Content for TS 28.105 Word version: 18.3.0
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. 11
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) and NG-RAN (see TS 38.300 and TS 38.401).
2 References p. 11
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] Void
[9]
TS 28.405: "Telecommunication management; Quality of Experience (QoE) measurement collection; Control and configuration".
[10] Void
[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".
[15]
TS 28.533: "Management and orchestration; Architecture framework".
[16]
TS 38.300: "NR; NR and NG-RAN Overall description; Stage-2".
[17]
TS 38.401: "NG-RAN; Architecture description".
[18]
TS 28.541: "Management and orchestration; 5G Network Resource Model (NRM); Stage 2 and stage 3".
3 Definitions of terms, symbols and abbreviations p. 12
3.1 Terms p. 12
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:
a manageable artifact of an ML model.
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:
process performed by an ML training function to take training 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 initial training:
the ML model training that generates the initial version of an ML entity.
ML re-training:
The process of training of a previously trained ML model.
ML joint training:
the ML training for a group of ML models that are trained and targeted for inference.
ML training:
refers to the end-to-end processes to enable an ML training function to perform ML model initial training or re-training (as defined above).
ML training function:
a logical function with ML model training capabilities.
AI/ML inference:
refers to the process of running a set of input data through a trained ML entity to produce set of output data, such as predictions.
AI/ML inference function:
a logical function that employs an ML model to conduct inference.
3.2 Symbols p. 12
Void.
3.3 Abbreviations p. 12
For the purposes of the present document, the abbreviations given in TR 21.905 and TS 28.533. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in TR 21.905 and TS 28.533.
AI
Artificial Intelligence
ML
Machine Learning
