Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements
  • H04W24/02—Arrangements for optimising operational condition
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/14—Network analysis or design
  • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W8/00—Network data management
  • H04W8/22—Processing or transfer of terminal data, e.g. status or physical capabilities
  • H04W8/24—Transfer of terminal data

Definitions

• Certain examples of the present disclosure provide one or more techniques for supporting Artificial Intelligence/Machine Learning (AI/ML) model life cycle management in wireless communication networks.
• AI/ML Artificial Intelligence/Machine Learning
• certain examples of the present disclosure provide one or more techniques for registering, selecting, updating, configuring, and subscribing to AI/ML models in 3 rd Generation Partnership Project (3GPP) 5G or 6G networks.
• 3GPP 3 rd Generation Partnership Project
• 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
• terahertz bands for example, 95GHz to 3THz bands
• 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
• AI/ML model/functionality life cycle management by a management entity in a mobile communications network, the method comprising: receiving, from a first entity, a request to register an AI/ML model/functionality, the request including metadata related to the AI/ML model/functionality; assigning an identifier (ID) to the AI/ML model/functionality; and storing information on the AI/ML model/functionality and the related metadata
• the metadata related to the AI/ML model/functionality includes information on one or more of a performance indicator, a use-case, a model/functionality input, a model/functionality output, accuracy metrics, a number of layers, a number of parameters, a model/functionality format, a model/functionality scenario, a model/functionality site, version number, model deployment side, model training side, model inference side, model training online or offline, model training type, model size, model format, performance metrics, single or multiple functionality model, and open or proprietary-format model.
• the ID includes at least one of a global model ID, a local model ID, a global functionality ID, and a local functionality ID.
• the stored information on the AI/ML model/functionality includes information on one or more of model/functionality ID, model architecture, model weights, validity, registration time, and registration location.
• the storing comprises storing the information on the AI/ML model/functionality and the related metadata in a model/functionality template or profile.
• the method further comprises transmitting, to the first entity, a response to the request to register an AI/ML model/functionality indicating a success or a failure of the request to register an AI/ML model/functionality.
• the response indicates that the request to register an AI/ML model/functionality has been successful.
• the response indicates that the request to register an AI/ML model/functionality has failed.
• the response that indicates that the request to register an AI/ML model/functionality has failed includes an indication of a failure cause, the failure cause including one or more of registration not supported or registration not accepted.
• the storing includes storing the AI/ML model/functionality.
• the method further comprises receiving, from the first entity, an update message for the AI/ML model/functionality and updating the AI/ML model/functionality based on the update message.
• the method further comprises transmitting, to the first entity, a response to the update message indicating a success or a failure of the updating of the AI/ML model/functionality.
• the response to the update message indicates that the update of the AI/ML model/functionality has been successful.
• the response to the update message indicates that the update of the AI/ML model/functionality has been unsuccessful.
• the response that indicates that the request to update the AI/ML model/functionality is unsuccessful includes an indication of a failure cause, the failure cause including one or more of update is not allowed, update is not supported, and the model/functionality is not supported.
• the method further comprises: receiving, from the first entity, an AI/ML model/functionality selection request including selection information; identifying a registered AI/ML model/functionality based on the selection information; and transmitting, to the first entity, an AI/ML model/functionality selection response including information on the identified AI/ML model/functionality.
• the information on the identified AI/ML model/functionality includes one or more of an ID of the identified AI/ML model/functionality, a profile of the identified AI/ML model/functionality, and a validation value of the identified AI/ML model/functionality.
• the selection request includes one or more of an AI/ML model/functionality description, an AI/ML model/functionality ID, a validation criteria, an AI/ML model/functionality type, an AI/ML model/functionality training type, an AI/ML model/functionality deployment type, and an aggregate evaluation function.
• the management entity is an AI/ML model/functionality LCM entity.
• the first entity is a client entity, a network entity (e.g. RAN, CN, other internal/external entity), a network function, a UE, a server, an OAM, and/or an AF.
• a network entity e.g. RAN, CN, other internal/external entity
• a network function e.g. a UE, a server, an OAM, and/or an AF.
• the management entity is implemented in one or more of a client entity, a network entity (e.g. RAN, CN, other internal/external entity), a network function, a UE, a server, an OAM, and/or an AF.
• a network entity e.g. RAN, CN, other internal/external entity
• a network function e.g. a UE, a server, an OAM, and/or an AF.
• the artificial intelligence/machine learning (AI/ML) model/functionality life cycle management (LCM) is model-based or functionality-based.
• a management entity for artificial intelligence/machine learning (AI/ML) model/functionality life cycle management (LCM) in a mobile communications network configured to: receive, from a first entity, a request to register an AI/ML model/functionality, the request including metadata related to the AI/ML model/functionality; assign an identifier (ID) to the AI/ML model/functionality; and store information on the AI/ML model/functionality and the related metadata.
• AI/ML artificial intelligence/machine learning
• LCM life cycle management
• Embodiments of the present disclosure provides methods and apparatus for AI/ML model/functionality lifecycle management including registration, selection and update of model or functionality.
• Figure 1 provides an overview of MLOps processes for end-to-end lifecycle management.
