hash stringlengths 32 32 | doc_id stringlengths 5 12 | section stringlengths 5 1.47k | content stringlengths 0 6.67M |
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23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 6.5 Proposal #5: DOCX Linting
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23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 6.5.1 Description
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23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 6.5.1.1 Description of tools
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23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 6.5.1.1.1 Introduction
| Linting is an automated process to analyse code and linters are available for most programming languages. Similarly, DOCX files are in a programmatic XML representation and linting procedures could also be applied. For linting a set of rules need to be provided that can be extracted from TR 21.801, 21.900, and the pres... |
23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 6.5.1.1.2 ETSI MCC Macro Quick Check
| A "Quick Check" tool is available as part of the ETSI MCC Word Ribbon residing on the 3GPP FTP (https://www.3gpp.org/ftp/Information/Tools/_MCC_Ribbon_2024-06.zip). To start the Quick Check tool one needs to find the button in the Ribbon illustrated in Figure 6.5.1.1-1.
Figure 6.5.1.1.2-1: Linting rules provided by... |
23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 6.5.1.1.3 Alternative linters
| In addition or instead of existing macros 3GPP could develop linting rules and potentially even an own linter to vet the specs for any violation of the rules in 21.801, 21.802 (the present document if it defines new rules as e.g. in proposal #6) or 21.900.
Examples of such rules could be like in the following table, a... |
23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 6.5.1.2 Description of procedures
| Linting using the presented tools should be applied to any 3GPP TS/TR documents at all stages of development. This would ensure that specs are fulfilling the objective of high quality standards, can be easier maintained and also converted to other formats, if necessary.
In case a common infrastructure such as 3GPP For... |
23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 6.5.2 Evaluation against requirements of section 4.3
| TBD
6.X Proposal #X
6.X.1 Description
6.X.1.1 Description of tools
6.X.1.2 Description of procedures
6.X.2 Evaluation against requirements of section 4.3
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23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 7 Overall evaluation
| Editor's note: Overall evaluation of combined proposals from sections 5 and 6, including trials.
|
23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 8 Recommendations
| Editor's note: Final recommendations
Annex A:
3GPP Stakeholder Survey on CR Tools
As part of the effort to develop tools ''New Working Methods'' during the years 2015-2022, a 3GPP stakeholder survey was performed in 2022 [3]. The goal of this survey was to provide clear input on requirements and expectations wit... |
23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 25.4 I need to identify all abbreviations in a CR that are neither defined in the specification, nor in 21.905, nor in the cited 3GPP specifications in the reference section.
| Some secretaries found this useful, but most found the feature unimportant:
|
23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 25.5 I need to search change marked documents for all changes after a given date, e.g. after CEST yesterday.
| While most rapporteurs agreed this was important, there was disagreement with secretaries. One even commented: "don't do this!"
|
23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 27.6 I need to create an interim version of the target specification that reflects the specification status after the first of more than one working group meeting in a single quarter.
| NOTE 1: Though interim versions of specifications have no official status since CRs are only sent to TSG for approval at the end of a quarter, some delegates may benefit from the ability to view the cumulative result of all agreed CRs (and even postponed CRs) to a given specification
While some rapporteurs found this... |
23fc60931a9695f8f8c221e6f4caaa93 | 21.802 | 27.4 I need to be able to use the CR and specification tool to apply pseudo-CRs as changes to a source specification.
| NOTE 2: Pseudo-CRs (pCRs) are currently informally structured documents. Please take into account in answering this question that in order support implementation of pseudo-CRs in a tool, it may be necessary that pseudo-CRs documents become more formal in their structure. For example, it may be necessary to define and f... |
c88f99102e281fc307d2c2b828a1297d | 28.887 | 1 Scope
| The present document studies and assesses enhancements to the management aspects of management data, focusing on data discovery, data collection, data storage, data access etc., for 5G-Advanced. The study aims to identify the new functionalities, or enhancement to the data management capabilities and propose potential ... |
c88f99102e281fc307d2c2b828a1297d | 28.887 | 2 References
| 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.
