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WO2024231363A1 - Procédé d'adaptation de modèle avancé pour réseau d'accès radio - Google Patents

Procédé d'adaptation de modèle avancé pour réseau d'accès radio Download PDF

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Publication number
WO2024231363A1
WO2024231363A1 PCT/EP2024/062499 EP2024062499W WO2024231363A1 WO 2024231363 A1 WO2024231363 A1 WO 2024231363A1 EP 2024062499 W EP2024062499 W EP 2024062499W WO 2024231363 A1 WO2024231363 A1 WO 2024231363A1
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WIPO (PCT)
Prior art keywords
model
models
sub
complexity
different
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Pending
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PCT/EP2024/062499
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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 WO2024231363A1 publication Critical patent/WO2024231363A1/fr
Anticipated expiration legal-status Critical
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Classifications

    • 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
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0681—Configuration of triggering conditions
    • 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
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0806—Configuration setting for initial configuration or provisioning, e.g. plug-and-play
    • 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
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0813—Configuration setting characterised by the conditions triggering a change of settings
    • H04L41/0816—Configuration setting characterised by the conditions triggering a change of settings the condition being an adaptation, e.g. in response to network events
    • 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
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0893—Assignment of logical groups to network elements
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/16—Threshold monitoring

Definitions

  • the present disclosure relates to AI/ML based model adaptation, where techniques for pre-configuring and signaling the specific information about mapping relationship information using association between models and other index values for the model adaptation are presented.
  • AI/ML artificial intelligence/machine learning
  • RP-213599 3GPP TSG RAN 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 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 framework will be one of key work scope.
  • AI/ML framework various aspects are under consideration for investigation and one of key items is about lifecycle management of AI/ML model 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 of scenarios for model training/inference is that both functions are located within RAN node.
  • AI Artificial Intelligence
  • ML Machine Learning
  • model transfer signaling for model adaptiation/switching by network side can be quite increased so as to adapt to different dynamic conditions associated with UE capability changes. Due to this, the applicable model(s) at UE can be unmatched with the reported UE capability as there is delay with legacy UE capability report.
  • EP3543917A1 describes the use of low-precision methods (i.e., methods that use low-precision weights) to train deep neural networks (DNNs).
  • W02020234902A1 describes a radio mapping architecture for applying Machine Learning techniques to wireless radio access networks.
  • W02022033804A1 describes splitting an AI/ML model into a plurality of sub-parts and forming a set of aggregation chunks.
  • US20090170552A1 describes a switching profile method for mobile device with detection of predetermined condition.
  • the described problem is solved the embodiments of his application.
  • the first aspect is a method of advanced model adaptation for radio access network of generating the pre-configured mapping relationship between applicable sub-model IDs with different threshold values, comprising, threshold values are pre-defined to match with activation triggering of different AI/ML sub-models, sub-model IDs are split from the common model ID, where sub-models can have lower model complexity/size compared with common model ID, the attribute data for threshold value calculation, ⁇ UE device resource specification, model configuration, site/scenario, application ⁇ , can be defined for different use case/application and implementation-specific environment together with considering UE capability, the listed sub-models can be dynamically configured with the pre-defined parameter set change information sent from network where the determined model ID can be macro model and the listed multiple sub-models can be micro models so that macro model is full featured model with highest complexity/size while micro model is partially featured model with lower complexity/size, depending on practical use case, the determined model ID itself can be one of the indexed sub-model, the size of mapping
  • the method is characterized by, that the UE monitors model operation and device resource status supporting it together so that the pre-configured threshold value is detected for triggering and when triggering is enabled with specific threshold value, the current model in operation is then switched to the associated sub-model with the matched threshold value autonomously.
  • the method is characterized by, that the network side provides the mapping relationship and triggering information with the associated configuration, comprising, threshold values are defined/generated based on attribute data, ⁇ UE device resource specification, model configuration, site/scenario, application ⁇ , different number of mapping relationships and triggering information can be formed for different use case/application and implementation-specific environment together with considering UE capability.
  • the method is characterized by that is a method of forming full model (fML) and partial models (pML) for radio access network, comprising, network generates two model categories such that fML is the original model having full feature set and pML is the simplified model having lower complexity and/or smaller feature set for applying any specific model(s) to UE.
  • fML and pML are pre-configured so that model complexity is lower and feature set size is smaller for partial models where model configuration parameters such as number of layers and feature input can be adjusted to determine a finite set of full-/partial-models. Selection of models from fML and pMLs depends on UE ML capacity status to match with target model operation so that any pMLs can be run on the reduced available resource at UE device.