• Figure 2 provides an example of a new model /functionality registration procedure (Class 1) for a successful operation in accordance with the present disclosure.
• Figure 3 provides an example of a new model /functionality registration procedure (Class 1) for an unsuccessful operation in accordance with the present disclosure.
• Figure 4 provides an example of a new model or functionality registration procedure (Class 2) in accordance with the present disclosure.
• Figure 5 provides an example of a new model /functionality update procedure for a successful operation in accordance with the present disclosure.
• Figure 6 provides an example of a new model /functionality update procedure for an unsuccessful operation in accordance with the present disclosure.
• Figure 7 provides an example of a new model /functionality update procedure (Class 2) in accordance with the present disclosure.
• Figure 8 provides an example of a new model/functionality update procedure for a successful operation in accordance with the present disclosure.
• Figure 9 provides an example of a new model/functionality modify procedure for a successful operation in accordance with the present disclosure.
• Figure 10 provides an example of a new model /functionality selection procedure for a successful operation in accordance with the present disclosure.
• Figure 11 provides an example of model /functionality selection procedure for an unsuccessful operation in accordance with the present disclosure.
• Figure 12 is a block diagram of an exemplary entity that may be used in certain examples of the present disclosure.
• 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 provide one or more techniques for managing AI/ML models in wireless communications networks.
• certain examples of the present disclosure provide one or more techniques for managing AI/ML models in a 3GPP 5G and 6G networks.
• the present invention is not limited to these examples, 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, including any existing or future releases of the same standards specification.
• a particular 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 or entities may be omitted in certain examples.
• ⁇ Information carried by a particular message in one example may be carried by two or more separate messages in an alternative example.
• ⁇ Information carried by two or more separate messages in one example may be carried by a single message in an alternative example.
• the present disclosure introduces a new entity/function that is located internal or external to the network and/or UE(s) and/or server or Application Function (AF).
• the proposed entity will handle/manage various model LCM procedures in wireless networks.
• the new entity may be termed, for example, Model Learning Controller (MLC), an AI/ML model coordinating entity, AI/ML [model] manager, AI/ML controller, or any other suitable term.
• MLC Model Learning Controller
• AI/ML [model] manager AI/ML controller
• the proposed entity could be included in (or part of) any network entity/function (internal or external), and/or UE(s), and/or server, and/or AF.
• the MLC could be part of RAN, CN (e.g. AMF, SMF, UPF, UDM, and/or any other internal or external Network entity/function) or OTT server.
• the functionality may also be distributed in some examples.
• MLC handles the AI/ML model registration, model subscription, model selection, model update, model configuration, in addition to model transfer/delivery between the network entities/functions and/or UE(s).
• E-UTRAN Evolved Universal Terrestrial Radio Access Network
• 3GPP working groups are studying Artificial Intelligence/Machine Learning (AI/ML) and its use in 3GPP systems.
• 3GPP agreed " Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface " [2].
• Initial set of use cases includes: CSI feedback enhancement, Beam management and Positioning accuracy enhancements.
• Protocol aspects related to, e.g., capability indication, configuration and control procedures (training/inference), and management of data and AI/ML model.
• capability indication e.g., capability indication, configuration and control procedures (training/inference), and management of data and AI/ML model.
• configuration and control procedures e.g., training/inference
• management of data and AI/ML model e.g., management of data and AI/ML model.
• collaboration level specific specification impact per use case.
• Terminology is to be defined.
• Terminology is to be defined. This includes model fine tuning, retraining, and re-development via online/offline training.
• FFS Detailed discussion of model ID with associated information and/or model functionality.
• FFS usage of model ID with associated information and/or model functionality based LCM procedure
• Data collection may be performed for different purposes in LCM, e.g., model training, model inference, model monitoring, model selection, model update, etc. each may be done with different requirements and potential specification impact.
• Model selection refers to the selection of an AI/ML model among models for the same functionality. (Exact terminology to be discussed/defined)
• Model selection refers to the selection of an AI/ML model among models for the same functionality. (Exact terminology to be discussed/defined)
• Study AI/ML model monitoring for at least the following purposes: model activation, deactivation, selection, switching, fallback, and update (including re-training).
• Model selection refers to the selection of an AI/ML model among models for the same functionality. (Exact terminology to be discussed/defined)
• UE may have one AI/ML model for the functionality, or UE may have multiple AI/ML models for the functionality.
• Model identification A process/method of identifying an AI/ML model for the common understanding between the NW and the UENote: The process/method of model identification may or may not be applicable. Note: Information regarding the AI/ML model may be shared during model identification.
• Terminology Description Functionality identification A process/method of identifying an AI/ML functionality for the common understanding between the NW and the UENote: Information regarding the AI/ML functionality may be shared during functionality identification.
• FFS granularity of functionality
• Model update Process of updating the model parameters and/or model structure of a model Model parameter update Process of updating the model parameters of a model
• MLOps is a system of processes for the end-to-end AI/ML lifecycle management at scale. These processes ensure that a model can be scaled for a large user base and perform accurately.
• the MLOps processes can be split into three categories [9], as shown in Figure 1.
• This category involves data collection, data analysis, data validation, feature engineering and data splitting .