-... |
c88f99102e281fc307d2c2b828a1297d | 28.887 | 3 Definitions of terms, symbols and abbreviations
| |
c88f99102e281fc307d2c2b828a1297d | 28.887 | 3.1 Terms
| For the purposes of the present document, the terms given in TR 21.905 [1] 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 [1].
example: text used to clarify abstract rules by applying them literally.
|
c88f99102e281fc307d2c2b828a1297d | 28.887 | 3.2 Symbols
| For the purposes of the present document, the following symbols apply:
<symbol> <Explanation>
|
c88f99102e281fc307d2c2b828a1297d | 28.887 | 3.3 Abbreviations
| For the purposes of the present document, the abbreviations given in TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in TR 21.905 [1].
<ABBREVIATION> <Expansion>
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c88f99102e281fc307d2c2b828a1297d | 28.887 | 4 Use Cases
| 4.1 Request and Report of External Management Data
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c88f99102e281fc307d2c2b828a1297d | 28.887 | 4.1.1 Use Case#1-1: Time Issue of External Management Data
| 4.1.1.1 Description
External management data are modelled in TS 28.622[2] by ExternalDataType IOC (clause 4.3.73). This IOC defines attributes to indicate the type of external management data and related meta data.
Specified meta data are
- mediaLocation: “address from which the described external management d... |
c88f99102e281fc307d2c2b828a1297d | 28.887 | 4.3.1 Use Case#3-1: Access control for performance metrics
| 4.3.1.1 Description
When an MnS consumer requests the collection of performance metrics using the PerfMetricJob IOC (see clause 4.3.31 of TS 28.622 [2]), the MnS producer needs to be able to determine whether the MnS consumer is authorized to collect such data or not.
4.3.1.2 Problem Statement
Clause 7.3 o... |
c88f99102e281fc307d2c2b828a1297d | 28.887 | 4.4.1 Use Case#4-1: Enhancement of Management data collection
| 4.4.1.1 Description
The ManagementDataCollection IOC defined in TS 28.622 [2] represents a management data collection request job for trace metrics and performance metrics. The ManagementDataCollection IOC includes attributes “managementData”, “targetNodeFilter”, “collectionTimeWindow”, “reportingCtrl”, “dataScop... |
c88f99102e281fc307d2c2b828a1297d | 28.887 | 4.4.2 Use Case#4-2: To clarify the condition attribute in ManagementDataCollection
| 4.4.2.1 Description
ManagementDataCollection IOC defined in 3GPP TS 28.622 [2], clause 4.3.47.1 has a condition attribute which summarizes that each SS defines its own condition syntax, but must support one or more assertions combined with logical AND/OR/NOT; conditionStatus is TRUE only if the entire condition e... |
c88f99102e281fc307d2c2b828a1297d | 28.887 | 5 Conclusion and Recommendations
| Editor’s Note: This clause is to summarize the identified key items and to discuss recommendations for a potential Work Item.
5.1 Request and Report of External Management Data
5.2 UE Data Collection
5.3 Enhancement of Management Services Access Control (MSAC)
5.4 Clarification of Mechanisms to Discover, Reques... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 1 Scope
| The present document …
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 2 References
| 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.
-... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 3 Definitions of terms, symbols and abbreviations
| |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 3.1 Terms
| For the purposes of the present document, the terms given in TR 21.905 [1] 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 [1].
example: text used to clarify abstract rules by applying them literally.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 3.2 Symbols
| For the purposes of the present document, the following symbols apply:
<symbol> <Explanation>
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 3.3 Abbreviations
| For the purposes of the present document, the abbreviations given in TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in TR 21.905 [1].
OTT Over the top
CSI Channel State Information
VFL Vertical Federated L... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5 Management capabilities for AI/ML lifecycle
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1 ML model training
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1 Use cases
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.1 Management support to training for UE-side model training
| Editor’s note: Further work on UE-side data collection for RAN-related use case, including documentation of potential solutions, is postponed in Rel-20 in accordance with TSG SA guidance [10].
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.1.1 Management support to AI/ML-based beam management
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.1.1.1 Description
| To support AI/ML-based beam management defined in TS 38.300 [2], for beam prediction management, UE can send the data of beam prediction management to UE-side training entity (e.g. a server inside MNO or an OTT server) via gNB and 3GPP management system for UE-side model training (see NOTE 1). 3GPP management system ne... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.1.1.2 Potential requirements
| REQ-ML_UESIDE-01: The 3GPP management system should have a capability allowing the authorized UE-side training entity to request the UE-side training data for the UE-side model training.