  • Scalable model structure supports models with different complexity levels by using parameter configuration.
  • the method is characterized by different number of UE groups can be configured to operate the matched model(s) with the reported UE ML capability information.
  • the present disclosure relates to an apparatus for Method of advanced model adaptation for radio access network of generating the preconfigured mapping relationship between applicable sub-model IDs with different threshold values and of forming full model (fML) and partial models (pML)
  • the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of first and the second aspect.
  • the present disclosure relates to a user Equipment comprising an apparatus according to any one of the embodiments of the first and the second aspect.
  • the present disclosure relates to a base station user Equipment comprising an apparatus according to any one of the embodiments of the first and the second aspect.
  • the present disclosure relates to a wireless communication system, wherein the base-station (gNB) comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of first and the second aspect, and wherein the user equipment (UE) comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of first and the second aspect.
  • the base-station comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of first and the second aspect
  • UE user equipment
  • Figure 1 is an exemplary table of threshold-based mapping relationship.
  • Figure 2 is an exemplary signaling of autonomous sub-model switching by UE.
  • Figure 3 is a flow chart of mapping relationship configuration with triggering information by network side.
  • Figure 4 is a flow chart of processing autonomous sub-model switching by UE.
  • Figure 5 is an exemplary block diagram of using full-/partial-models.
  • 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.
  • the non-limiting term user equipment (UE) or wireless device may be used and may refer to any type of wireless device communicating with a network node and/or with another UE in a cellular or mobile communication system.
  • Examples of UE are target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communication, PDA, PAD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, UE category Ml, UE category M2, ProSe UE, V2V UE, V2X UE, etc.
  • D2D device to device
  • M2M machine to machine
  • PDA machine to machine
  • PAD machine to machine
  • Tablet mobile terminals
  • smart phone laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles
  • UE category Ml UE category M2
  • ProSe UE ProSe UE
  • V2V UE V2X UE
  • terminologies such as base station/gNodeB and UE should be considered non-limiting and do in particular 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
  • embodiments may be embodied as a system, apparatus, method, or program product. Accordingly, embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects.
  • 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 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 readonly 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).
  • 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.
  • the disclosure is related to wireless communication system, which may be for example a 5G NR wireless communication system. More specifically, it represents a RAN of the wireless communication system, which is used exchange data with UEs via radio signals. For example, the RAN may send data to the UEs (downlink, DL), for instance data received from a core network (CN). The RAN may also receive data from the UEs (uplink, UL), which data may be forwarded to the CN.
  • the RAN comprises one base station, BS. Of course, the RAN may comprise more than one BS to increase the coverage of the wireless communication system.
  • Each of these BSs may be referred to as NB, eNodeB (or eNB), gNodeB (or gNB, in the case of a 5G NR wireless communication system), an access point or the like, depending on the wireless communication standard(s) implemented.
  • the UEs are located in a coverage of the BS.
  • the coverage of the BS corresponds for example to the area in which UEs can decode a PDCCH transmitted by the BS.
  • An example of a wireless device suitable for implementing any method, discussed in the present disclosure, performed at a UE corresponds to an apparatus that provides wireless connectivity with the RAN of the wireless communication system, and that can be used to exchange data with said RAN.
  • a wireless device may be included in a UE.
  • the UE may for instance be a cellular phone, a wireless modem, a wireless communication device, a handheld device, a laptop computer, or the like.
  • the UE may also be an Internet of Things (loT) equipment, like a wireless camera, a smart sensor, a smart meter, smart glasses, a vehicle (manned or unmanned), a global positioning system device, etc., or any other equipment that may run applications that need to exchange data with remote recipients, via the wireless device.
  • LoT Internet of Things
  • the wireless device comprises one or more processors and one or more memories.
  • the one or more processors may include for instance a central processing unit (CPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.
  • the one or more memories may include any type of computer readable volatile and non-volatile memories (magnetic hard disk, solid-state disk, optical disk, electronic memory, etc.).
  • the one or more memories may store a computer program product, in the form of a set of programcode instructions to be executed by the one or more processors to implement all or part of the steps of a method for exchanging data, performed at a UE’s side, according to any one of the embodiments disclosed herein.
  • the wireless device can comprise also a main radio, MR, unit.
  • the MR unit corresponds to a main wireless communication unit of the wireless device, used for exchanging data with BSs of the RAN using radio signals.
  • the MR unit may implement one or more wireless communication protocols, and may for instance be a 3G, 4G, 5G, NR, WiFi, WiMax, etc. transceiver or the like.
  • the MR unit corresponds to a 5G NR wireless communication unit.
  • 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.
  • 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).