• the data is prepared for an AI/ML task, where it applies data pre-processing and feature engineering and split the data into training, validation, and test datasets to solves a target task.
• This category involves model configuration, model training and model validation .
• the data scientist implements different algorithms with the prepared data to train various ML models.
• you subject the implemented algorithms to hyper-parameter tuning to get the best performing ML model.
• the output of this step is a trained model.
• This category involves model deploying, model serving, model monitoring and model re-training .
• the validated model is deployed to a production environment to serve predictions.
• the model deployment can be REST API micro service to serve online predictions or an embedded model to an edge or mobile device.
• the model predictive performance is monitored to potentially invoke a new iteration in the ML process.
• the AI/ML model exchange between the network and the UE could be needed, for example, based on:
• collaboration Level z may require model transfer between the network and UE to support inference and/or training at the network and/or the UE.
• model use case or model functionality for example, the model is used for one functionality or multiple models are used for the same functionality. Moreover, whether the models are scenario-, site-, or configuration-specific. This could mean the need to exchange the model several times between the network and the UE.
• model lifecycle (LCM) management procedures for example, model training, update, and fine-tuning, re-training, other LCM procedures.
• model transfer during model training e.g. joint training in the case of two-sided model training (between the UE and the network).
• model transfer/delivery and model LCM procedures may result in increased signaling overhead, power consumption and resource usage due to introducing and handling AI/ML related operations in wireless networks.
• the present disclosure provides solutions to introduce AI/ML operations into wireless networks and address these challenges related to AI/ML model lifecycle management.
• the following aspects are considered in this disclosure:
• the proposed approaches may also apply for the gNB, NG-RAN case and all related RRC signaling and/or messages, and X2, Xn, S1, NG, F1, E1, and/or other interfaces signaling and message, and/or other related network entities (e.g. MME, UPF, SMF, AMF, other), in addition to possible interaction with NWDAF (for analytics and predictions purposes).
• gNB NG-RAN case and all related RRC signaling and/or messages
• NWDAF for analytics and predictions purposes.
• All proposals, examples, and/or Figures may use, existing and/or new signalling/messaging/IEs/ procedures (e.g. RRC/NAS, NG, S1, Xn, X2, F1, E1, system information (periodic and/or on-demand), and/or other type of signalling between any network entity/function and the UE(s).
• the term “Client” is used to refer to any network entity (RAN, CN, other internal/external entity or function), UE, server, or AF.
• Step 1 The client triggers the model (or functionality) registration procedure for an AI/ML model for given/specific functionality(s) (or use case(s)/scenario(s)/ configuration(s)/site(s)) by sending a model registration request message, including one or more of any available assistance information related to the model (e.g. model functionality/use-case/scenario/site, version number, model deployment side, model training side, model inference side, model training online or offline, model training type, model size, model format, number of parameters, model input/output, accuracy metrics, performance metrics, KPIs, single or multiple functionality model, open- or proprietary-format model, number of layers, other).
• model functionality/use-case/scenario/site version number
• model deployment side model training side
• model inference side model training online or offline
• model training type model size
• model format number of parameters
• model input/output accuracy metrics
• performance metrics KPIs
• Step 2a the MLC assigns a model ID (local ID or global ID or temporary ID) and/or functionality ID (local ID or global ID or temporary ID) for the registered model and stores this model and/or model information (e.g. in the MLC database).
• the MLC may identify the model in relation to its specific functionality (or use-case/scenario/configuration/site information).
• the MLC may store information on one or more of the model architecture, weights and metadata (and other model related information) and/or other information related to the model (e.g. received in the client model registration request) into a model template (e.g. namely, Model Profile (MP)) and/or a functionality template (e.g. namely, Functionality Profile (FP)). Also, additionally, the MLC may store the model and/or model information with time and location information (e.g. time and/or location of model registration request, validity of time and/or location of stored or registered model and/or model information).
• a model template e.g. namely, Model Profile (MP)
• FP Functionality Profile
• time and location information e.g. time and/or location of model registration request, validity of time and/or location of stored or registered model and/or model information.
• Step 3 the MLC acknowledges the model registration success or model registration failure to the client. Additionally, the MLC may provide assistance information related to the registered model/functionality (e.g. validity information on usage of the model /functionality and model /functionality information).
• assistance information related to the registered model/functionality (e.g. validity information on usage of the model /functionality and model /functionality information).
• the model registration procedure may be defined as a new class 1 procedure, using new and/or existing messages and/or IEs, or may be defined using existing class 1 procedures, using new and/or existing messages and/or IEs.
• the model registration procedure maybe triggered based on a client request and the registration request (success) is acknowledged by the registration entity.
• FIG. 2 shows an example of a new class 1 Model Registration (or Functionality Registration) procedure, in which a Client sends a MODEL / FUNCTIONALITY REGISTRATION REQUEST message, including information on Model Profile (MP) and/or Functionality (FP) and/or other information related to the model and/or functionality registration request.
• the MLC may acknowledge a successful model (and/or functionality) registration, for example, as shown in steps above (i.e. Step 1, Step 2a, 2b, and Step3).
• the MLC responds to the client request using, for example, namely MODEL / FUNCTIONALITY REGISTRATION RESPONSE or MODEL / FUNCTIONALITY REGISTRATION REQUEST ACKNOWLEDGE message.
