Standardisation
| No. | Contribution No. | Meeting / Date / Partners | Title | Purpose & Relation with Project | Related Project Task(s) |
|---|---|---|---|---|---|
1 | S1-253613 | 3GPP SA1#111 /
Aug. 25-29, 2025 LNV | New Use Case on Proactive AI Agent for Personal Safety | This contribution aligns well with FLECON-6G’s PoC4 (“Public Safety Services”) and, more generally, with the project’s objectives of developing trustworthy, native AI, delivering “zero-touch” automated solutions, and validating them in vertical applications. The proposed new 6G requirements, such as enabling a user-authorised AI agent to initiate emergency communications autonomously, are aligned with the research planned within WP4, specifically Task 4.1 on “Native and Trustworthy AI algorithms for zero-touch decision automation. | PoC4 (“Public Safety Services”) & Task 4.1 |
2 | S2-2507591 | 3GPP SA2#171 /
Aug. 25-29, 2025 LNV | Spec 23.700-04: KI#1 Update of Solution #3: Addressing Editor’s Notes. | The proposed solution defines a standardised mechanism for collecting datasets from UEs in a structured and scalable manner. These datasets are important for training AI models in Task 4.1 (Native and Trustworthy AI algorithms), which plans to “train the Foundation Models (FMs) that facilitate Native AI integration…trained by telecom datasets contributed by project partners”. The contribution promotes the project’s vision of “Native and Trustworthy AI”, which inherently depends on the availability of high-quality data for training and operating AI models. | Task 4.1 (Native and Trustworthy AI algorithms) |
3 | S6-253711 | 3GPP SA6#68 /
Aug. 25-29, 2025 LNV | Spec 23.700-83: Solution on support for VAL UE roaming | The solution enables collaboration/federation between different AI/ML Enablement servers, operated in different networks. This aligns with the objectives of Task 3.2 (Secure and trusted Federation for dynamic resource composition), which focuses on designing and implementing the FLECON-6G NoN Collaborative Framework. The negotiation and interaction between the source and target AI/ML Enablement servers is an implementation of the federation principles needed to support the NoN concept. Also, the proposed procedure enables the “dynamic composition and operation of multi-domain 6G networks of networks”, in line with the objective of TO6. The procedure defines how an AI/ML service can be seamlessly migrated from a source server in one network to a target server in another network as the UE moves. | Task 3.2 & NoN |
4 | S6-253681 | 3GPP SA6#68 /
Aug. 25-29, 2025 LNV | Spec 23.700-83: AIMLAPP architecture enhancements | The multi-operator and roaming architectures presented in the contribution support the project’s vision to realise an “Intelligent 6G Network of Networks” built on “multi-domain, multi-stakeholder infrastructures”. The proposed architecture defines how different stakeholders (networks/operators) can collaborate. Also, the proposed multi-operator architecture with distributed AIMLE servers represents a “collaborative and distributed framework for… multi-domain 6G networks of networks”, in line with the objective TO6. | NoN & TO6 |
5 | S2-2508698 | 3GPP SA2#171 /
Oct. 13-17, 2025 LNV | Spec 23.801-01: [WT#6, Computing] Working Scope and Key Issues for WT#6 Computing. | This contribution directly supports FLECON-6G architectural work by contributing to WP2 activities on system architecture extensibility and WP4 (T4.3) work on orchestration and resource coordination, enabling flexible 6G services and computing-aware network designs. | T4.3 |
6 | S2-2508520 | 3GPP SA2#171 /
Oct. 13-17, 2025 LNV | Spec 23.700-14: Evaluations and proposed Interim conclusion for KI#2. | This contribution aligns with FLECON-6G WP2 architecture activities and WP3 (T3.4) service enablement, supporting the project’s objective of multi-functional 6G networks that jointly provide connectivity and sensing services for vertical applications. | T3.4 |
7 | S6-254286 | 3GPP SA6#69 /
Oct. 13-17, 2025 LNV | Spec: 23.700-15: New solution to KI#3 | This contribution supports WP3 (T3.1) and WP5 (T5.1) by translating sensing outputs into actionable, application-level services, thereby enabling the evaluation of flexible 6G technologies in realistic vertical scenarios. | T3.1, T5.1 |
8 | S2-2508904 | 3GPP SA2#171 /
Oct. 13-17, 2025 LNV | Spec: 23.700-04: KI#1: Interim conclusions on controllability and visibility. | Maps to FLECON-6G WP4 (T4.1, T4.4) on AI/ML-enabled analytics and automation, contributing to the project’s goals of intelligent, adaptive, and automated 6G network operations that leverage data-driven control loops. | T4.1, T4.4 |
