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33.801-01
5.3.2 Security Assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.3.3 Key Issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.3.3.y Key Issue #3.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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5.4 Security Area #4: Security for Core Network, Interconnect and Roaming
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5.4.1 Introduction
Study potential aspects to secure core network communication for the different communication modes within a network and between networks to mitigate threats identified. This is based on the system architecture requirements developed in TR 23.801-1 [6]. Editor's note: This study will be based on agreements documented ...
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5.4.2 Security Assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.4.3 Key Issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.4.3.y Key Issue #4.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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5.5 Security Area #5: Subscription Authentication and Authorization
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5.5.1 Introduction
This security area includes the following security aspects related to authentication and authorization between the UE and the 6GS regardless of access type (i.e., 3GPP access and/or non-3GPP access): Editor’s Note: Whether trusted or untrusted non-3GPP access, or both are in scope is FFS. -Authentication, key agreem...
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5.5.2 Security Assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.5.3 Key Issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.5.3.y Key Issue #5.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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5.6 Security Area #6: Security and Privacy Aspects of Network Exposure
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5.6.1 Introduction
This security area covers the following aspects. - The security and privacy aspects of the exposure mechanism(s) defined in TR 23.801-01 [4]. Editor's Note: The above aspect needs to be expanded Editor's Note: Coordination with other working groups over terminology is needed to achieve clearer specifications.  Edit...
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5.6.2 Security Assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.6.3 Key Issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.6.3.y Key Issue #6.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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5.7 Security Area #7: Data Collection for Security Monitoring
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5.7.1 Introduction
NOTE: The study in this security area is expected to affect the CN and OAM and it does not affect the ME and UICC apps. No security related data will be collected from the UE and there is no impact on the UE. Security monitoring is important for the operational security aspects of a network. The study in this security...
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5.7.2 Security Assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.7.3 Key Issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.7.3.y Key Issue #7.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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6 Solutions
6.x Solutions to Security Area #x <security area name> 6.x.y Solutions to Key Issue #x.y <key issue name> 6.x.y.z Solution #x.y.z: <solution name> Editor's Note: Solutions are added incrementally (z = 1, 2, 3…) when new solution is identified. 'x' refers to the security area, y to the key issue. If a solution addre...
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7 Conclusions
Editor’s note: This clause will contain the overall conclusions made by SA3. The structure of this clause is FFS Annex A Attacker Model A.1 General This clause includes an introduction to the attacker model. This annex is used to provide preliminary understanding in developing the key issues; however, it is not...
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1 Scope
The present document provides the description and investigation of new AI/ML based use cases, i.e., multi-hop UE trajectory, AI/ML based intra-CU LTM, and other handover enhancements.
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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. -...
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3 Definitions of terms, symbols and abbreviations
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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]. <Void>
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3.2 Symbols
For the purposes of the present document, the following symbols apply: <Void>
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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]. <Void>
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4 Use cases and Solutions
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4.1 Multi-hop UE trajectory
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4.1.1 Use Case Description
In Rel-18, the cell-based UE trajectory prediction and measured cell-based UE trajectory are limited to the first-hop target NG-RAN node. Predicted multi-hop UE trajectory across gNBs consists of a list of cells belonging to gNBs where the UE is expected to connect and these cells are listed in chronological order. M...
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4.1.2 Solutions and Standard Impacts
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4.1.2.1 Locations for AI/ML Model Training and AI/ML Model Inference
The following solutions are considered for supporting multi-hop UE trajectory: - AI/ML Model Training is located in the OAM and AI/ML Model Inference is located in the gNB. - AI/ML Model Training and AI/ML Model Inference are both located in the gNB. In case of CU-DU split architecture, the following solutions are p...
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4.1.2.2 Input data of multi-hop UE trajectory
To predict the multi-hop UE trajectory, a gNB may need the following information as input data: From the UE: - UE measurement report related to serving cell and neighbouring cells, e.g., RSRP, RSRQ, SINR - Mobility History Information From the neighbouring RAN nodes: - UE History Information From the local node: ...