REQ-ML_UESIDE-02: The 3GPP management system should have a capability to request and get the UE-side training data from gNB(s) for t... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.1.1.3 Possible solutions
| Editor’s note: Possible solutions, addressing the requirements for this use case, are for further discussion in future release(s).
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.2 Management support to OAM-centric training for NG-RAN NW-side model
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.2.1 Management support to AI/ML-based beam management
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.2.1.1 Description
| To support AI/ML-based beam management defined in TS 38.300 [2], UE can provide data of beam management to 3GPP management system via gNB (see NOTE 1). 3GPP management system needs to collect data from gNB for OAM-centric training for NG-RAN NW-side model.
Figure 5.1.1.2.1.1-1: Management of NW-side data collection ... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.2.1.2 Potential requirements
| REQ-ML_NWSIDE-01: The 3GPP management system should have a capability to configure the NW-side training data collection for OAM-centric NW-side model training.
REQ-ML_NWSIDE-02: The 3GPP management system should have a capability to obtain the NW-side training data for OAM-centric NW-side model training.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.2.1.3 Possible solutions
| TBD
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.2.1.4 Possible solutions evaluation
| TBD
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.3 Management of Vertical Federated Learning
| |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.3.1 Description
| In Rel-19, Vertical Federated Learning (VFL) is introduced in core network for NWDAF(s) and AF(s). There may be one NWDAF or one AF acting as a VFL server, and one or multiple NWDAF(s) and/or one or multiple AF(s) acting as VFL Client(s). Vertical Federated Learning is available among NWDAFs or between NWDAF(s) and AF(... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.3.2 Potential requirements
| REQ-VFL_MGMT-01: The ML training MnS producer should have a capability allowing an authorized consumer to get the VFL role (VFL server or VFL client) of an ML Training Function in VFL process.
REQ-VFL_MGMT-02: The ML training MnS producer should have a capability allowing an authorized consumer to specify requirements... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.3.3 Possible solutions
| To support VFL in 3GPP management system, the following enhancements are proposed:
- Enhancements Aspects #1, extending the MLTrainingFunction IOC with the following aspects:
1) Extend learningTechnologyName by changing the allowed value “FL” to “VFL” and “HFL” to differentiate different FL training types supported b... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.3.4 Possible solutions evaluation
| Only one solution is identified, which is feasible.
5.1.1.4 Management support to data collection for two-sided model training
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.4.1 Management support to CSI compression
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.4.1.1 Description
| To support Channel State Information (CSI) compression defined in TR 38.843 [3], the operator may support the collection and deliver of relevant data for two-sided model training (see NOTE 1) to a UE-side model training entity (e.g. a server deployed by an MNO or by an OTT service provider). The UE-side model training ... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.4.1.2 Potential requirements
| REQ-ML_TWOSIDE-01: The 3GPP management system should have a capability allowing a UE-side training entity to subscribe for receiving relevant data for CSI compression.
REQ-ML_TWOSIDE-02: The 3GPP management system should have a capability to configure one or more gNBs to produce and report relevant data for CSI compre... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.4.1.3 Possible solutions
| |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.4.1.3.2 Solution #1
| NOTE: Since this use case will be discussed again in 6G timeframe, as per SA guidance, the proposed solution should be taken as possible in the context of 5G only.
The workflow below describes the use case as in Figure 5.1.1.4.1.1-1.
Step 1a. The UE-side training entity (external MnS consumer) sends request to th... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.4.1.3.1.1 Possible solutions for IOC used in the management service interface between UE-side training and 3GPP management system.
| Possible solutions for the IOC used in step 1 include using one of these:
- ManagementDataCollection IOC
- PerfMetricJob IOC
- TraceJob IOC
- Brand-new IOC
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.4.1.3.1.2 Possible solutions for IOC used in the management service interface 3GPP management system and gNB
| Possible solutions for the IOC used in step 4 include using one of these:
- PerfMetricJob IOC
- TraceJob IOC
- Brand-new IOC
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.4.1.3.2 Solution #2
| For this use case, the following approach is considered: gNB -> OAM -> UE-side training entity (a server inside MNO or an OTT server), where the gNB is the data-collection entity for relevant data for CSI Compression. The proposed solution below is only applicable for the case that gNB is the data-collection entity for... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.5 Enhancement on LCM of Federated Learning
| |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.5.1 Description
| Federated learning (FL) is a distributed machine learning approach that allows multiple FL clients to collaboratively train an ML model on local datasets contained in each FL Client without explicitly exchanging data samples.