• the MLC may provide assistance information related to the registered model and/or functionality.
• bracketed annotations are providing examples of the content of these messages such that the messages are not limited to such content.
• the messages may include none, some or all of the example content and/or additional content described in the relevant portions of the detailed description.
• the Client may perform a new class 1 Functionality Registration Procedure, in which functionality is defined in relation to a given model or multiple models, or as a generic functionality or as functionality(s) defined separately to a model(s).
• a new class 1 Functionality Registration Procedure in which functionality is defined in relation to a given model or multiple models, or as a generic functionality or as functionality(s) defined separately to a model(s).
• FUNCTIONALITY REGISTRATION REQUEST /RESPONSE or FUNCTIONALITY REGISTRATION REQUEST ACKHOWLEDGE messages see example in Figure 3.
• model (or functionality) registration procedure may be defined as a new class 1 procedure, using new and/or existing messages and/or IEs, or defined using existing class 1 procedures, using new and/or existing messages and/or IEs.
• the model registration procedure (or functionality registration procedure) may be triggered based on a client request and the registration request failure maybe acknowledged to this client by the registration entity.
• the model /functionality registration entity may reply to the client model registration request with a model /functionality registration failure and optionally may indicate to the client the failure reason/cause, using a newly defined appropriate cause value, for example, Cause IE "Registration not supported”, “Registration not accepted”, or any other suitable naming.
• the model /functionality registration entity may reject model /functionality registration request based on information (e.g. subscription information) received from the UDM, or another network entity/function, and/or server or via AF or OAM.
• information e.g. subscription information
• model or functionality registration procedure may be defined as a new class 2 procedure, using new and/or existing messages and/or IEs, or defined using existing class 2 procedures, using new and/or existing messages and/or IEs.
• model or functionality registration procedure maybe triggered based on a client request and/or other assistance information from the network. See example in Figure 4.
• a model may be registered together with/for a given functionality/scenario/use-case/configuration/site or together with/for multiple functionalities/use-cases/scenarios/sites/configurations.
• a functionality may be registered together with/for a given model or multiple models.
• a functionality maybe registered for use with one model or multiple models.
• Step 1 The Client initiates the AI/ML model update procedure by sending, for example, a MODEL / FUNCTIONALITY UPDATE message.
• This message may include information on updated MP(s) or FP(s) or any model related information.
• the MLC may update the stored model information, using the received updated MP(s) and/or FP(s) information (e.g. information provided under lists of MP(s) and/or FP(s)). That is, the MLC may store the MP(s) and/or FP(s) information or update it if already stored.
• the MLC may store the MP(s) and/or FP(s) information or update it if already stored.
• the MLC may ignore any update to the AI/ML model if not meeting a pre-defined criteria and/or conditions (e.g. MP(s) and/or FP(s) version is/are not valid or outdated).
• a pre-defined criteria and/or conditions e.g. MP(s) and/or FP(s) version is/are not valid or outdated.
• the MLC may update all or part of the stored model information.
• Step 2 The MLC may reply to client, for example, by sending a MODEL / FUNCTIONALITY UPDATE ACKNOWLEDGE message to acknowledge that it has successfully updated the model and/or model information. Additionally, the MLC may provide information on whether all or part of model information is updated and/or any information related to the update model. For example, MLC may indicate to the client that only information related to a given functionality/use case/scenario/configuration is updated.
• Figure 5 shows an example of a new model /functionality update procedure described in above steps.
• the MLC may not accept the model update, fully or partially, and may respond, for example, with a MODEL / FUNCTIONALITY UPDATE FAILURE OR a MODEL / FUNCTIONALITY UPDATE REJECT message and appropriate cause value (e.g. Update is not allowed, Update is not supported, Model/Functionality update not supported, or any other suitable naming).
• Figure 6 shows an example of a new model /functionality update failure procedure.
• the model update procedure may be defined as a new class 2 procedure, using new and/or existing messages and/or IEs, or defined using existing class 2 procedures, using new and/or existing messages and/or IEs.
• the model update procedure may be triggered based on a client request and/or other assistance information from the network.
• Figure 7 shows an example of a new model /functionality update Class 2 procedure.
• the MLC following reception of MODEL / FUNCTIONALITY UDPATE message, from the Client, may update the model and/or functionality, based on information included in the MODEL / FUNCTIONALITY UPDATE message, for example, MP(s), FP(s), other model related information, and/or other assistance information provided by the network.
• information included in the MODEL / FUNCTIONALITY UPDATE message for example, MP(s), FP(s), other model related information, and/or other assistance information provided by the network.
• the MLC may ignore the request for model update, if the model update information is missing or not appropriate (e.g. does not meet a pre-defined conditions or criteria).
• the MLC may or may not inform the client of ignoring model update request.
• the MLC may initiate the Model update procedure towards the Client, or group of Clients that are involved in this model.
• all signalling /messaging defined in above and following examples may be re-used with suitable modification of naming and signalling direction.
• the MLC notify the Client or group of Client involved in a model, that this model was modified, e.g. by a Client(s), involved with this model and/or the MLC, as shown in Figure 8.
