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10.7.5 Required data
For the Query A1 policy status request, the rApp provides the rAppId and the A1 policy identifier. The A1 policy functions respond with the status of the A1 policy. The status information of A1 policy is defined in A1 TD [3].
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10.8 A1-Related use case 6: Subscribe to status of an A1 policy
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10.8.1 Overview
This use case provides the description and requirements for subscribe to status of an A1 policy.
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10.8.2 Background and goal of the use case
Subscribe to A1 policy status changes procedure is defined as part of A1 policy management service in R1GAP [1].
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10.8.3 Entities/resources involved in the use case
1) A1 Policy functions in the role of A1 policy management service Producer: a) support functionality to allow rApps to subscribe to and unsubscribe from A1 policy status changes; b) support notifying the rApp regarding the changes in the status of an A1 policy; c) provide response of success or failure to subscribe to...
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10.8.4 Solutions
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10.8.4.1 Subscribe to A1 policy status change
Table 10.8.4.1-1: subscribe to A1 policy status change Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp subscribes to notifications about changes of the status of an A1 policy. Actors and Roles - rApp in the role of A1 policy management service Consumer. - A1 policy functions in the role of A...
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10.8.4.2 Notify A1 policy status change
Table 10.8.4.2-1: Notify A1 policy status change use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp receives notifications about changes in the status of an A1 policy. Actors and Roles - rApp in the role of A1 policy management service Consumer. - A1 policy functions in the role of A1 ...
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10.8.4.3 Unsubscribe from A1 policy status change
Table 10.8.4.3-1: Unsubscribe from A1 policy status change Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp unsubscribes from A1 policy status change notifications. Actors and Roles - rApp in the role of A1 policy management service Consumer. - A1 policy functions in the role of A1 policy man...
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10.8.5 Required data
To receive notifications regarding changes in status change of an A1 policy, the rApp as A1 policy service Consumer subscribes to notifications by providing rAppId and A1 policy identifier. When the A1 policy functions have successfully processed the request, they respond by providing a subscription identifier. The A1 ...
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11 Use cases for AI/ML Workflow Services
11.1 AI/ML workflow-related use case 1: AI/ML model registration and deregistration
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11.1.1 Overview
This use case enables an rApp as AI/ML model management and exposure services Consumer to register AI/ML models, query AI/ML model registration, update AI/ML model registrations and deregister AI/ML models,
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11.1.2 Background and goal of the use case
An AI/ML model producer rApp can register an AI/ML model, query and update the registration of an AI/ML model and deregister an AI/ML model with the AI/ML workflow functions.
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11.1.3 Entities/resources involved in the use case
1) AI/ML workflow functions as AI/ML model management and exposure services Producer: a) support the functionality to allow an rApp to register, query and update the registration of, and deregister an AI/ML model; b) provide the response of success or failure results to the register AI/ML model, query, and update AI/ML...
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11.1.4 Solutions
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11.1.4.1 Register AI/ML model
Table 11.1.4.1-1: Register AI/ML model Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML model producer rApp registers an AI/ML model. Actors and Roles - rApp in the role of AI/ML model management and exposure services Consumer. - AI/ML workflow functions in the role of AI/ML model management...
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11.1.4.2 Query AI/ML model registration
Table 11.1.4.2-1: Query AI/ML model registration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML model producer rApp queries an AI/ML model registration. Actors and Roles - rApp in the role of AI/ML model management and exposure services Consumer. - AI/ML workflow functions in the role of A...
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11.1.4.3 Update AI/ML model registration
Table 11.1.4.3-1: Update AI/ML model registration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML model producer rApp updates the registration of an AI/ML model. Actors and Roles - rApp in the role of AI/ML model management and exposure services Consumer. - AI/ML workflow functions in the r...
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11.1.4.4 Deregister AI/ML model
Table 11.1.4.4-1: Deregister AI/ML model Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML model producer rApp deregisters an AI/ML model. Actors and Roles - rApp in the role of AI/ML model management and exposure services Consumer. - AI/ML workflow functions in the role of AI/ML model manage...
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11.1.5 Required data
The AI/ML model-related information includes the AI/ML model identifier, AI/ML model description, input, and output datatype, etc. For registering an AI/ML model, the rApp provides the rAppId and AI/ML model-related information. On successful registration, AI/ML workflow functions provide an AI/ML model registration id...
