Content for TS 28.105 Word version: 17.2.0
1 Scope
2 References
3 Definitions of terms, symbols and abbreviations
3.1 Terms
3.2 Symbols
3.3 Abbreviations
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1 Scope p. 7
The present document specifies the Artificial Intelligence / Machine Learning (AI/ML) management capabilities and services for 5GS where AI/ML is used, including management and orchestration (e.g. MDA, see TS 28.104) and 5G networks (e.g. NWDAF, see TS 23.288).
The present document also describes the functionality and service framework for AI/ML management.
2 References p. 7
The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or non-specific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1]
TR 21.905: "Vocabulary for 3GPP Specifications".
[2]
TS 28.104: "Management and orchestration; Management Data Analytics".
[3]
TS 23.288: "Architecture enhancements for 5G System (5GS) to support network data analytics services".
[4]
TS 28.552: "Management and orchestration; 5G performance measurements".
[5]
TS 32.425: "Telecommunication management; Performance Management (PM); Performance measurements Evolved Universal Terrestrial Radio Access Network (E-UTRAN)".
[6]
TS 28.554: "Management and orchestration; 5G end to end Key Performance Indicators (KPI)".
[7]
TS 32.422: "Telecommunication management; Subscriber and equipment trace; Trace control and configuration management".
[8]
TS 32.423: "Telecommunication management; Subscriber and equipment trace; Trace data definition and management".
[9]
TS 28.405: "Telecommunication management; Quality of Experience (QoE) measurement collection; Control and configuration".
[10]
TS 28.406: "Telecommunication management; Quality of Experience (QoE) measurement collection; Information definition and transport".
[11]
TS 28.532: "Management and orchestration; Generic management services".
[12]
TS 28.622: "Telecommunication management; Generic Network Resource Model (NRM) Integration Reference Point (IRP); Information Service (IS)".
[13]
TS 32.156: "Telecommunication management; Fixed Mobile Convergence (FMC) Model repertoire".
[14]
TS 32.160: "Management and orchestration; Management service template".
3 Definitions of terms, symbols and abbreviations p. 8
3.1 Terms p. 8
For the purposes of the present document, the terms given in TR 21.905 and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in TR 21.905.
ML entity:
an entity that is either an ML model or contains an ML model and ML model related metadata, it can be managed as a single composite entity.
AI decision entity:
an entity that applies a non-ML based logic for making AI decisions that can be managed as a single composite entity.
ML model:
mathematical algorithm that can be "trained" by data and human expert input as examples to replicate a decision an expert would make when provided that same information.
ML model training:
capabilities of an ML training function to take data, run it through an ML model, derive the associated loss and adjust the parameterization of that ML model based on the computed loss.
ML training:
capabilities and associated end-to-end processes to enable an ML training function to perform /ML model training (as defined above).
ML training function:
a function with ML training capabilities; it is also referred to as MLT function.
AI/ML inference function:
a function that employs an ML model and/or AI decision entity to conduct inference.
3.2 Symbols p. 8
Void.
3.3 Abbreviations p. 8