When receiving an FL training request, the ML training MnS Producer acting as FL server needs... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.5.2 Potential requirements
| REQ-FL_MGMT-01: A ML training function supporting FL should enable a MnS consumer to request for training in consideration of energy information based selection criteria of FL clients.
REQ-FL_MGMT-02: The 3GPP management system should have a capability allowing an MLTrainingFunction acting as the FL Server to select F... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.5.3 Possible solutions
| It is proposed to add these criteria of renewable energy availability, renewable energy percentage and carbon emission information as optional attributes in FLClientSelectionCriteria <<dataType>> defined in clause 7.4.22.2 of TS 28.105 [4] to be able to use them as a criteria for selecting the FL clients by the FL serv... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.5.4 Possible solutions evaluation
| The solution described in clause 5.1.1.5.3 is feasible as it just enhances existing NRM i.e. FLClientSelectionCriteria <<dataType>> with information of renewable energy availability, renewable energy percentage and carbon emission and uses them as criteria for FL client selection in a federated learning.
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.6 Enhanced RL training with performance targets
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.6.1 Description
| During RL training, a policy is learnt to maximize a reward aggregated over time. The reward function is defined by the producer from a set of targets set by the consumer. The targets include thresholds such as “Call drop rate(CDR) < 1%”, “Call setup success rate (CSSR) > 90%”, but also optimization instructions such a... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.1.1.6.2 Potential requirements
| REQ-ENH_RL_TRAINING-01: The ML training MnS producer should have a capability to allow an authorized MnS consumer to specify the RL performance targets in an ML model training request.
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.2 ML model testing
| Editor’s note: Similar clause structure as in subclause 5.1 will be adopted.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.3 AI/ML inference emulation
| Editor’s note: Similar clause structure as in subclause 5.1 will be adopted.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.4 ML model deployment
| Editor’s note: Similar clause structure as in subclause 5.1 will be adopted.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.4.1 ML model transfer delivery to UE
| Editor’s note: The content in this clause remains for further discussion and depends on further correspondence and confirmation from the relevant RAN WGs.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.4.1.1 Description
| RAN1 and RAN2 started in Rel-18 a joint study on AI/ML for NR air interface (see FS_NR_AIML_air [5]) whose outcomes were reported in TR 38.843 [3], approved in RAN#102 (Dec. 2023). One of the features in scope of this study was ML model transfer/delivery to UE. The clause 7.2.1.4 of TR 38.843 [3] documented several pot... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.4.1.2 Potential requirements
| None.
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.4.1.3 Possible solutions
| SA5 should look at conclusion documented in RAN1#120 meeting Chair notes [8] section 9.1.4.2, and should not discuss solution 4b for now, given that RAN has not provided any guidance to SA5 on the need to standardize a solution for this feature.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.4.1.4 Possible solutions evaluation
| With no further action required from RAN, the analysis of the two options defined for Solution 4b would remain as follows:
The option “OAM can transfer/deliver AI/ML models to UE via OAM→UE, e.g., via IP tunnel” in Solution 4b is not feasible, given that the 3GPP management system does not have a direct interface with... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 5.5 AI/ML inference
| Editor’s note: Similar clause structure as in subclause 5.1 will be adopted.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6 AI/ML sustainability
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6.0 Sustainable aspects of ML model training and inference
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6.0.1 Description
| ML model training and AI/ML inference can require substantial computing power and therefore significant energy.
For sustainability goals in AI/ML management, it is valuable for the 3GPP management system to reflect the type of energy supplied to the managed entities that are used for ML model training and AI/ML infer... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6.0.2 Potential requirements
| REQ-ML_SUST-01. The 3GPP management system should enable the operator to configure and update the energy related information for the managed entities representing an ML model training function.
REQ-ML_SUST-02. The 3GPP management system should expose the energy information of the managed entities representing an ML mo... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6.0.3 Possible solutions
| It is proposed one solution consisting of two parts: one impacting TS 28.310 [9] (see clause 6.0.3.1), and other impacting TS 28.105 [4] (see clause 6.0.3.2).