• each Client may trigger Model update procedure, shown in Figures 5, 6, and 7.
• the MLC may trigger the model update procedure using a new procedure (class 1 or class 2).
• Figure 8 shows an example of a new model/functionality update notification procedure (class 2).
• Model update procedures/signalling/message exchange between the client and the MLC, described above, may be assigned a (unique) procedure ID, for example, by the entity (e.g. Client and/or MLC) triggering procedure/signalling/message.
• entity e.g. Client and/or MLC
• the MLP or Client may subscribe to any changes/updates of a given model, list of models, functionality and/or list of functionalities and/or any other modification/update in model(s) and/or functionality (ies) at the Client or MLP.
• entity (Client or MLP) that receives the subscription message for model and/or functionality modification/change/update will inform the other entity (MLP or Client) of any changes/modification/updates to the model(s) and/or functionality(ies).
• the Client request model update or model modification, by sending, for example, MODEL MODIFY INDICATION message (or FUNCTIONALTY MODIFY INDICATION message) to the MLC (or the Client(s)).
• MODEL MODIFY INDICATION message or FUNCTIONALTY MODIFY INDICATION message
• the MLC or the Client(s)
• the MLC for each model (part of model, or functionality) indicated in this message, update the indicated model (part of model, or functionality) based on model and/or functionality information included in the MODEL MODIFY INDICATION message.
• MODEL MODIFY INDICATION message may include a list of model (and/or functionalities) that are to be updated or modified, e.g. Model Modify List IE, Functionality Modify List IE, or a combined list of models and functionalities.
• the list(s) may include other model and/or functionality related information (e.g. MP(s), FP(s), other).
• the MLC reports to the Client(s) in MODEL MODIFY CONFIRM message (or FUNCTIONALTY MODFY CONFIRM message) the result of modification for each model (part of model, and/or functionality) listed in MODEL MODIFY INDICATION message:
• Model Modify Confirm List IE (or Functionality Modify Confirm List IE) may be included containing:
• Figure 9 shows an example of a new MODEL / FUNCTIONALITY MODIFY procedure.
• Step 1 The Client triggers a model selection process by sending a model (or functionality) selection request to MLC (e.g. MODEL / FUNCTIONALITY SELECTION REQUEST message).
• MLC e.g. MODEL / FUNCTIONALITY SELECTION REQUEST message
• This request may include information related to model and/or model functionality/use-case/scenario/configuration. For example:
• Model description (list of model functionalities (e.g. Functionality ID, Functionality Type), other information).
• Other assistance information from the network related to model selection e.g. pre-configured information, based on PLMN rules, operator rules, country regulations, and/or client subscription information
• information from interaction with NWDAF e.g. pre-configured information, based on PLMN rules, operator rules, country regulations, and/or client subscription information
• the model (or functionality) selection request may include pre-defined Aggregate Function(s) .
• the aggregate function performs a calculation on a set of MP(s) (and/or FP(s)) that fulfil the selection information in the model selection request. For example:
• o Best Evaluation Function return the MP (and/or FP) that has the best Evaluation Criteria.
• o Average Function averaging all the model weights of MP(s) (and/or FP(s)) and return an average model. For example, MP(s) and/or FP(s) received from several Clients in the network.
• o Neighbour Selection Function return the MP(s) (and/or FP(s)) of the cell neighbours only.
• o Limit number of MPs (and/or FPs) returned to the Client For example, define and use a maximum number on MPs (and/or FPs) returned to the Client, and/or information related to the aggregated MPs (and of FPs).
• Step 2 The MLC sends to the client, if available, the selected MP(s) (and/or FP(s)) that fulfil the selection information in the model selection request.
• the selected MP(s) could be included in a new message MODEL / FUNCTIONALITY SELECTION RESPONSE or MODEL / FUNCTIONALITY SELECTION REQUEST ACKNOWLEDGE.
• the MLC may also send the following information (e.g. in new or existing IE):
• the MP(s) and/or FP(s) (and/or any other information related to selected model/model functionality) that has the best validation value (i.e. based on Validation Criteria).
• the MLC may aggregate two or more models that fulfil the model selection conditions/criteria and send to the aggregated model to the Client.
• the MLC may send a list of all models, part of the models, or aggregate model to the Client.
• the MLC may send the criteria used for model aggregation and/or model ID(s) for aggregated models.
• the MLC may send information (e.g. list of models, model ID(s)) on models that was not aggregated and/or not selected (e.g. failed the selection process).
• the MLC may include the reason for model selection failure (e.g. Model ID X, Validation Criteria ⁇ 95%).
• Figure 10 shows an example of a new model /functionality selection procedure (successful operation).
• the MLC may not accept the model (or functionality) selection request and may respond, for example, with a MODEL / FUNCTIONALITY SELECTION FAILURE or MODEL / FUNCTIONALITY SELECTION REJECT message and appropriate cause value (e.g. selection is not allowed, selection is not supported, model selection not supported, functionality selection not supported, or any other suitable naming).
• Figure 11 shows an example of a new model /functionality selection procedure (unsuccessful operation).
• the model /functionality selection procedure maybe defined as a new class 2 procedure, using new and/or existing messages and/or IEs, or defined using existing class 2 procedures, using new and/or existing messages and/or IEs.