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4.1.2.3 Output data of multi-hop UE trajectory
For multi-hop UE trajectory, the following information can be considered as output data: - Predicted multi-hop UE trajectory, including a list of cells that UE is expected to connect to in chronological order and the associated expected time UE stays in the cell
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4.1.2.4 Feedback of multi-hop UE trajectory
For multi-hop UE trajectory, the following information can be considered as feedback: - Measured UE Trajectory collected at each visited gNB
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4.1.2.5 Standard impact
Multi-hop UE trajectory prediction is transferred to the target NG-RAN nodes over the Xn interface via the Handover Preparation procedure. For the subsequent handovers, multi-hop predicted UE trajectory includes a list of cells where the UE is expected to connect to in chronological order. For the subsequent hops, Dat...
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4.2 AI/ML assisted Intra-CU LTM
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4.2.1 Use Case Description
L1/L2 Triggered Mobility (LTM) is specified in TS 38.300 [2]. Intra-CU LTM is specified in TS38.401[3]. AI/ML can be used to optimise Intra-CU LTM procedures, e.g., to enhance Network and UE performance, optimize resource allocation and reduce mobility failures. AI/ML assisted L3 and L1 measurements based intra-CU L...
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4.2.2 Solutions and Standard Impacts
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4.2.2.1 Locations for AI/ML Model Training and AI/ML Model Inference
For CU-DU split architecture, the following solutions are possible: - AI/ML Model Training is located in the OAM and AI/ML Model Inference is located in the gNB-CU. - AI/ML Model Training and Model Inference are both located in the gNB-CU.
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4.2.2.2 Input data of AI/ML based Intra-CU LTM
For AI/ML optimization of intra-CU LTM the following information can be considered as input data: From local node: - L3 measurement results - UE history information - Measured/Predicted radio resource status per cell/SSB area - Measured/Predicted cell-based UE trajectory - Historical UE’s candidate LTM cell and b...
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4.2.2.3 Output data of AI/ML based Intra-CU LTM
For AI/ML optimization of Intra-CU LTM the following information can be considered as output data: - Predicted candidate cell(s) and beam(s) for LTM HO Preparation - Predicted target cell(s) and beam(s) for cell switch command - Predicted cell(s) and beam(s) for early UL synchronization - Predicted TA value(s) for ...
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4.2.2.4 Feedback of AI/ML based Intra-CU LTM
The following data can be considered as feedback data for intra-CU LTM: - LTM target cell and beam for cell switch - Measured TA value(s) - SON Reports (RLF, SHR, etc.) - Timing of cell switch execution - Timing of early UL synchronization
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4.2.2.5 Potential standard impacts
F1 impacts are expected to support transferring of the information listed in clause 4.2.2.2, clause 4.2.2.3 and clause 4.2.2.4, if applicable.
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4.3 Other handover enhancements – AI/ML assisted inter-CU LTM
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4.3.1 Use Case Description
Inter-CU LTM is specified in TS 38.300 [2]. AI/ML may be used to optimise inter-CU LTM procedures, e.g., for candidate cell selection.
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4.3.2 Solutions and Standard Impacts
If applicable, refer to the Solutions and standards impacts for intra-CU LTM in clause 4.2.2 to support inter-CU LTM with standards impacts over Xn interface.
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5 Conclusion
The following use cases are recommended by RAN3 to be specified in the Rel-20 normative phase: - Multi-hop UE trajectory - AI/ML assisted intra-CU LTM - AI/ML assisted inter-CU LTM Recommended solutions and standard impacts for each use case are based on section 4.1.2, section 4.2.2, and section 4.3.2. For each us...
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1 Scope
The present document is intended to capture the output of study item for "Study on Integrated Sensing And Communication (ISAC) for NR" [2]. CP-OFDM is considered as baseline waveform. The purpose of this TR is to document the following investigations for NR ISAC. - Performance evaluation of gNB-based mono-static sens...