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11.2.1 Overview
This use case enables an rApp to retrieve information about registered AI/ML Models and their associated information.
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11.2.2 Background and goal of the use case
The discover AI/ML model procedure is defined as part of the AI/ML workflow services in R1GAP [1].
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11.2.3 Entities/resources involved in the use case
1) AI/ML workflow functions in the role of AI/ML model management and exposure service Producer: a) support functionality allowing rApps to discover the registered AI/ML Models; 2) rApp in the role of AI/ML model management and exposure Consumer: a) initiates the procedure to discover the registered AI/ML models.
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11.2.4 Solutions
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11.2.4.1 Discover AI/ML models
Table 11.2.4.1-1: Discover AI/ML models Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp discovers registered AI/ML models. Actors and Roles - rApp in the role of AI/ML model management and exposure Consumer. - AI/ML workflow functions in the role of AI/ML model management and exposure servic...
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11.2.5 Required data
For the Discover AI/ML model request, the rApp provides the rAppId and optional selection criteria. The AI/ML workflow functions respond with information which includes the model identifiers and metadata information. The metadata information contains AI/ML model related information and training related information. If ...
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11.3.1 Overview
This use case defines how an rApp as an AI/ML training services Consumer requests AI/ML training, queries AI/ML training job status, cancels AI/ML training, and receives notification about AI/ML training job status change, when the AI/ML workflow functions in the SMO/Non-RT RIC framework acts as the AI/ML training serv...
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11.3.2 Background and goal of the use case
An AI/ML training services Consumer rApp can request the AI/ML workflow functions in the Non-RT RIC framework to train an AI/ML model. For on-going AI/ML training, the AI/ML training services Consumer rApp can query the training job status or cancel the AI/ML training. If the training job status is changed, the AI/ML w...
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11.3.3 Entities/resources involved in the use case
1) AI/ML workflow functions as AI/ML training services Producer: a) support the functionality to allow an AI/ML training services Consumer rApp to request training, query training job status, and cancel training; b) provide the response of success or failure results to the request training, query training job status, a...
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11.3.4 Solutions
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11.3.4.1 Request AI/ML training
Table 11.3.4.1-1: Request AI/ML training use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML training services Consumer rApp requests training of an AI/ML model. Actors and Roles - AI/ML training services Consumer rApp in the role of Service Consumer. - The AI/ML workflow functions in ...
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11.3.4.2 Query AI/ML training job status
Table 11.3.4.2-1: Query AI/ML training job status use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML training services Consumer rApp query training job status of a created training job. Actors and Roles - AI/ML training services Consumer rApp in the role of Service Consumer. - The AI/...
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11.3.4.3 Cancel AI/ML training
Table 11.3.4.3-1: Cancel AI/ML training use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML training services Consumer rApp cancels training of an AI/ML model. Actors and Roles - AI/ML training services Consumer rApp in the role of Service Consumer. - The AI/ML workflow functions in th...
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11.3.4.4 Notify AI/ML training job status change
Table 11.3.4.4-1: Notify AI/ML training job status change use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML workflow functions notify the AI/ML training services Consumer rApp about the training job status change of a created training job. Actors and Roles - AI/ML training services C...
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11.3.5 Required data
For requesting training, the AI/ML training services Consumer rApp needs to provide its rAppId, information for training (e.g. information about training dataset, model identifier, training criteria.), and optionally a notification URI to receive notification about training job status change. If the AI/ML workflow func...
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11.4.1 Overview
This use case defines how an rApp as an AI/ML training services Consumer requests AI/ML training, queries AI/ML training job status, cancels AI/ML training, and receives notification about AI/ML training job status change, when another rApp acts as the AI/ML training services Producer.
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11.4.2 Background and goal of the use case
An AI/ML training services Consumer rApp can request the AI/ML training service Producer rApp to train an AI/ML model. For on-going AI/ML training, the AI/ML training services Consumer rApp can query the training job status or cancel the AI/ML training. If the training job status is changed, the AI/ML training services...
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11.4.3 Entities/resources involved in the use case
1) AI/ML training services Producer rApp: a) supports the functionality to allow an AI/ML training services Consumer rApp to request training, query training job status, and cancel training; b) provides the response of success or failure results to the request training, query training job status, and cancel training re...