• the model /functionality selection procedure maybe triggered based on a client request and/or other assistance information from the network.
• the MLC may trigger the model and/or functionality selection request at the Client side.
• the Client has multiple models for a given functionality or multiple functionalities, and the MLC may request selection of a given model for usage with a given functionality or more than one functionality.
• the MLC may include in the selection request, information on model(s) and/or functionality(ies) that the MLC require the Client to select.
• the MLC may also include in the selection request instruction for the Client to process the selected model and/or functionality (e.g. activate, deactivate, switch, other model and/or functionality LCM procedures).
• the MLC may include, if available, in the selection request, information related to the MP(s) and/or FP(s) of the model(s) and/or functionality to be selected and processed at the Client.
• the MLC may send the selection request independently to the model processing request to the client. That is, the MLC may send the model/functionality selection request and model/functionality processing request messages, separately in the same or different procedures. Additionally, the request messages may be sent in parallel or sequentially (e.g. step 1: model/functionality selection request, step 2: process model/functionality request).
• the Client following reception of model selection request from the MLC, may select the model and/or functionality, based on information included in the model (or functionality) selection message, for example, list of MP(s), FP(s), other model related information, and/or other assistance information provided by the network.
• the MLC may ignore the model /functionality selection request, if the model /functionality selection information is missing, incomplete, or not appropriate (e.g. does not meet a pre-defined conditions or criteria).
• the MLC (or Client) may or may not inform the client (or MLC) of ignoring (or cause for ignoring a model /functionality) selection request.
• Step 1 The Client triggers a model subscription process by sending a model (and/or functionality) subscription request to the MLC, for example, MODEL / FUNCTIONALITY SUBSCRIPTION REQUEST message.
• This request may include information related to one or more of a given model(s) and/or model functionality (ies)/use-case(s)/scenario(s)/configuration(s). For example:
• Model description (list of model functionalities (e.g. Functionality ID, Functionality Type), other information), and/or Model ID (local, or global, or temporary ID).
• model functionalities e.g. Functionality ID, Functionality Type
• Model ID local, or global, or temporary ID
• Step 2 The MLC creates an event trigger for the selected MP(s) (and/or FP(s)) that fulfil the subscription information in the model (and/or functionality) subscription request.
• the selected MP(s) could be included in a new message MODEL/ FUNCTIONALITY SUBSCRIPTION RESPONSE or MODEL / FUNCTIONALITY SUBSCRIPTION REQUEST ACKNOWLEDGE.
• Step 3 The MLC monitors any change in the subscribed MP(s) (and/or FP(s)) and send the changes to the Client.
• the MLC may not accept the model and/or functionality subscription request, and may respond, for example, with a MODEL / FUNCTIONALITY SUBSCRIPTION FAILURE or MODEL / FUNCTIONALITY SUBSCRIPTION REJECT message and an appropriate cause value (e.g. subscription is not allowed, subscription is not supported, Model/Functionality subscription not supported, or any other suitable naming).
• a MODEL / FUNCTIONALITY SUBSCRIPTION FAILURE or MODEL / FUNCTIONALITY SUBSCRIPTION REJECT message and an appropriate cause value e.g. subscription is not allowed, subscription is not supported, Model/Functionality subscription not supported, or any other suitable naming.
• Step 1 The client triggers the model (and/or functionality) configuration procedure for an AI/ML model for given/specific functionality(s) (or use case(s)/scenario(s)/configuration(s)/ site(s)) by sending a model configuration request message (e.g. MODEL / FUNCTIONALITY CONFIGURATION REQUEST message, including information related to model (and/or functionality) configuration.
• a model configuration request message e.g. MODEL / FUNCTIONALITY CONFIGURATION REQUEST message, including information related to model (and/or functionality) configuration.
• model ID local ID or global ID or temporary ID
• functionality ID local or global ID or temporary ID
• model size e.g. number of layers/number of parameter
• model inputs e.g. input dimension
• model output e.g. output dimension
• scenario other parameters
• Step 2 the MLC identifies the model (and/or model functionality/functionalities) by using the received Model ID (and/or Functionality ID), or other information related to the configuration of the model and/or functionality, and configures the model (and/or functionality) based on configuration parameters in the request message.
• the MLC may configure the model for a given functionality or multiple functionalities. In another example, the MLC may configure the functionality for a given model or multiple models.
• Step 3 The MLC may reply to client, for example, by sending a MODEL / FUNCTIONALITY CONFIGURATION RESPONSE message or MODEL / FUNCTIONALITY CONFIGURATION ACKNOWLEDGE message to acknowledge that it has successfully configured the model and/or functionality.
• the MLC may not accept the model and/or functionality configuration request, and may respond, for example, with a MODEL / FUNCTIONALITY CONFIGURATION FAILURE or MODEL / FUNCTIONALITY CONFIGURATION REJECT message and an appropriate cause value (e.g. configuration is not allowed, configuration is not supported, Model/Functionality configuration not supported, or any other suitable naming).
• a MODEL / FUNCTIONALITY CONFIGURATION FAILURE or MODEL / FUNCTIONALITY CONFIGURATION REJECT message and an appropriate cause value e.g. configuration is not allowed, configuration is not supported, Model/Functionality configuration not supported, or any other suitable naming.