  • AI/ML model enabled wireless communication network When AI/ML model enabled wireless communication network is deployed, it is then important to consider how to handle AI/ML model/dataset transfer under operations such as model training, inference, monitoring, updating, etc.
  • AI/ML model When AI/ML model is transferred to UE, the transferred model cannot be fully operated if model size and/or complexity is higher than ML capability supported by UE. For example, there are many different levels of ML capabilities for each UEs.
  • the common model to be transferred can work well for some UEs and not for other UEs if minimum ML capability is not guaranteed for all UEs due to the limited device resource such as compute power/memory size/battery power consumption etc.
  • ML support capability at UE can be dynamically changed because applicable conditions such as scenarios/sites for any specific model/functionality are not static, but dynamic. Therefore, when UE ML capability is dynamic, the configured model operation with specific functionality can be unstable along with performance quality degradation depending on UE ML capability status change. Model transfer signaling by network side can be quite increased so as to adapt to different dynamic conditions associated with UE capability changes. Due to this, the applicable model(s) at UE can be unmatched with the reported UE capability as there is delay with legacy UE capability report.
  • the threshold-based mapping relationship between applicable sub-model IDs with different threshold values is pre- configured/provided by network side. For example, when a threshold value is triggered at UE during model operation, UE can autonomously switch between the pre-configured sub-models that is matched with the indicated threshold value.
  • the threshold-based mapping relationship with triggering configuration information is sent through system information or RRC signaling.
  • Figure 1 shows an exemplary table of threshold-based mapping relationship.
  • threshold values are pre-defined to match with activation triggering of different AI/ML sub-models.
  • Sub-model IDs are split from the common model ID, where submodels can have lower model complexity/size compared with common model ID.
  • Threshold values are pre-configured to reflect available resource of UE capability to run a model where each indexed sub-models can be properly performed based on the available UE capability resource according to indication of threshold values.
  • Threshold values e.g., derived from using attribute data, ⁇ UE device resource specification, model configuration, site/scenario, application ⁇
  • Threshold values can be defined for different use case/application and implementation-specific environment together with considering UE capability such as compute power, memory size, and battery power level, etc. where the common or standardized capability requirements to run models can be used to threshold value calculation although there are varying types of devices with model support capabilities.
  • this threshold-based mapping relationship need to be updated and sent to UE (e.g., through RRC signaling).
  • the listed sub-models can be dynamically configured with the pre-defined parameter set change information sent from network where the determined model ID can be macro model and the listed multiple submodels can be micro models so that macro model is full featured model with highest complexity/size while micro model is partially featured model with lower complexity/size.
  • the determined model ID itself can be one of the indexed sub-model. Therefore, any separate model transfers of each sub-models are not needed where any sub-models can be dynamically switched at UE side based on the pre-defined parameter set change information.
  • the size of mapping table or number of sub-models can be adapted to the specific model operation applications and/or environment.
  • Figure 2 shows an exemplary signaling of autonomous sub-model switching by UE.
  • UE can autonomously switch/adapt models based on mapping table and the preconfiguration information about sub-models with triggering rule.
  • UE sends ML capability status and model switching updates. This example is based on event-triggered method for model switching/adaptation. However, using the pre-configured sub-models periodically model switching/adaptation can be made as well.
  • Figure 3 shows a flow chart of mapping relationship configuration with triggering information by network side.
  • the mapping relationship and triggering information are configured together.
  • Threshold values are pre-defined to match with activation triggering values of different AI/ML sub-models.
  • Threshold values are pre-configured to reflect available resource of UE capability to run a model where each indexed sub-models can be properly performed based on the available UE capability resource according to indication of threshold values.
  • Threshold values e.g., derived from using attribute data, ⁇ UE device resource specification, model configuration, site/scenario, application ⁇
  • Figure 4 shows a flow chart of processing autonomous sub-model switching by UE. After receiving mapping relationship configuration with triggering information, UE monitors model operation and device resource status supporting it together so that the pre-configured threshold value is detected for triggering. When triggering is enabled with specific threshold value, the current model in operation is then switched to the associated sub-model with the matched threshold value.
  • Figure 5 shows an exemplary block diagram of using full-/partial-models.
  • fML full model
  • pML partial models
  • network When applying any specific model(s) to UE, network generates two model categories such that fML is the original model having full feature set and pML is the simplified model having lower complexity and/or smaller feature set. Therefore, the key differences between fML and pML are that model complexity is lower and feature set size is smaller for partial models where model configuration parameters such as number of layers and feature input can be adjusted to determine a finite set of full-Zpartial- models.
  • Scalable model structure then supports models with different complexity levels by using parameter configuration in this example.
  • two or more groups of UEs can be also determined such as UE group #1 that can support full model transfer and UE group #2 that can support partial model transfer that are matched with the reported UE ML capability information.
  • Number of UE groups can be configurable. Therefore, UE grouping-based model adaptation can be applicable based on scalable model set.