9 | S2-2511142 | 3GPP SA2#172 /
Nov. 17-21, 2025 LNV, LMI | Spec 23.801-01: [WT#1.1, all topics] Work task #1.1 Support of 6G Control signalling. | The FLECON-6G project aims to advance the framework for intelligent 6G networks and to influence and support the architectural foundations of future 6G systems, particularly in the areas of native AI, flexible control, and programmable interfaces. S2-2511142 aligns directly with this objective, contributing to the ongoing TR 23.801-01 study, which defines architectural elements for 6G, including how control signalling should be supported in new system designs. As a result, it helps shape the foundational architecture that will support advanced network features (such as AI-driven control, flexible signalling paths, and integrated compute-network coordination), all of which are core to the project’s vision for future 6G networks. | Task 2.1 |
10 | S2-2511144 | 3GPP SA2#172 /
Nov. 17-21, 2025 LNV, LMI | Spec 23.801-01: [WT#1.2, Slicing] Proposal for a new KI on Network Slicing in 6G. | Network slicing is a core network resource that the FLECON-6G Network of Networks (NoN) must compose, provision and expose across multiple domains and stakeholders. By steering the 6G slicing baseline toward simplification, improved application-to-slice mapping and slicing-aware interworking/migration, this Key Issue strengthens the resource-abstraction layer on which FLECON’s secure and trusted federation (Task 3.2) dynamically composes ad-hoc service topologies and on which the Smart Marketplace exposes Network-Slice-as-a-Service (NaaS) to verticals. | Task 3.2 & NoN |
11 | S2-2511196 | 3GPP SA2#172 /
Nov. 17-21, 2025 LNV, LMI | Spe 23.801-01: [WT#6] 6G Computing Support | Aligns directly with FLECON-6G’s Accelerated Programmable Edge Computing (APEC) and with the integration of computational resources into the Network of Networks. Standardising native 6G computing support – exposure of compute to third-party AFs and to the UE, service continuity upon compute relocation, compute-specific quality of service and compute-server discovery – provides the architectural hooks FLECON needs to offload application logic to the programmable edge, to expose compute as a marketplace asset (IaaS), and to keep extreme-edge services continuous as workloads move. | Task 3.3 |
12 | S2-2511197 | 3GPP SA2#172 /
Nov. 17-21, 2025 LNV, LMI, ORO | Spec 23.801-01: [WT#5 Data framework] WT scope and KI on 6G Data framework | FLECON-6G’s vision of a purely data-driven, end-to-end Native AI, together with its multi-domain Network Digital Twins, depends on systematic access to network data. The Smart Marketplace likewise monetises data exposure to verticals. This Work Task / Key Issue establishes the 6G data framework (discovery, collection, labelling, processing, storage and exposure, plus privacy/consent/governance) that becomes the substrate for FLECON’s trustworthy AI training and inference, for feeding the NDTs, and for data/AIaaS exposure across the NoN. | Task 4.1 |
13 | S2-2511212 | 3GPP SA2#172 /
Nov. 17-21, 2025 LNV, LMI | Spec 23.801-01: [WT#3, Network for AI] WT and KI proposal for WT#3 of 6G network for AI. | Foundational for FLECON-6G’s Native AI via Foundation Models and its agentic Intent Management Function (IMF). Standardising how the 6G network discovers and enables communication between AI agents (on UEs and on AFs) and how network capabilities are exposed to AI agents gives FLECON the in-network substrate for FM-driven, agentic task planning and intent decomposition, as well as for AI components that cooperate across the Network of Networks. | Task 4.4 & NoN |
14 | S2-2511215 | 3GPP SA2#172 /
Nov. 17-21, 2025 LNV, LMI | Spec 23.801-01: [WT#1.2, quality of service]WT & KI for quality of service Framework. | FLECON-6G promises SLA performance guarantees for services negotiated through its intent-driven Smart Marketplace (latency/reliability intents aligned with TMF 921) and must carry new AI/ML application traffic. This QoS Work Task / Key Issue evolves the 6G QoS framework toward dynamic, less resource-intensive QoS targets, UE-application-network QoS collaboration, and finer QoS monitoring and differentiation – exactly the mechanisms FLECON relies on to honour negotiated SLAs and to support its extreme use cases (immersive reality, autonomous robots, uRLLC public safety). | Task 3.1 |
15 | S2-2511217 | 3GPP SA2#172 /