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11.4.4 Solutions
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11.4.4.1 Request AI/ML training
Table 11.4.4.1-1: Request AI/ML training use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML training services Consumer rApp requests training of an AI/ML model. Actors and Roles - AI/ML training services Consumer rApp in the role of Service Consumer. - AI/ML training service Producer ...
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11.4.4.2 Query AI/ML training job status
Table 11.4.4.2-1: Query AI/ML training job status use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML training services Consumer rApp query training job status of a created training job. Actors and Roles - AI/ML training services Consumer rApp in the role of Service Consumer. - AI/ML t...
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11.4.4.3 Cancel AI/ML training
Table 11.4.4.3-1: Cancel AI/ML training use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML training services Consumer rApp cancels training of an AI/ML model. Actors and Roles - AI/ML training services Consumer rApp in the role of Service Consumer. - AI/ML training service Producer rA...
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11.4.4.4 Notify AI/ML training job status change
Table 11.4.4.4-1: Notify AI/ML training job status change use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML training service Producer notifies the AI/ML training services Consumer rApp about the training job status change of a created training job. Actors and Roles - AI/ML training s...
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11.4.5 Required data
For requesting training, the AI/ML training services Consumer rApp needs to provide its rAppId, information for training (e.g. information about training dataset, model identifier, training criteria.), and optionally a notification URI to receive notification about training job status change. If the AI/ML training serv...
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11.5 AI/ML workflow-related use case 5: AI/ML model Retrieve
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11.5.1 Overview
This use case defines how an rApp retrieves model location details as a consumer of registered AI/ML model(s).
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11.5.2 Background and goal of the use case
The AI/ML model retrieve procedures are defined as part of the AI/ML workflow services as part of AI/ML workflow services in R1GAP [1].
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11.5.3 Entities/resources involved in the use case
1) AI/ML workflow functions: a) support model management and exposure functionality to allow an rApp to retrieve the location details of AI/ML model(s). ETSI ETSI TS 104 230 V10.0.0 (2026-02) 127 2) rApp: a) initiates the procedure to retrieve the location details of AI/ML model(s).
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11.5.4 Solutions
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11.5.4.1 Retrieve model
Table 11.5.4.1-1: Model retrieve Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp retrieves AI/ML model(s). Actors and Roles - rApp in the role of Model retrieve service Consumer. - AI/ML workflow functions in the role of Model retrieve service Producer. Assumptions n/a Preconditions - The rA...
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11.5.5 Required data
For Retrieve AI/ML model information request, the rApp provides the rAppId, and model identifier(s). For Retrieve AI/ML model information response, the AI/ML workflow functions provide model identifier(s) and model location details. 11.6 AI/ML workflow-related use case 6: AI/ML model deployment
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11.6.1 Overview
This use case defines how an rApp requests the deployment of an AI/ML model as an AI/ML model Consumer.
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11.6.2 Background and goal of the use case
The AI/ML model deployment procedures are defined as part of the AI/ML workflow services as part of AI/ML workflow services in R1GAP [1].
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11.6.3 Entities/resources involved in the use case
1) AI/ML workflow functions: a) support model management and exposure functionality to allow an rApp to request the deployment of an AI/ML model. 2) rApp: a) initiates the procedure to deploy the updated artifact version of an AI/ML model being consumed by the rApp. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 129
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11.6.4 Solutions
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11.6.4.1 Retrieve model
Table 11.6.4.1-1: Model deployment Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp requests to deploy an AI/ML model being consumed by the rApp. Actors and Roles - rApp in the role of Model deployment service Consumer. - AI/ML workflow functions in the role of Model deployment service Produc...
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11.6.5 Required data
For a model deployment request, the rApp provides the rAppId, AI/ML model identifier, and the target deployment location of the AI/ML model. For a model deployment status notification, AI/ML workflow functions provide AI/ML model identifier and the deployment status. 11.7 AI/ML workflow-related use case 7: AI/ML model ...
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11.7.4 Solutions
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11.7.4.1 Subscribe AI/ML model performance
Table 11.7.4.1-1: Subscribe AI/ML model performance Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML workflow functions subscribe to AI/ML model performance. Actors and Roles - rApp in the role of AI/ML model performance monitoring service Producer. - AI/ML workflow functions in the role of ...
