  • LADN PDU session may be considered to act as a local PDU session which is closer to the UE’s location, and hence may improve the overall latency in sharing the data to the UE.
  • the network may provide this information (i.e., LADN information) to the UE optionally if the UE indicates support for LADN AI/ML connection or for AI/ML, or based on subscription information.
  • the LADN information for AI/ML may be provided to the UE via NAS messages or RRC messages.
  • the network may provide this information to the UE optionally if the subscription information indicates that this is permissible (or required) for the UE in question, optionally where the subscription information may also contain a list of TAIs (or general location information) that may define where the UE need be in order to provide this information to the UE.
  • Figure 5 is a block diagram illustrating an exemplary network entity 200 (or electronic device, or network node etc.) that may be used in examples of the present disclosure.
  • a UE, device, network entity, network node, network function, network etc. as described in any of the embodiments/examples disclosed above may be implemented by or comprise network entity 200 (or be in combination with network entity 200).
  • AI/ML NF 100 may be implemented by or in combination with, or comprise, network entity 200.
  • the network entity 200 comprises a controller 205 (or at least one processor) and at least one of a transmitter 201, a receiver 203, or a transceiver (not shown).
  • controller 205 may be arranged to control the network entity 200 to perform any of the one or more features, operations or functions disclosed in relation to a network entity above;
  • transmitter 201 may be arranged to transmit any one or more of the information, signals, data etc. mentioned above;
  • receiver 203 may be arranged to receive any one or more of the information, signals, data etc. mentioned above.
  • the person skilled in the art would understand how such a network entity 200 in accordance with anyone or more example/embodiment disclosed herein may be provided.
  • FIG. 6 is a block diagram illustrating a structure of a UE according to an embodiment of the disclosure.
  • the UE may include a transceiver 610, a memory 620, and a processor 630.
  • the transceiver 610, the memory 620, and the processor 630 of the UE may operate according to a communication method of the UE described above.
  • the components of the UE are not limited thereto.
  • the UE may include more or fewer components than those described above.
  • the processor 630, the transceiver 610, and the memory 620 may be implemented as a single chip.
  • the processor 630 may include at least one processor.
  • the UE of FIGURE 6 corresponds to the UEs (103) of FIGS. 1, 3, 5.
  • the transceiver 610 collectively refers to a UE receiver and a UE transmitter, and may transmit/receive a signal to/from a base station or a network entity.
  • the signal transmitted or received to or from the base station or a network entity may include control information and data.
  • the transceiver 610 may include a RF transmitter for up-converting and amplifying a frequency of a transmitted signal, and a RF receiver for amplifying low-noise and down-converting a frequency of a received signal.
  • the transceiver 610 may receive and output, to the processor 630, a signal through a wireless channel, and transmit a signal output from the processor 630 through the wireless channel.
  • the memory 620 may store a program and data required for operations of the UE. Also, the memory 620 may store control information or data included in a signal obtained by the UE.
  • the memory 620 may be a storage medium, such as read-only memory (ROM), random access memory (RAM), a hard disk, a CD-ROM, and a DVD, or a combination of storage media.
  • the processor 630 may control a series of processes such that the UE operates as described above.
  • the transceiver 610 may receive a data signal including a control signal transmitted by the base station or the network entity, and the processor 630 may determine a result of receiving the control signal and the data signal transmitted by the base station or the network entity.
  • FIG. 7 is a block diagram illustrating a structure of a base station according to an embodiment of the disclosure.
  • the base station may include a transceiver 710, a memory 720, and a processor 730.
  • the transceiver 710, the memory 720, and the processor 730 of the base station may operate according to a communication method of the base station described above.
  • the components of the base station are not limited thereto.
  • the base station may include more or fewer components than those described above.
  • the processor 730, the transceiver 710, and the memory 720 may be implemented as a single chip.
  • the processor 730 may include at least one processor.
  • the transceiver 710 collectively refers to a base station receiver and a base station transmitter, and may transmit/receive a signal to/from a terminal (UE) or a network entity.
  • the signal transmitted or received to or from the terminal or a network entity may include control information and data.
  • the transceiver 710 may include a RF transmitter for up-converting and amplifying a frequency of a transmitted signal, and a RF receiver for amplifying low-noise and down-converting a frequency of a received signal.