• model LCM procedures described above are provided.
• the implementation of the above-described procedures are not limited to the following examples.
• the information including the messages below may vary and may include any of the information detailed in the above-described procedures.
• the initial message sent/transmitted by the client may be generally termed an AI/ML model management request message and a response (if present) sent/transmitted from the MLC to the client an AI/ML model management response message; however, the messages may be referred to by any suitable label with their content unaffected.
• Step 1 The client sends a request to MLC to register a new AI/ML model in the database.
• the client sends some or all information needed to create a Model Profile (e.g. model functionality/use-case/scenario/site/configuration, version number) and/or similarly a Functionality Profile, and/or other information related to the model.
• Model Profile e.g. model functionality/use-case/scenario/site/configuration, version number
• Functionality Profile e.g. model functionality/use-case/scenario/site/configuration, version number
• Model Profile model architecture, weights and metadata
• Functionality Profile e.g. in MLC or MLC database
• Step 3 The MLC sends to the client the unique Model ID (and/or Functionality ID).
• the MLC may include information related to the Model or Model functionality/functionalities.
• Step 1(b) The client updates the Model Profile (and/or Functionality Profile) and/or any other information related to the Model and/or Model functionality/functionalities
• Step 2 The client sends to MLC the updated Model Profile (and/or Functionality Profile) and/or any other information related to the Model and/or Model functionality/functionalities.
• MLC updates/replaces (totally or partially) the existing/stored (e.g. in MLC) Model Profile (and/or Functionality Profile) with the received MP (and/or FP).
• Step 1 The client triggers a model selection process by sending a model selection request to MLC.
• This request may include information on selection conditions (or criteria) and/or an Aggregation Function.
• the Aggregation Function performs a calculation on multiple Model Profiles (or Functionality Profiles) that fulfil the model /functionality selection conditions/criteria.
• o Location [Cell 1, Cell 2, ...]
• Step 2 The MLC sends to the client the output of Aggregate Functions. That is, the MLC forwards the MP (and/or FP) and/or any other information related to the selected model and/or model functionality/functionalities.
• Step 1 The client sends a request to the MLC with a subscription condition/criteria.
• o Functionality ID some unique ID (or description or information associated with functionality)
• Step 2 The MLC monitors the Model(s) Profiles (and/or Functionality(ies) Profiles), that fulfil the subscription condition, for any change(s) in the database and send/indicate this change to the Client.
• Step 1 The client sends a request to the MLC with a configuration request condition/criteria.
• Step 2 The MLC configures the model (Model ID 12345) and send the configured model to the Client.
• one or more of the described steps or states may be modified (e.g., two or more steps or states may be combined), omitted (e.g., one or more of the steps or states may not be included) or moved (e.g., the one or more steps, or a combination thereof, may be provided in a different order), in other examples related to this solution, if desired and appropriate, as would be understood by the skilled person. Additionally, it will be appreciated that additional steps or states may be added, or additional actions/operations performed in each described step or state.
• Model ID e.g. local ID or global ID, temporary ID, or (unique) ID in a cell, area, country, or across the network
• o Functionality e.g. local ID or global ID, temporary ID, or (unique) ID across the network
• ID e.g. local ID or global ID, temporary ID, or (unique) ID across the network
• Model Type e.g. Low Speed UEs
• Model Validation/Evaluation Type e.g. System Performance/Inference
• o UE Type e.g. Vehicular, NTN, IoT, UAV, other
• o UE Spatial-temporal e.g. Indoor, Outdoor, Evening, Week, month, year
• o UE Service e.g. Video Streaming
• Updated Location e.g. Cell ID, location coordinates, .
• o Subscriber List (e.g. Cell ID, UE ID, entity IP(s) address).
• o List of Cells e.g. serving and/or neighbour cells where model/functionality is available
• Functionality Profile may contain all, part of, similar or modified information to that included in the example above for the MP case.
• Certain examples of the present disclosure may be provided in the form of an apparatus/device/network entity configured to perform one or more defined network functions and/or a method therefor. Certain examples of the present disclosure may be provided in the form of a system (e.g. network or wireless communication system) comprising one or more such apparatuses/devices/network entities, and/or a method therefor.
• a system e.g. network or wireless communication system
• Figure 12 is a block diagram of an exemplary entity that may be used in examples of the present disclosure, where the entity may be a network entity or an entity external to the network.
• the client, AI/ML coordinating entity, and MLC of the examples of Figures 2-11 may comprise an entity of Figure 12.
• an entity may be implemented, for example, 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.
• the functionality of the entity may also be distributed.
• the entity 1200 comprises a processor (or controller) 1201, a transmitter 1203 and a receiver 1205.
• the receiver 1205 is configured for receiving one or more messages from one or more other network entities, for example as described above.
• the transmitter 1203 is configured for transmitting one or more messages to one or more other network entities, for example as described above.
• the processor 1201 is configured for performing one or more operations, for example according to the operations as described above.
• a method for managing Artificial Intelligence/Machine Learning (AI/ML) models in a wireless communications network comprising a client entity and an AI/ML coordinating entity
• the method comprising: transmitting from the client entity to the AI/ML coordinating entity an AI/ML model management request message; and receiving and processing at the AI/ML coordinating entity the AI/ML model management request message.
