ETSI TS 128 105 V18.9.0 (2026-04)

5G; Management and orchestration; Artificial Intelligence/ Machine Learning (AI/ML) management (3GPP TS 28.105 version 18.9.0 Release 18)

RTS/TSGS-0528105vi90

General Information

• Status: Not Published
• Technical Committee: 3GPP SA 5 - Telecom Management
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Completion Date: 13-Apr-2026

Standards Content (Sample)

TECHNICAL SPECIFICATION
5G;
Management and orchestration;
Artificial Intelligence/ Machine Learning (AI/ML) management
(3GPP TS 28.105 version 18.9.0 Release 18)


3GPP TS 28.105 version 18.9.0 Release 18 1 ETSI TS 128 105 V18.9.0 (2026-04)

Reference
RTS/TSGS-0528105vi90
Keywords
5G
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ETSI
3GPP TS 28.105 version 18.9.0 Release 18 2 ETSI TS 128 105 V18.9.0 (2026-04)
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ETSI
3GPP TS 28.105 version 18.9.0 Release 18 3 ETSI TS 128 105 V18.9.0 (2026-04)
Contents
Intellectual Property Rights . 2
Legal Notice . 2
Modal verbs terminology . 2
Foreword . 9
1 Scope . 11
2 References . 11
3 Definitions of terms, symbols and abbreviations . 12
3.1 Terms . 12
3.2 Symbols . 13
3.3 Abbreviations . 13
4 Concepts and overview . 13
4.1 Overview . 13
4a AI/ML management functionality and service framework . 14
4a.0 ML model lifecycle . 14
4a.1 Functionality and service framework for ML model training . 15
4a.2 AI/ML functionalities management scenarios (relation with managed AI/ML features) . 16
5 Void . 18
6 AI/ML management use cases and requirements . 18
6.1 ML model lifecycle management capabilities . 18
6.2 Void . 19
6.2a Void . 19
6.2b ML model training. 19
6.2b.1 Description . 19
6.2b.2 Use cases . 20
6.2b.2.1 ML model training requested by consumer . 20
6.2b.2.2 ML model training initiated by producer . 20
6.2b.2.3 ML model selection. 21
6.2b.2.4 Managing ML model training processes . 21
6.2b.2.5 Handling errors in data and ML decisions . 21
6.2b.2.6 ML model joint training . 22
6.2b.2.7 ML model validation performance reporting . 22
6.2b.2.8 Training data effectiveness reporting . 23
6.2b.2.9 Performance management for ML model training . 23
6.2b.2.9.1 Overview . 23
6.2b.2.9.2 Performance indicator selection for MLmodel training . 23
6.2b.2.9.3 ML model performance indicators query and selection for ML model training . 23
6.2b.2.9.4 MnS consumer policy-based selection of ML model performance indicators for ML model
training. 24
6.2b.3 Requirements for ML model training . 24
6.2c ML model testing . 26
6.2c.1 Description . 26
6.2c.2 Use cases . 27
6.2c.2.1 Consumer-requested ML model testing . 27
6.2c.2.2 Producer-initiated ML model testing . 27
6.2c.2.3 Joint testing of multiple ML models . 27
6.2c.2.4 Performance management for ML model testing . 27
6.2c.2.4.1 Overview . 27
6.2c.2.4.2 Performance indicator selection for ML model testing . 27
6.2c.2.4.3 ML model performance indicators query and selection for ML model testing . 28
6.2c.2.4.4 MnS consumer policy-based selection of ML model performance indicators for ML model
testing . 28
6.2c.3 Requirements for ML model testing . 28
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6.3 AI/ML inference emulation . 29
6.3.1 Description . 29
6.3.2 Use cases . 29
6.3.2.1 AI/ML inference emulation . 29
6.3.3 Requirements for Managing AI/ML inference emulation. 29
6.4 ML model deployment . 29
6.4.1 ML model loading . 29