Nov. 17-21, 2025 LNV, LMI | Spec 23.801-01: [WT#1.2, Policy] Policy Framework architecture in 6G. | Policy control is the mechanism by which FLECON-6G’s Cognitive Intent Management Function translates high-level intents into enforceable network behaviour for zero-touch, closed-loop operation. Simplifying the 6G policy framework (AM-/SM-PCF), considering user preferences, exposing policy information and addressing roaming directly support FLECON’s intent-driven service delivery and its automated policy enforcement across the multi-stakeholder Network of Networks. | Task 4.4 & NoN |
16 | S2-2511254 | 3GPP SA2#172 /
Nov. 17-21, 2025 LMI, ADS | Spec 23.801-01: [WT#7]: Work task and key issue for 6G satellite access. | Non-Terrestrial Networks are a key domain of FLECON-6G: the project delivers a dedicated NTN Network Digital Twin. It validates an end-to-end NTN PoC, and its Network of Networks must span fixed, terrestrial, mobile, and satellite segments. Defining the 6G architecture for satellite access (6G RAT for NTN, store-and-forward, TN/NTN selection across LEO/MEO/GEO, mobility, and service continuity) provides the standards baseline for FLECON’s NTN twin models and for the NoN, which federates alongside terrestrial resources. | Task 4.2 & NoN |
17 | S2-2511030 | 3GPP SA2#172 /
Nov. 17-21, 2025 LNV, LMI, ORO | Spec 23.700-67: KI#2 conclusions. | Drastically reducing the energy footprint relative to 5G is one of FLECON-6G’s headline KVIs and a central objective of its sustainability pillar. These conclusions standardise energy-aware policy control (the PCF taking network-energy-saving decisions from EIF/OAM energy information and AF requests, and collaborating with the AF on quality of service/PDTQ/BDT adjustments), giving FLECON concrete network mechanisms with which to operationalise its energy-efficiency and sustainability targets across the NoN. | Task 2.2 & NoN |
18 | S1-254388 | 3GPP SA1#112 /
Nov. 17-21, 2025 NVD | Spec 22.870: Update on Use case 6.24 distributed 6G network for AI computing | This use case captures the energy-aware, distributed edge computing that FLECON-6G targets. Routing AI workloads to compute sites based on available compute capacity and surplus (renewable) power, while preserving low latency and session continuity during workload relocation, mirrors FLECON’s goals of wire-speed edge processing, distributed compute orchestration within the NoN, and order-of-magnitude energy-footprint reduction. | Task 3.3 & NoN |
19 | S1-254427 | 3GPP SA1#112 /
Nov. 17-21, 2025 TID | Spec 22.870: New Use Case on Medical applications in 6G | Although medical monitoring is not one of FLECON-6G’s four PoCs, this use case exercises the same building blocks the project addresses: AI inference hosted on the 6G edge, an energy-efficient battery-powered device with a demanding uplink, and reliable service continuity for a mission-critical application. It contributes service-level requirements that reinforce FLECON’s edge-AI, energy-efficiency and trustworthy-service requirements analysis (Task 2.1). | Task 2.1 |
20 | S1-254512 | 3GPP SA1#112 /
Nov. 17-21, 2025 TID | Spec 22.870: Pseudo-CR on update use case of network slicing | Promotes FLECON-6G’s requirements for automated, AI-driven slice lifecycle management and for exposing Network-Slice-as-a-Service via the Smart Marketplace, and it covers public-safety slices that map onto FLECON’s Public Safety PoC. Faster automated slice creation/modification/deletion, efficient scaling, and improved slice selection with session continuity underpin the zero-touch, intent-driven slice provisioning that FLECON pursues across its Network of Networks. | Task 2.1 & NoN |
21 | S5-255529 | 3GPP SA5#164 /
Nov. 17-21, 2025 LMI | Spec 28.883: Rel-20 pCR TR 28.883 Clarification of NDT Job Modification Behaviour | FLECON-6G builds a multi-layer NDT framework and an NDT-as-a-Service platform that provisions and operates twin instances on demand. Predictable NDT job lifecycle management is essential for using twins as sandboxes and as closed-loop arbiters. Clarifying when and how an NDT job can be modified makes twin operation consistent and tool-agnostic, supporting FLECON’s reliable, verifiable NDT-assisted automation and its DTaaS orchestration (Task 4.3). | Task 4.3 & NoN |
22 | S5-255531 | 3GPP SA5#164 /
Nov. 17-21, 2025 LMI | Spec 28.883: Rel-20 pCR TR 28.883 Create and Execute NDT Job | Separating NDT job creation from execution directly enables the role-based, on-demand twin provisioning at the heart of FLECON-6G’s NDT-as-a-Service and Smart-Marketplace vision – e.g., subject-matter experts define a twin job while other consumers execute it. This lifecycle clarity supports FLECON’s multi-layer NDT orchestration and its use of twins to pre-validate automation decisions before enforcement. | Task 4.3 & NoN |
23 | S5-255532 | 3GPP SA5#164 /