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11.7.4.2 Notify AI/ML model performance
Table 11.7.4.2-1: Notify AI/ML model performance Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp notifies subscribed consumers of AI/ML model performance information. Actors and Roles - rApp in the role of AI/ML model performance monitoring service Producer. - AI/ML workflow functions in the...
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11.8.1 Overview
This use case defines how an rApp as AI/ML model performance monitoring service Consumer subscribes to AI/ML model performance and receives notifications of AI/ML model performance information.
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11.8.2 Background and goal of the use case
An AI/ML model performance monitoring service Consumer can subscribe to AI/ML model performance and receive notifications of AI/ML model performance information. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 135
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11.8.3 Entities/resources involved in the use case
1) rApp as AI/ML model performance monitoring service Producer: a) supports the functionality to allow an authorized consumer to subscribe to, and receive notifications of, AI/ML model performance information. 2) rApp as AI/ML model performance monitoring service Consumer: a) supports the functionality to initiate the ...
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11.8.4 Solutions
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11.8.4.1 Subscribe AI/ML model performance
Table 11.8.4.1-1: Subscribe AI/ML model performance Use Case Stage Evolution / Specification <<Uses>> Related use Goal rApp subscribes to AI/ML model performance. Actors and Roles - rApp in the role of AI/ML model performance monitoring service Producer. - rApp in the role of AI/ML model performance monitoring service ...
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11.8.4.2 Notify AI/ML model performance
Table 11.8.4.2-1: Notify AI/ML model performance Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp notifies subscribed consumers of AI/ML model performance information. Actors and Roles - rApp in the role of AI/ML model performance monitoring service Producer. - rApp in the role of AI/ML model...
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11.8.5 Required data
For the Subscribe AI/ML model performance request, the AI/ML model performance monitoring service Consumer rApp provides the AI/ML model identifier and notification URI. In the Subscribe AI/ML model performance request, the AI/ML model performance monitoring service Consumer rApp optionally provides the required AI/ML ...
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11.9.1 Overview
This use case defines how an rApp registers and deregisters AI/ML model training capability.
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11.9.2 Background and goal of the use case
The register AI/ML model training capability and deregister AI/ML model training capability are optional procedures defined as part of the AI/ML model training capability registration service in R1GAP [1]. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 139
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11.9.3 Entities/resources involved in the use case
1) AI/ML workflow functions: a) support functionality to allow an rApp to register and deregister AI/ML model training capability; b) support validation of AI/ML model training capability information. 2) rApp: a) initiates the procedure to register and deregister AI/ML model training capability.
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11.9.4 Solutions
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11.9.4.1 Register AI/ML model training capability
Table 11.9.4.1-1: AI/ML Model training capability registration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp registers an AI/ML model training capability. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role of ...
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11.9.4.2 Deregister AI/ML model training capability
Table 11.9.4.2-1: AI/ML Model training capability deregistration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp deregisters an AI/ML model training capability. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role...
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11.9.4.3 Update AI/ML model training capability registration
Table 11.9.4.3-1: Update AI/ML model training capability registration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp updates the registration of an AI/ML model training capability. Actors and Roles - rApp in the role of AI/ML model management and exposure services Consumer. - AI/ML workflow...
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11.9.4.4 Query AI/ML model training capability registration
Table 11.9.4.4-1: Query AI/ML model training capability registration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp queries an AI/ML model training capability registration. Actors and Roles - rApp in the role of AI/ML model management and exposure services Consumer. - AI/ML workflow functio...
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11.9.5 Required data
The AI/ML model training capability information includes but not limited to supported training frameworks, supported library packages, supported library versions, resource information. For registering an AI/ML model training capability, the rApp provides the rAppId and AI/ML model training capability information. On su...
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11.10.4.1 Request AI/ML model inference
Table 11.10.4.1-1: Request AI/ML model inference use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML inference services Consumer requests inference for an AI/ML model. Actors and Roles - AI/ML inference services Consumer in the role of Service Consumer. - The AI/ML workflow functions i...
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11.10.4.2 Cancel AI/ML model inference
Table 11.10.4.2-1: Cancel AI/ML model inference use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML inference services Consumer cancels the AI/ML model inference that it has previously requested. Actors and Roles - AI/ML inference services Consumer in the role of Service Consumer. - Th...
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11.11.4.1 Subscribe AI/ML model changes
Table 11.11.4.1-1: Subscribe AI/ML model changes Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp subscribes to notifications regarding changes of AI/ML models. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role ...