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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. -...
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3 Definitions of terms, symbols and abbreviations
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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.
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3.2 Symbols
For the purposes of the present document, the following symbols apply: <symbol> <Explanation>
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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]. CPI Coherent Processing Interval STX Sensing Transmitter SRX Sensing Rec...
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4 Performance metrics
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4.1 Definitions of performance metrics
In the evaluation of NR ISAC, the following performance metrics are considered: - Horizontal/vertical positioning accuracy is defined as the absolute value of the difference between the estimated horizontal/vertical position and the corresponding true position of a sensing target. NOTE: There should be only one est...
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4.2 Performance objectives
The following performance objectives are adopted for the evaluation of UAV use case with gNB-based monostatic sensing. Table 4.2-1: Performance objectives Performance metrics Values Missed detection Probability 5% False Alarm Probability Type 1 5% False Alarm Probability Type 2 5% Horizontal Positioning Accur...
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5 Measurements
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5.1 Definitions of Levels/Options
From physical layer perspective, the following measurements that may be reported from RAN are identified in the study of NR ISAC. - Level A: Raw data per Tx antenna port per OFDM symbol per RX antenna port per TRP for a given time stamp - Option A1: Amplitude and phase samples in time/delay domain of the estimated ch...
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5.2 Measurement quantization
From physical layer perspective, the following methods can be considered to define the quantization for the measurements of Level A/B, e.g., floating point, uniform scalar, non-uniform scalar. Other quantization method is not precluded. From physical layer perspective, on measurement quantization of Level C/D for posi...
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5.3 Assistance information
For Level A/B, at least the following examples of assistance information are identified as needed to assist the further processing at Sensing Function. Additional assistance information is not precluded. The assistance information may or may not be provided by RAN. - Level A - Location of TRP - Step size in delay d...
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5.4 Payload size estimation
The payload size estimation of each measurement level/option for NR ISAC can be done by the following formulas. - Level A: Msubcarrier* Ncpi *Mtxport*Nrxport*Ntrp*(Nquan1+ Nquan2) where, - Msubcarrier: number of subcarriers in an OFDM symbol - Ncpi: number of OFDM symbols within CPI - Mtxport: number of reference ...
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6 Performance evaluation
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6.1 Evaluation methodologies
The following general procedure for performance evaluation of NR ISAC is provided. 1) Simulation parameter configuration 2) Sensing scenario generation, including the deployment of sensing Tx/Rx (STXs/SRXs) 3) Dropping N target(s), where N is equal to 0 or larger than 0 4) Channel generation and STX/SRX determina...
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6.2 Evaluation Assumptions
The evaluation assumptions for the evaluation of UAV use case with gNB-based monostatic sensing are provided in Annex A. In order to define the sensing resource ratio used in the evaluation of NR ISAC, three kinds of resources are defined - Type_1: Resources that are used for sensing signal transmission - Type_2: Par...
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6.3 Performance evaluation results
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6.3.0 Introduction
In Clause 6.3, a set of reported performance metrics obtained by a combination of all evaluation parameters from a company, i.e., a row in the excel sheets “Baseline 1”, “Baseline 2” and “Other configurations” in R1-2601610, is referred as a result from a source. NOTE: The reported results do not compare different qua...
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6.3.1 Baseline configuration 1
17 sources ([3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21]) report 46 results using baseline configuration 1. 21 results from 11 sources ([3, 5, 6, 8, 9, 13, 15, 16, 17, 20, 21]) show that all performance objectives can be met simultaneously, of which 15 results from 9 sources ([3, 5, 6, 8, 9, 15, 16, 1...
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6.3.2 Baseline configuration 2
15 sources ([3, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 20, 21]) report 29 results using baseline configuration 2. 13 results from 9 sources ([3, 5, 6, 8, 9, 17, 18, 20, 21]) show that all performance objectives can be met simultaneously, of which 11 results from 7 sources ([3, 6, 8, 9, 17, 18, 21]) model a target i...