  • the transceiver 710 may receive and output, to the processor 730, a signal through a wireless channel, and transmit a signal output from the processor 730 through the wireless channel.
  • the memory 720 may store a program and data required for operations of the base station. Also, the memory 720 may store control information or data included in a signal obtained by the base station.
  • the memory 720 may be a storage medium, such as read-only memory (ROM), random access memory (RAM), a hard disk, a CD-ROM, and a DVD, or a combination of storage media.
  • the processor 730 may control a series of processes such that the base station operates as described above.
  • the transceiver 710 may receive a data signal including a control signal transmitted by the terminal, and the processor 730 may determine a result of receiving the control signal and the data signal transmitted by the terminal.
  • FIG. 8 is a block diagram illustrating a structure of a network entity according to an embodiment of the disclosure.
  • the network entity of the present disclosure may include a transceiver 810, a memory 820, and a processor 830.
  • the transceiver 810, the memory 820, and the processor 830 of the network entity may operate according to a communication method of the network entity described above.
  • the components of the terminal are not limited thereto.
  • the network entity may include more or fewer components than those described above.
  • the processor 830, the transceiver 810, and the memory 820 may be implemented as a single chip.
  • the processor 830 may include at least one processor.
  • the network entity illustrated in FIG. 8 may correspond to the NFs (105) illustrated in FIGS. 1, 3, 5, 7, and 9.
  • the transceiver 810 collectively refers to a network entity receiver and a network entity transmitter, and may transmit/receive a signal to/from a base station or a UE.
  • the signal transmitted or received to or from the base station or the UE may include control information and data.
  • the transceiver 810 may include a RF transmitter for up-converting and amplifying a frequency of a transmitted signal, and a RF receiver for amplifying low-noise and down-converting a frequency of a received signal.
  • the transceiver 810 may receive and output, to the processor 830, a signal through a wireless channel, and transmit a signal output from the processor 830 through the wireless channel.
  • the memory 820 may store a program and data required for operations of the network entity. Also, the memory 820 may store control information or data included in a signal obtained by the network entity.
  • the memory 820 may be a storage medium, such as ROM, RAM, a hard disk, a CD-ROM, and a DVD, or a combination of storage media.
  • the processor 830 may control a series of processes such that the network entity operates as described above.
  • the transceiver 810 may receive a data signal including a control signal, and the processor 830 may determine a result of receiving the data signal.
  • a computer-readable recording medium having one or more programs (software modules) recorded thereon may be provided.
  • the one or more programs recorded on the computer-readable recording medium are configured to be executable by one or more processors in an electronic device.
  • the one or more programs include instructions to execute the methods according to the embodiments described in the claims or the detailed description of the present disclosure.
  • the programs may be stored in random access memory (RAM), non-volatile memory including flash memory, read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), a magnetic disc storage device, compact disc-ROM (CD-ROM), a digital versatile disc (DVD), another type of optical storage device, or a magnetic cassette.
  • RAM random access memory
  • ROM read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • CD-ROM compact disc-ROM
  • DVD digital versatile disc
  • the programs may be stored in a memory system including a combination of some or all of the above-mentioned memory devices.
  • each memory device may be included by a plural number.
  • the programs may also be stored in an attachable storage device which is accessible through a communication network such as the Internet, an intranet, a local area network (LAN), a wireless LAN (WLAN), or a storage area network (SAN), or a combination thereof.
  • the storage device may be connected through an external port to an apparatus according the embodiments of the present disclosure.
  • Another storage device on the communication network may also be connected to the apparatus performing the embodiments of the present disclosure.
  • the user equipment can include any number of each component in any suitable arrangement.
  • the figures do not limit the scope of this disclosure to any particular configuration(s).
  • figures illustrate operational environments in which various user equipment features disclosed in this patent document can be used, these features can be used in any other suitable system.
  • At least some of the example embodiments described herein may be constructed, partially or wholly, using dedicated special-purpose hardware.
  • Terms such as ‘component’, ‘module’ or ‘unit’ used herein may include, but are not limited to, a hardware device, such as circuitry in the form of discrete or integrated components, a Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuit (ASIC), which performs certain tasks or provides the associated functionality.