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6.0.3.1 Update Energy Information NRM fragment
| It is proposed to update the Energy Information NRM fragment represented in 8.2.1 of TS 28.310 [9], by adding in ManagedEntity the following:
- The MLTrainingFunction IOC (see clause 7.3a.1.2.1 in TS 28.105 [4])
- The list of IOCs represented by AIMLSupportedFunction (see clause 7.3a.4.1.1 in TS 28.105 [4])
These ad... |
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6.0.3.2 Update MLTrainingFunction
| It is proposed to add a new attribute to MLTrainingFunction IOC: “energyInfoGroupRef”. This attribute allows specifying the DN of the energyInfoGroup MOI representing the operator-configured energy information.
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6.0.4 Possible solutions evaluation
| The solution described in clause 6.0.3 is feasible.
The part of the solution described in clause 6.0.3.1 allows satisfying REQ-ML_SUST-01 and REQ-ML_SUST-03.
The part of the solution described in clause 6.0.3.2 allows satisfying REQ-ML-SUST-02.
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172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6.1 Sustainability for ML training
| Editor’s note: Similar clause structure as in subclause 5.1 will be adopted.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 6.2 Sustainability for AI/ML inference
| Editor’s note: Similar clause structure as in subclause 5.1 will be adopted.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 7 Registration and discovery for AI/ML
| Editor’s note: Similar clause structure as in subclause 5.1 will be adopted.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 8 Relation with other management capabilities
|
Editor’s note: Similar clause structure as in subclause 5.1 will be adopted.
|
172e0e616ab35c29a035e8029b31cc86 | 28.882 | 9 Conclusion and recommendations
|
Annex A (informative):
Change history
Change history
Date
Meeting
TDoc
CR
Rev
Cat
Subject/Comment
New version
2025-08
SA5#162
Initial skeleton
0.0.0
2025-10
SA5#163
S5-254664
S5-254666
S5-254667
S5-254744
pCR
pCR
pCR
pCR
pCR TR 28.882 initial ToC
Pseudo-CR on TR 28.882 a... |
05c56d19e9004109bb58601d28cb2cad | 28.883 | 1 Scope
| The present document describes use cases, potential requirements, and potential solutions aimed at enhancing the Network Digital Twin (NDT) defined in TS 28.561, with aspects on interaction and collaboration between NDT and network functions/automation functions, multiple NDT collaborations, data collection requirement... |
05c56d19e9004109bb58601d28cb2cad | 28.883 | 2 References
| 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.
-... |
05c56d19e9004109bb58601d28cb2cad | 28.883 | 3 Definitions of terms, symbols and abbreviations
| |
05c56d19e9004109bb58601d28cb2cad | 28.883 | 3.1 Terms
| For the purposes of the present document, the terms given in TR 21.905 [1] 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 [1].
|
05c56d19e9004109bb58601d28cb2cad | 28.883 | 3.2 Symbols
| Void.
|
05c56d19e9004109bb58601d28cb2cad | 28.883 | 3.3 Abbreviations
| For the purposes of the present document, the abbreviations given in TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in TR 21.905 [1].
IHF Intent Handling Function
NDT Network Digital Twin
|
05c56d19e9004109bb58601d28cb2cad | 28.883 | 4 Concepts and background
| The concepts and overview described in TS 28.561 [3] are applicable in the present document. Network Digital Twin (NDT) is used as a replica of a mobile network, to learn how an actual mobile network would behave in certain scenarios, without causing any changes to the actual mobile network. To provide meaningful resul... |
05c56d19e9004109bb58601d28cb2cad | 28.883 | 5 Use cases
| |
05c56d19e9004109bb58601d28cb2cad | 28.883 | 5.1 Use Case #1: NDT support intent pre-evaluation
| |
05c56d19e9004109bb58601d28cb2cad | 28.883 | 5.1.1 Description
| TS 28.312 [2] introduces the intent pre-evaluation which includes Intent Feasibility check and Intent Exploration. Intent feasibility check enables the MnS consumer to check if the proposed intent can be supported by the MnS producer. Intent exploration enables the MnS consumer and the MnS producer to find the intent f... |
05c56d19e9004109bb58601d28cb2cad | 28.883 | 5.1.2 Potential requirements
| No new requirements.
|
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