• AI/ML Artificial Intelligence/Machine Learning
• the AI/ML model management request message is an AI/ML model registration request message.
• the AI/ML model registration request message includes parameters of an AI/ML model to be registered, and the processing comprises registering the AI/ML model by assigning an ID to the AI/ML model and storing the AI/ML model in an AI/ML model database.
• storing the AI/ML model comprises storing the parameters of the AI/ML model in an AI/ML model template.
• the method further comprises transmitting from the AI/ML coordinating entity to the client, an AI/ML model management response message indicating successful registration of the AI/ML model or unsuccessful registration of the AI/ML model.
• the AI/ML model management response message indicating a successful registration of the AI/ML model includes information on the registered AI/ML model.
• the AI/ML model management request message is an AI/ML model update request message.
• the AI/ML model update request message includes AI/ML model identification information and updated AI/ML model parameters
• the processing comprises updating a stored AI/ML model identified by the AI/ML identification information based updated AI/ML model parameters.
• the method further comprises transmitting from the AI/ML coordinating entity to the client an AI/ML model management response message indicating a successful updating of the stored AI/ML model, successful updating of parts of the stored AI/ML model, or a failure to update the stored AI/ML model.
• the AI/ML model management request message is an AI/ML model selection request message.
• the AI/ML model selection request message includes AI/ML model selection information including information on one or more of an AI/ML model description, AI/ML model functionality, and AI/ML model selection criteria, and the processing comprises selecting an AI/ML model based on the AI/ML model selection information.
• the method further comprises transmitting from the AI/ML coordinating entity to the client an AI/ML model management response message indicating the selected AI/ML model or that AI/ML model selection has failed.
• the AI/ML model management request message is an AI/ML model subscription request message.
• the AI/ML model subscription request message includes AL/ML model subscription information including one or more of AI/ML model identification information, AI/ML model functionality information, and AI/ML model selection criteria
• the processing comprises identifying an AI/ML model based on the AI/ML model subscription information and monitoring the identified AI/ML model for changes
• the method further comprises, in response to a change in the identified AI/ML model, transmitting from the AI/ML coordinating entity to the client an AI/ML model change notification message indicating the change in the identified AI/ML model.
• the method further comprises transmitting from the AI/ML coordinating entity to the client an AI/ML model management response message indicating a success of subscription request if the subscription request is implemented or failure of the subscription request if the subscription request cannot be implemented.
• the AI/ML model management request message is an AI/ML model configuration request message.
• the AI/ML model configuration request message includes AI/ML model identification information and AI/ML model configuration information.
• the processing includes identifying an AI/ML model based on the AI/ML model identification information and configuring the identified AI/ML model based on the AI/ML model configuration information.
• the method further comprises transmitting from the AI/ML entity to the client an AI/ML model management response message including information on the configured AL/ML model if the configuration is successful, or indicating a failure of the configuration request if the configuration request cannot be implemented.
• the method further comprises transmitting from the AI/ML coordinating entity to the client, an AI/ML model management response message in response to the AI/ML model management request message indicating one or more of an acknowledgment of the AI/ML model management request message, successful processing of the AI/ML model management request message, or unsuccessful processing of the AI/ML model management request message.
• the AI/ML model management request message includes one or more of
• the client is any one of a network entity, a user equipment, a server, or an application function of the wireless communications network.
• the AI/ML coordinating entity is any one of or is included in any one of a network entity, a user equipment, a server, an application function, and a model learning controller of the wireless communications network.
• the AI/ML model management request message is based on a 3GPP Next Generation Application Protocol (NGAP) Class 1 or a Class 2 elementary procedure.
• NGAP Next Generation Application Protocol
• the AI/ML model management request message and the AI/ML model management response message are based on a 3GPP NGAP Class 2 elementary procedure.
• the AI/ML model management request and/or response messages are based on one or more of RRC/NAS, NG, S1, Xn, X2, F1, E1 signalling protocols, system information (periodic and/or on-demand), and/or other type of signalling between any network entity/function and the UE(s).
• the client is a UE or a gNB and the AI/ML coordinating entity is located in the RAN or Core Network (e.g. in an AMF).
• the AI/ML coordinating entity is located in the RAN or Core Network (e.g. in an AMF).
• the wireless communications network is a 3GPP compliant 5G or 6G network.
• a method of an Artificial Intelligence/Machine Learning (AI/ML) entity for managing AI/ML models is a wireless communications network comprising a client and the AI/ML coordinating entity is provided, the method comprising:
• AI/ML model management request message includes one or more of an AI/ML model registration request, an AI/ML model update request, an AI/ML model selection request, an AI/ML model subscription request, and an AI/ML model configuration request.
• a network entity of a wireless communications network configured to implement the method of any of the preceding examples is provided.
• Such an apparatus and/or system may be configured to perform a method according to any aspect, embodiment, example or claim 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, aspect and/or claim 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.
• Certain examples of the present disclosure provide a computer or processor-readable data carrier having stored thereon a computer program according to any example, aspect, claim or embodiment disclosed herein.

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