  • FPGA Field Programmable Gate Array
  • ASIC Application Specific Integrated Circuit
  • the described elements may be configured to reside on a tangible, persistent, addressable storage medium and may be configured to execute on one or more processors.
  • These functional elements may in some embodiments include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
  • components such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
  • components such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
  • references herein to the “network” may refer to at least one of the RAN, AMF, SMF, UDM, NEF, PCF, the new defined entity (non-limitingly termed AI/ML NF 100), or any other entity in the 5GC and is not limited to particular nodes/entities.
  • the network may be defined differently.
  • network signalling to provide any of the above-described information may, as non-limiting examples, include RRC or NAS messages or system information. It will be appreciated that, optionally, network entities may first share necessary information amongst them/each other and then a recipient entity may share the information with the UE using the appropriate signalling.
  • the proposals apply to at least LTE, NR, NR NTN or IoT NTN (note this list is merely to give some examples and should not be seen as limiting), including any related signalling/messages on any of the inferences X2, Xn, NG, S1, F1, etc (again, this list is merely to give some examples and should not be seen as limiting).
  • LTE Long Term Evolution
  • NR Long Term Evolution
  • NR NTN or IoT NTN
  • IoT NTN IoT NTN
  • Such an apparatus and/or system may be configured to perform a method according to any aspect, embodiment or example disclosed herein.
  • Such an apparatus may comprise one or more elements, for example one or more of receivers, transmitters, transceivers, processors, controllers, modules, units, and the like, each element configured to perform one or more corresponding processes, operations and/or method steps for implementing the techniques described herein.
  • an operation/function of X may be performed by a module configured to perform X (or an X-module).
  • the one or more elements may be implemented in the form of hardware, software, or any combination of hardware and software.
  • examples of the present disclosure may be implemented in the form of hardware, software or any combination of hardware and software. Any such software may be stored in the form of volatile or non-volatile storage, for example a storage device like a ROM, whether erasable or rewritable or not, or in the form of memory such as, for example, RAM, memory chips, device or integrated circuits or on an optically or magnetically readable medium such as, for example, a CD, DVD, magnetic disk or magnetic tape or the like.
  • volatile or non-volatile storage for example a storage device like a ROM, whether erasable or rewritable or not
  • memory such as, for example, RAM, memory chips, device or integrated circuits or on an optically or magnetically readable medium such as, for example, a CD, DVD, magnetic disk or magnetic tape or the like.
  • the storage devices and storage media are embodiments of machine-readable storage that are suitable for storing a program or programs comprising instructions that, when executed, implement certain examples of the present disclosure. Accordingly, certain examples provide a program comprising code for implementing a method, apparatus or system according to any example, embodiment and/or aspect disclosed herein, and/or a machine-readable storage storing such a program. Still further, such programs may be conveyed electronically via any medium, for example a communication signal carried over a wired or wireless connection.

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Abstract

La divulgation concerne un système de communication 5G ou 6G permettant de prendre en charge un débit supérieur de transmission de données. Selon un mode de réalisation, une première entité comprend : un émetteur ; un récepteur ; et un dispositif de commande. Le dispositif de commande est configuré pour : recevoir, en provenance d'une seconde entité, des premières informations ou une signalisation associées au transfert de données d'intelligence artificielle ou d'apprentissage machine (AI/ML) ; et transmettre des premières données d'IA/ML à la seconde entité à l'aide de services de diffusion ou multidiffusion (MBS) ou d'un réseau de données de zone locale (LADN), sur la base des premières informations ou de la signalisation.
PCT/KR2023/016188 2022-11-04 2023-10-18 Procédés et appareil de transfert de données ia/ml dans un système de communication sans fil WO2024096386A1 (fr)

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FUJITSU: "Discussions on sub use cases and spec impacts for AIML for positioning accuracy enhancement", 3GPP DRAFT; R1-2206169, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG1, no. Toulouse, France; 20220822 - 20220826, 12 August 2022 (2022-08-12), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France, XP052274104 *
INTERDIGITAL: "FS_AMMT – Updating of new requirements and KPIs for the use-case on real time media editing with on-board AI inference", 3GPP DRAFT; S1-204396, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. SA WG1, no. Electronic Meeting; 20201111 - 20201120, 20 November 2020 (2020-11-20), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France , XP051956805 *

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