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11.11.4.2 Notify AI/ML model changes
Table 11.11.4.2-1: Notify AI/ML model changes Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp receives a notification regarding a change of a registered AI/ML model. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the...
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11.11.4.3 Unsubscribe AI/ML model changes
Table 11.11.4.3-1: Unsubscribe AI/ML model changes Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp unsubscribes from notifications. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role of AI/ML model management an...
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11.12.4.1 Query AI/ML model inference capability
Table 11.12.4.1-1: Query AI/ML model inference capability use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML inference services Consumer retrieves the AI/ML model inference capability information. Actors and Roles - AI/ML inference services Consumer in the role of Service Consumer. - ...
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11.13.4.1 Query AI/ML model training capability
Table 11.13.4.1-1: Query AI/ML model training capability. Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp queries registered AI/ML model training capability. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role of...
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1 Scope
The present document has the goal of defining practical and effective methods for planning wireless backhaul links according to the novel approach detailed in ETSI GR mWT 028 [i.1]. One key area of investigation and contribution is the design of appropriate prediction models of the aggregated traffic demand statistics ...
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2 References
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2.1 Normative references
Normative references are not applicable in the present document.
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2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. NOTE: While any hyperlinks i...
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3 Definition of terms, symbols and abbreviations
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3.1 Terms
For the purposes of the present document, the following terms apply: peak (aggregated) traffic demand: maximum value of the aggregated traffic demand process experienced by a given backhaul link
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3.2 Symbols
For the purposes of the present document, the following symbols apply: , , , ℓ generic indices  ∈  is an element of set   Backhaul Traffic Availability total number of capacities that can be delivered by a given backhaul link   th capacity that can be delivered by a given backhaul link (with  () < ...
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3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply: 2G 2nd Generation 3G 3rd Generation 4G 4th Generation 5G 5th Generation ACM Adaptive Coding and Modulation ATTM Access, Terminals, Transmission and Multiplexing BH Backhaul BTA Backhaul Traffic Availability CDF Cumulative Distribution Function...
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4.1 Overview
The aim of this clause is to present a quick and straightforward analytical procedure for deriving the lower bound of the BTA of any given backhaul link on the basis of the sole knowledge of its expected average and peak aggregated traffic demands. The proposed approach offers thus the key benefit of leading to the der...
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4.3 The analytical procedure for deriving BTA lower bounds
The possibility of using the Beta distributions family to approximate the statistical behaviour of any backhaul traffic demand allows to derive the analytical method disclosed in the present clause, targeted at associating any transport link with a worst-case BTA value that can be readily used for a conservative but ef...
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5.1 Overview
The definition of appropriate prediction models of the backhaul traffic demand statistics can lead to the strategic benefit of making the choice of the throughput distributions required for computing the BTA metric a process completely transparent to the end user, and can thus greatly contribute to the successful adopt...
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5.2 Data collection
To assess the novel BTA metric (defined in equation (1)), ETSI GR mWT 028 [i.1] recommends to employ the cumulative distribution function of the average input traffic demand of the link observed with a time granularity on the order of 1 second. The present clause is devoted to discuss practical guidelines to achieve an...
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5.3 Data classification
Each traffic cumulative distribution function obtained by applying the guidelines described in clause 5.2 should be also associated with a list of significant attributes (also referred to as labels in the following) characterizing the link under analysis, e.g. including the deployment conditions and the involved RAN te...
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5.4 Clustering of traffic demand distributions
The ensemble of measured and properly classified traffic CDFs should be then assigned to a reduced set of homogeneous clusters on the basis of their respective link attributes. Afterwards, a group of representative cumulative distribution functions should be identified for each cluster, such as (see figure 13 for refer...
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5.5 Experimental results
The present clause 5.5 illustrates the results of a measurement campaign conducted with the primary goal of providing a guideline on how to apply the methodologies described in clauses 5.2 through 5.4, which are targeted to identify the representative traffic statistical distributions of a set of reference RAN scenario...
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6 Link planning example
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6.1 Overview
The present clause 6 is entirely devoted to the description of a comprehensive planning procedure based on the New KPIs methodology, with specific reference to the illustrative backhaul scenario sketched in figure 17. Before delving into the link design example, clause 6.2 explores practical strategies for determining ...