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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

La présente divulgation concerne des procédés d'adaptation/commutation de modèle pour un modèle basé sur IA/ML dans un système de communication mobile sans fil comprenant une station de base (par exemple, gNB) et une station mobile (par exemple, UE). Une relation de mappage basée sur un seuil est configurée pour permettre une adaptation de modèle à l'aide d'informations de déclenchement en fonction de changements dynamiques d'environnement d'opération de modèle au niveau du côté dispositif.
PCT/EP2024/062499 2023-05-11 2024-05-07 Procédé d'adaptation de modèle avancé pour réseau d'accès radio Pending WO2024231363A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090170552A1 (en) 2007-12-31 2009-07-02 Jian-Liang Lin Method of switching profiles and related mobile device
EP3543917A1 (fr) 2018-03-19 2019-09-25 SRI International Inc. Adaptation dynamique de réseaux neuronaux profonds
WO2020234902A1 (fr) 2019-05-20 2020-11-26 Saankhya Labs Pvt. Ltd. Architecture de mappage radio permettant d'appliquer des techniques d'apprentissage machine à des réseaux d'accès radio sans fil
WO2022033804A1 (fr) 2020-08-10 2022-02-17 Interdigital Ce Patent Holdings, Sas Inférence de modèles ai/ml tranche par tranche sur des réseaux de communication
WO2022223499A1 (fr) * 2021-04-20 2022-10-27 Interdigital Ce Intermediate, Sas Distribution de modèle ai/ml basée sur un manifeste de réseau
WO2023056580A1 (fr) * 2021-10-06 2023-04-13 Qualcomm Incorporated Surveillance de messages qui indiquent une commutation entre des groupes de modèles d'apprentissage machine (ml)

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090170552A1 (en) 2007-12-31 2009-07-02 Jian-Liang Lin Method of switching profiles and related mobile device
EP3543917A1 (fr) 2018-03-19 2019-09-25 SRI International Inc. Adaptation dynamique de réseaux neuronaux profonds
WO2020234902A1 (fr) 2019-05-20 2020-11-26 Saankhya Labs Pvt. Ltd. Architecture de mappage radio permettant d'appliquer des techniques d'apprentissage machine à des réseaux d'accès radio sans fil
WO2022033804A1 (fr) 2020-08-10 2022-02-17 Interdigital Ce Patent Holdings, Sas Inférence de modèles ai/ml tranche par tranche sur des réseaux de communication
WO2022223499A1 (fr) * 2021-04-20 2022-10-27 Interdigital Ce Intermediate, Sas Distribution de modèle ai/ml basée sur un manifeste de réseau
WO2023056580A1 (fr) * 2021-10-06 2023-04-13 Qualcomm Incorporated Surveillance de messages qui indiquent une commutation entre des groupes de modèles d'apprentissage machine (ml)

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