6.4.1.1 Description . 29
6.4.1.2 Use cases . 30
6.4.1.2.1 Consumer requested ML model loading . 30
6.4.1.2.2 Control of producer-initiated ML model loading . 30
6.4.1.2.3 ML model registration . 30
6.4.1.3 Requirements for ML model loading . 30
6.5 AI/ML inference . 31
6.5.1 AI/ML inference performance management . 31
6.5.1.1 Description . 31
6.5.1.2 Use cases . 31
6.5.1.2.1 AI/ML inference performance evaluation . 31
6.5.1.2.2 AI/ML performance measurements selection based on MnS consumer policy . 32
6.5.1.3 Requirements for AI/ML inference performance management. 32
6.5.2 AI/ML update control . 32
6.5.2.1 Description . 32
6.5.2.2 Use cases . 33
6.5.2.2.1 Availability of new capabilities or ML models . 33
6.5.2.2.2 Triggering ML model update . 33
6.5.2.3 Requirements for AIML update control . 33
6.5.3 AI/ML inference capabilities management . 34
6.5.3.1 Description . 34
6.5.3.2 Use cases . 34
6.5.3.2.1 Identifying capabilities of ML models . 34
6.5.3.2.2 Mapping of the capabilities of ML models . 35
6.5.3.3 Requirements for AI/ML inference capabilities management . 35
6.5.4 AI/ML inference capability configuration management . 35
6.5.4.1 Description . 35
6.5.4.2 Use cases . 35
6.5.4.2.1 Managing NG-RAN AI/ML-based distributed Network Energy Saving . 35
6.5.4.2.2 Managing NG-RAN AI/ML-based distributed Mobility Optimization . 36
6.5.4.2.3 Managing NG-RAN AI/ML-based distributed Load Balancing . 36
6.5.4.3 Requirements for AI/ML inference management . 36
6.5.5 AI/ML Inference History . 37
6.5.5.1 Description . 37
6.5.5.2 Use cases . 37
6.5.5.2.1 AI/ML Inference History - tracking inferences and context . 37
6.5.5.3 Requirements for AI/ML Inference History . 37
7 Information model definitions for AI/ML management. 38
7.1 Imported and associated information entities . 38
7.1.1 Imported information entities and local labels . 38
7.1.2 Associated information entities and local labels . 38
7.2 Void . 38
7.2a Common information model definitions for AI/ML management . 38
7.2a.1 Class diagram . 38
7.2a.1.1 Relationships . 38
7.2a.1.2 Inheritance . 39
7.2a.2 Class definitions . 39
7.2a.2.1 MLModel . 39
7.2a.2.1.1 Definition. 39
7.2a.2.1.2 Attributes . 40
7.2a.2.1.3 Attribute constraints . 40
7.2a.2.1.4 Notifications . 40
7.2a.2.2 MLModelRepository . 40
7.2a.2.2.1 Definition. 40
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7.2a.2.2.2 Attributes . 40
7.2a.2.2.3 Attribute constraints . 40
7.2a.2.2.4 Notifications . 40
7.2a.2.3 MLModelCoordinationGroup . 40
7.2a.2.3.1 Definition. 40
7.2a.2.3.2 Attributes . 41
7.2a.2.3.3 Attribute constraints . 41
7.2a.2.3.4 Notifications . 41
7.3 Void . 41
7.3a Information model definitions for AI/ML operational phases . 41
7.3a.1 Information model definitions for ML model training . 41
7.3a.1.1 Class diagram . 41
7.3a.1.1.1 Relationships . 41
7.3a.1.1.2 Inheritance . 42
7.3a.1.2 Class definitions . 42
7.3a.1.2.1 MLTrainingFunction . 42
7.3a.1.2.1.1 Definition . 42
7.3a.1.2.1.2 Attributes . 43
7.3a.1.2.1.3 Attribute constraints . 43
7.3a.1.2.1.4 Notifications. 43
7.3a.1.2.2 MLTrainingRequest . 43
7.3a.1.2.2.1 Definition . 43
7.3a.1.2.2.2 Attributes . 44
7.3a.1.2.2.3 Attribute constraints . 44
7.3a.1.2.2.4 Notifications. 44
7.3a.1.2.3 MLTrainingReport . 44
7.3a.1.2.3.1 Definition . 44
7.3a.1.2.3.2 Attributes . 45
7.3a.1.2.3.3 Attribute constraints . 45
7.3a.1.2.3.4 Notifications. 45
7.3a.1.2.4 MLTrainingProcess . 45
