TR 28.910
Study on enhancement of Autonomous network levels
V18.0.0 (PDF)
2023/12 27 p.
- Rapporteur:
- Dr. Cao, Xi
China Mobile Com. Corporation
full Table of Contents for TR 28.910 Word version: 18.0.0
1 Scope
2 References
3 Definitions of terms, symbols and abbreviations
4 Introduction on relevant study items and work items in 3GPP
5 Issue investigations and potential solutions for scenarios listed in TS 28.100
6 Issue investigations and potential solutions for new scenarios
7 Conclusion and recommendation
$ Change history
1 Scope p. 7
The present document studies on enhancement of autonomous network levels. It introduces the relevant study items and work items in 3GPP, identifies the additional generic MnS requirements of generic autonomous network level for the scenarios defined in Rel-17 and documents potential solutions for the identified generic MnS requirements. It also identifies the enhanced autonomy capabilities corresponding to different autonomous network levels for additional management use cases which is not defined in Rel-17 and documents potential solutions for the enhanced autonomy capabilities. Based on the investigation and studies, it provides recommendations for the further normative work.
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 28.310: "Management and orchestration; Energy efficiency of 5G".
[3]
TR 28.813: "Management and orchestration; Study on new aspects of Energy Efficiency (EE) for 5G".
[4]
TS 28.100: "Management and orchestration; Levels of autonomous network".
[5]
TS 28.312: " Management and orchestration; Intent driven management services for mobile networks".
[6]
TS 28.104: "Management and orchestration; Management Data Analytics".
[7]
TS 28.532: "Management and orchestration; Generic management services".
[8]
TS 28.541: "Management and orchestration; 5G Network Resource Model (NRM); Stage 2 and stage 3".
[9]
TS 28.622: "Telecommunication management; Generic Network Resource Model (NRM) Integration Reference Point (IRP); Information Service (IS)".
[10]
TS 28.552: "Management and orchestration; 5G performance measurements".
[11]
TS 32.422: "Telecommunication management; Subscriber and equipment trace; Trace control and configuration management".
[12]
TS 28.313: "Management and orchestration; Self-Organizing Networks (SON) for 5G networks".
[13]
TS 28.535: "Management and orchestration; Management services for communication service assurance; Requirements".
[14]
TS 28.105: "Management and orchestration; Artificial Intelligence / Machine Learning (AI/ML) management".
[15]
TS 28.554: "Management and orchestration; 5G end to end Key Performance Indicators (KPI)".
[16]
TR 28.912: "Study on enhanced intent driven management services for mobile networks".
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.
3.2 Symbols p. 8
Void
3.3 Abbreviations p. 8
4 Introduction on relevant study items and work items in 3GPP p. 8
In TS 28.100, categorizations of the tasks in a workflow are specified in clause 4.3.4 including intent handling, awareness, analysis, decision and execution, and these task categories are used in the framework approach for evaluating autonomous network levels (ANL) in clause 5 of TS 28.100.
In clause 4.2.1 of TS 28.535, management control loop including monitor, analytic, decision, execution is described. And in clause 6.1 of TS 28.104, management loop including observation, analytics, decision, execution is specified as well. The management control loop and management loop defined in TS 28.535 and TS 28.104 are similar, and both of them have the similar consideration with the workflow specified in TS 28.100, i.e. awareness, analysis, decision and execution. MDA role in the management loop is within the scope of analytics (see TS 28.100).
In addition to the above four items included in management control loop or management loop, intent handling is also introduced in TS 28.100 which means certain autonomous network levels are required to support intent driven management control loop autonomously. And in clause 4.2.2 of TS 28.312, intent driven MnS is defined with the capabilities including intent translation and intent fulfilment result/information evaluating, which are the main tasks included in the categorization of intent handling defined in clause 4.3.4 of TS 28.100.
And if Artificial Intelligence/Machine Learning (AI/ML) technology is utilized in autonomous networks, AI/ML model training MnS specified in TS 28.105, can be used to enable analytics or decision autonomy capabilities and thus to achieve certain autonomous network levels.
5 Issue investigations and potential solutions for scenarios listed in TS 28.100 p. 9
5.1 Key Issue# 5.1: Enhancement of generic autonomous network level for network optimization p. 9
5.1.1 Description p. 9
5.1.2 Potential solutions p. 9
5.1a Key Issue# 5.1a: Enhancement of autonomous network level for radio network coverage optimization p. 10
5.1a.1 Description p. 10
5.1a.2 Potential solutions p. 10
5.1b Key Issue# 5.1b: Analysis on the solution for MnS requirements of autonomous network level for RAN UE throughput optimization p. 11
5.1b.1 Description p. 11
5.1b.2 Potential solutions p. 11
5.2 Key Issue# 5.2: Enhancement of generic autonomous network level for RAN NE deployment p. 12
5.2.1 Description p. 12
5.2.2 Potential solutions p. 12
5.3 Key Issue# 5.3: Enhancement of generic autonomous network level for fault management p. 13
5.3.1 Description p. 13
5.3.2 Potential solution p. 13
6 Issue investigations and potential solutions for new scenarios p. 13
6.1 Key Issue# 6-1: Autonomous network level for RAN energy saving use case p. 13
6.1.1 Description p. 13
6.1.1.1 Use case p. 13
6.1.1.2 Workflow p. 14
6.1.1.3 Classification of autonomous network level p. 14
6.1.1.4 Autonomy capability description for RAN energy saving p. 16
6.1.1.5 Potential MnS requirements p. 17
6.1.2 Potential solutions p. 17
6.2 Key Issue# 6-2: Autonomous network level for 5GC NF deployment p. 18
6.2.1 Description p. 18
6.2.1.1 Use case p. 18
6.2.1.2 Workflow p. 18
6.2.1.3 Classification of autonomous network level p. 18
6.2.1.4 Autonomy capability description for management system p. 20
6.2.2 Potential solutions p. 20
6.3 Key Issue# 6-3: Management of Autonomous Network Level (ANL) p. 21
7 Conclusion and recommendation p. 22
7.1 Conclusion of key issue investigation p. 22
7.1.1 Key Issue# 5.1: generic autonomous network level for network optimization p. 22
7.1.2 Key Issue# 5.1a: autonomous network level for radio network coverage optimization p. 23
7.1.3 Key Issue# 5.1b: autonomous network level for RAN UE throughput optimization p. 23
7.1.4 Key Issue# 5.2: autonomous network level for RAN NE deployment p. 23
7.1.5 Key Issue# 5.3: autonomous network level for fault management p. 23
7.1.6 Key Issue# 6.1: autonomous network level for RAN energy saving p. 23
7.1.7 Key Issue# 6.2: autonomous network level for 5GC NF deployment p. 23
7.1.8 Key Issue# 6.3: management of autonomous network level p. 23
7.2 Conclusion and recommendation in general p. 23
$ Change history p. 25