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6.3.3 Other configurations
11 sources ([3, 7, 8, 10, 12, 13, 15, 16, 17, 18, 20]) report 55 results using other configurations. 34 results from 8 sources ([3, 8, 13, 15, 16, 17, 18, 20]) show that all performance objectives can be met simultaneously, of which 27 results from 5 sources ([3, 8, 16, 17, 18]) model a target in the channels of multip...
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7 Network architecture
Editor’s note: This section is to capture the study outcome of network architecture. Applicability to gNB bistatic sensing may be considered as part of this network architecture without additional architecture impacts. No inter-gNB coordination will be studied.
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8 RAN-CN procedures and signalling
Editor’s note: This section is to capture the study outcome of procedures and signaling aspects between RAN and CN for gNB-based monostatic sensing.
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9 Conclusions
The performance for UAV sensing use case with gNB monostatic sensing is evaluated based on the evaluation assumptions in Annex A (including UMa-AV, Sensing Tx/Rx operating simultaneously, FR1), with detailed assumptions and modelling reported by the companies as captured in Annex B. Among all the reported evaluation re...
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1 Scope
The present document covers the Radio Access Architecture and Interface aspects of the study item “Study on 6G Radio” [2]. 2 Time Lines
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2.1 Expected Output and Time scale
New specifications Type TS/TR number Title For info at TSG# For approval at TSG# Remarks External TR 38.960 Study on 6G Radio TSG#115 TSG#116 This TR is led by RAN TR editor: Kumagai, Shinya, NTT DOCOMO, shinya.kumagai.yw@nttdocomo.com Note: The pCR(s) to this TR will be provided by RAN WGs at the en...
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2.2 Interim Milestone
TSG#112 (June/2026): RAN3 to provide interim study results to allow TSGs to make a decision on: - RAN-CN interface: P2P vs SBI - RAN internal interfaces: CU-DU split, CP-UP split.
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3 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. -...
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4 Definitions of terms, symbols and abbreviations
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4.1 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. Standalone 6G RAN: refers...
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4.2 Symbols
For the purposes of the present document, the following symbols apply: <symbol> <Explanation>
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4.3 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]. 6GR 6G Radio ANR Automatic Neighbour Relation MDT Minimization of Drive ...
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5 Objectives and requirements
The detailed objectives of the study are: Single technology framework based on a stand-alone architecture to support the agreed existing and new services, and to satisfy the usage scenarios, requirements, deployment scenarios and design principles with acceptable performance/complexity trade-off, as determined by the ...
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5.1 General Principles
All requirements of TR 38.914 will serve as the basis for RAN3 architecture design principles. NOTE 1: If needed, RAN3 will check with relevant working groups on issues concerning security and data privacy. The 6G RAN architecture and interfaces shall allow for different RAN implementations, e.g. virtualized, cloud-b...
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5.2 Deployment Scenarios
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5.2.1 General
This section may be used to describe the details/solutions related to deployment scenarios as per 38.914. The 6G RAN architecture shall strive to support the deployment scenarios defined in TR 38.914. - FFS on the implications of this requirement on 6G RAN architecture. - FFS whether all deployment scenarios of TR 3...
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5.2.2 Shared RAN deployment
6GR should support shared RAN deployments, including support of multiple participating operators. A shared RAN should be able to efficiently interoperate with a non-shared RAN. Mobility and service continuity between the non-shared RAN and the shared RAN shall be supported. Figure 5.2.2-1: Shared RAN deployment
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5.2.3 Non-Centralized deployment
In this scenario, the full protocol stack is supported at the 6G RANaNB node e.g. in a macro deployment or indoor hotspot environment (could be public or enterprise). The 6G RANaNB node can be connected to “any” transport. It is assumed that the 6G RANaNB node is able to connect to other 6G RANaNB nodes via an Inter 6...