7.3a.1.2.4.1 Definition . 45
7.3a.1.2.4.2 Attributes . 46
7.3a.1.2.4.3 Attribute constraints . 46
7.3a.1.2.4.4 Notifications. 47
7.3a.2 Information model definitions for AI/ML inference emulation . 50
7.3a.2.1 Class diagram . 50
7.3a.2.1.1 Relationships . 50
7.3a.2.1.2 Inheritance . 50
7.3a.2.2 Class definitions . 51
7.3a.2.2.1 AIMLInferenceEmulationFunction . 51
7.3a.2.2.1.1 Definition . 51
7.3a.2.2.1.2 Attributes . 51
7.3a.2.2.1.3 Attribute constraints . 51
7.3a.2.2.1.4 Notifications. 51
7.3a.3 Information model definitions for ML model deployment . 51
7.3a.3.1 Class diagram . 51
7.3a.3.1.1 Relationships . 51
7.3a.3.1.2 Inheritance . 52
7.3a.3.2 Class definitions . 52
7.3a.3.2.1 MLModelLoadingRequest . 52
7.3a.3.2.1.1 Definition . 52
7.3a.3.2.1.2 Attributes . 53
7.3a.3.2.1.3 Attribute constraints . 53
7.3a.3.2.1.4 Notifications. 53
7.3a.3.2.2 MLModelLoadingPolicy . 53
7.3a.3.2.2.1 Definition . 53
7.3a.3.2.2.2 Attributes . 53
7.3a.3.2.2.3 Attribute constraints . 53
7.3a.3.2.2.4 Notifications. 53
7.3a.3.2.3 MLModelLoadingProcess . 54
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7.3a.3.2.3.1 Definition . 54
7.3a.3.2.3.2 Attributes . 54
7.3a.3.2.3.3 Attribute constraints . 55
7.3a.3.2.3.4 Notifications. 55
7.3a.4 Information model definitions for ML inference . 55
7.3a.4.1 Class diagram . 55
7.3a.4.1.1 Relationships . 55
7.3a.4.1.2 Inheritance . 56
7.3a.4.2 Class definitions . 56
7.3a.4.2.1 MLUpdateFunction . 56
7.3a.4.2.1.1 Definition . 56
7.3a.4.2.1.2 Attributes . 57
7.3a.4.2.1.3 Attribute constraints . 57
7.3a.4.2.1.4 Notifications. 57
7.3a.4.2.2 MLUpdateRequest . 57
7.3a.4.2.2.1 Definition . 57
7.3a.4.2.2.2 Attributes . 58
7.3a.4.2.2.3 Attribute constraints . 58
7.3a.4.2.2.4 Notifications. 58
7.3a.4.2.3 MLUpdateProcess . 58
7.3a.4.2.3.1 Definition . 58
7.3a.4.2.3.2 Attributes . 59
7.3a.4.2.3.3 Attribute constraints . 59
7.3a.4.2.3.4 Notifications. 59
7.3a.4.2.4 MLUpdateReport. 59
7.3a.4.2.4.1 Definition . 59
7.3a.4.2.4.2 Attributes . 60
7.3a.4.2.4.3 Attribute constraints . 60
7.3a.4.2.4.4 Notifications. 60
7.3a.4.2.5 AIMLInferenceFunction . 60
7.3a.4.2.5.1 Definition . 60
7.3a.4.2.5.2 Attributes . 61
7.3a.4.2.5.3 Attribute constraints . 61
7.3a.4.2.5.4 Notifications. 61
7.3a.4.2.6 AIMLInferenceReport . 61
7.3a.4.2.6.1 Definition . 61
7.3a.4.2.6.2 Attributes . 61
7.3a.4.2.6.3 Attribute constraints . 61
7.3a.4.2.6.4 Notifications. 61
7.4 Data type definitions . 62
7.4.1 ModelPerformance <> . 62
7.4.1.1 Definition . 62
7.4.1.2 Attributes . 62
7.4.1.3 Attribute constraints . 62
7.4.1.4 Notifications . 62
7.4.2 Void . 62
7.4.3 MLContext <> . 62
7.4.3.1 Definition . 62
7.4.3.2 Attributes . 62
7.4.3.3 Attribute constraints . 62
7.4.3.4 Notifications . 63
7.4.4 SupportedPerfIndicator <> . 63
7.4.4.1 Definition . 63
7.4.4.2 Attributes . 63
7.4.4.3 Attribute constraints . 63
7.4.4.4 Notifications . 63
7.4.5 AvailMLCapabilityReport <> . 63
7.4.5.1 Definition . 63
7.4.5.2 Attributes . 64
7.4.5.3 Attribute constraints . 64
7.4.5.4 Notifications . 64
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7.4.6 AIMLManagementPolicy <> . 64
7.4.6.1 Definition . 64
7.4.6.2 Attributes . 64
7.4.6.3 Attribute constraints . 64
7.4.6.4 Notifications . 64
7.4.7 ManagedActivationScope <> . 64
7.4.7.1 Definition . 64
7.4.7.2 Attributes . 65
7.4.7.3 Attribute constraints . 65
7.4.7.4 Notifications . 65
7.4.8. MLCapabilityInfo <> .
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