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5.2.4 Co-sited deployment with NR
In this scenario the logical 6G RANaNB node is physically co-sited with the logical gNB either as part of the same physical base station or as multiple physical base stations deployed at the same site. The 6G system and the 5G system remain architecturally and logically separated as two independent entities. Such co-si...
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5.3 Overall RAN Architecture
Editor’s Note: Figure 5.3-1 and title aim at providing a single framework based on a stand-alone architecture for existing and new services, taking into account legacy communication services, and is subject to study progress including new services. The names of the logical nodes and the interfaces are FFS. The 6G RAN...
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5.3.2 RAN Functions
The aim of this section is to describe the functions supported in RAN. The 6G RANaNB node hosts the following functions: - Selection of CN node for a UE; - Routing of User Plane data towards CN; - Routing of Control Plane information towards CN; - Connection setup and release; - Session Management; - Support of ...
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6 RAN Architecture and Interfaces
The aim of this section is to describe all aspects related to the Interfaces between RAN and CN. 6.1 RAN-CN Interface
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6.1.1 General Principles
The aim of this section is to describe general design principles and requirements for RAN-CN Interface. The general principles for the specification of the 6G RAN-CN interface are as follows: - the 6G RAN-CN interface supports the exchange of signalling information between the RAN and CN; - the 6G RAN-CN interface s...
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6.1.2 RAN-CN Interface Functions
RAN-CN control plane interface supports following functions: - UE context management : The functionality to manage UE context between the RAN and CN; - Transport of NAS messages: The functionality to transfer NAS messages between the CN and UE, subject to SA2 progress; - PDU Session Management: The functionality to ...
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6.1.3 RAN-CN Interface Options
This chapter includes description of RAN-CN interface options including protocol stacks, considering new and existing services. 6.1.3.1 RAN-CN Control Plane Editor’s Note: The encoding (e.g., ASN.1) for control plane interface options below is FFS. 6.1.3.1.1 Point to Point (P2P) A RAN-CN P2P interface refers to app...
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6.1.3.2 RAN-CN User Plane
Editor’s Note 1: The aim of this section is to describe the user plane protocol stack for the RAN-CN interface. Editor’s Note 2: On hold waiting for CT4 input. The user plane interface between the 6G RAN and CN for 6G is based on a point-to-point tunnel.
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6.1.4 Evaluation of RAN-CN Interface Options
This chapter includes evaluation and comparison of RAN-CN interface options described in clause 6.1.3.1 To evaluate the RAN-CN interface options, the following criteria can be considered: Deployment Evaluate the capability of supporting diverse deployment environments. Evaluate the ease of upgrading the 5G RAN-CN i...
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6.2 RAN Internal Interfaces
This chapter shall comprehensively address study on CU-DU and CP-UP aspects including functional split considering interim milestone TSG#112 (June/2026) to take a decision during TSG#115: March 2027 This chapter should also address the interfaces between 6G RAN nodeaNBs (equivalent to Xn).
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6.2.1 Interface between 6G RAN nodeaNBs
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6.2.1.1 General Principles
The aim of this section is to describe general design principles and requirements for RAN Internal Interfaces The general principles for the specification of the 6G RAN-RANaNB-aNB interface are as follows: - the interface between 6G RANaNB nodes supports the exchange of signalling information between 6G RAN nodeaNBs,...
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6.2.1.2 Functions
The control plane interface between 6G RAN nodeaNBs supports the following functions: - UE mobility management: function to manage the UE mobility between 6G RAN nodeaNBs. The user plane interface between 6G RAN nodeaNBs supports the following functions: - Data forwarding.
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6.2.2 Disaggregated RAN Architecture
RAN3 acknowledges that depending on deployment scenarios, there are benefits of HLS: - CU centralization and resource pooling: With HLS, it is possible to deploy CU and DU on cloud infrastructure. Multiple DUs may be connected to a single CU, allowing non-delay critical processing to be centralized at regional or cent...