Nov. 17-21, 2025 LMI | Spec 28.883: Rel-20 pCR TR 28.883 Clarification of Suspension and Resumption Capabilities for NDTJobs | Suspend/resume control over NDTJobs lets an NDT-as-a-Service platform reprioritise scarce twin resources (e.g., pause an optimisation twin to run a fault-handling one) – a capability FLECON-6G needs to orchestrate multiple concurrent twins and to enable closed-loop arbitration efficiently. Standardising this optional capability supports FLECON’s scalable, on-demand multi-layer NDT orchestration (Task 4.3). | Task 4.3 & NoN |
24 | S5-255683 | 3GPP SA5#164 /
Nov. 17-21, 2025 LMI | Spec 28.881: Rel-20 pCR TR 28.881 Add use case for Enhancement of radio service for protection. | FLECON-6G delivers intent-driven management (TMF-aligned intents consumed by its Cognitive IMF) and emphasises trustworthy, secure AI-driven operations, including AI-driven security components in its Public Safety PoC. Extending intent-based RadioServiceExpectations with service-protection (security) expectations lets operators declare protection levels as part of an intent, aligning with FLECON’s intent-driven, security-aware service assurance across the NoN. | Task 4.4 & NoN |
25 | S5-255518 | 3GPP SA5#164 /
Nov. 17-21, 2025 ORO | Spec 28.882: pCR on Rel-20 TR 28.882 Adding Enhancements on LCM of Federated Learning | Federated learning is a privacy-preserving, distributed form of the Native AI that FLECON-6G develops, and energy- and carbon-aware operation feeds into its sustainability pillar. Allowing the management system to select FL clients based on renewable-energy availability, renewable-usage percentage, and carbon emissions supports FLECON’s trustworthy, privacy-preserving, and sustainable AI/ML, where models are trained across the NoN with an explicitly reduced energy footprint. | Task 4.1 & NoN |
26 | S5-255519 | 3GPP SA5#164 /
Nov. 17-21, 2025 ORO, LMI | Spec: 28.882: Pseudo-CR TR 28.882 Sustainability aspects of ML model training and inference | Promotes FLECON-6G’s coupling of Native AI with its sustainability pillar by making AI/ML training and inference energy-aware at the management level. Exposing and using operator-configured energy information (renewable/carbon) for the entities that run ML training and inference gives FLECON a standards basis for energy- and carbon-aware, trustworthy AI/ML operation across the Network of Networks. | Task 4.1 & NoN |
27 | S2-2600224 | 3GPP SA2#173 /
Feb. 9-13, 2026 LNV | [KI#19, bullet 3] Supporting intent-based exposure from an AI agent in an AF. | This contribution directly supports FLECON-6G’s vision of “Native and Trustworthy AI” and the agentic Intent Management Function developed in WP4. Proposing intent-based exposure originating from an AI agent in an AF aligns with Task 4.1 (Native and Trustworthy AI algorithms) and Task 4.4 (Native AI integration for end-to-end DT-assisted automation), where AI agents decompose high-level intents into actionable network plans. | T4.1, T4.4 |
28 | S2-2600262 | 3GPP SA2#173 /
Feb. 9-13, 2026 LNV | [KI#22] Solution to support computing task offloading to the 6G network. | The proposed UE-to-network computing task offloading maps directly to FLECON-6G’s Accelerated Programmable Edge Computing (APEC) concept and Task 3.3 (Programmable platforms for data-plane flow control and extreme-edge integration). It also supports Task 4.3 on multi-domain orchestration of compute resources, advancing the project’s goal of wire-speed, ultra-low-latency edge processing. | T3.3, T4.3 |
29 | S2-2600240 | 3GPP SA2#173 /
Feb. 9-13, 2026 LMI | [KI#21, bullet 1] Network Digital Twin use case for the data framework. | This contribution introduces a Network Digital Twin use case for the 6G data framework that maps directly to FLECON-6G’s core NDT pillar. It supports Task 4.2 (Network Digital Twins framework) and Task 4.3 (multi-layer NDT orchestration), where twins are used for predictive what-if analysis and validation of network decisions before enforcement. | T4.2, T4.3 |
30 | S2-2600244 | 3GPP SA2#173 /
Feb. 9-13, 2026 LMI | [KI#18, bullet#1a and bullet#1b] How to fulfil requests from the UE or the AF that include intents. | Handling UE/AF requests that include intents aligns with FLECON-6G’s intent-driven service delivery and the Cognitive Intent Management Function (Task 4.4). It also supports Task 4.1 on zero-touch decision automation, reflecting the project’s move from low-level configuration toward high-level intent expression. | T4.1, T4.4 |
31 | S2-2600771 | 3GPP SA2#173 /
Feb. 9-13, 2026 LMI | Slice and Network-level energy-related information exposure | Exposing slice- and network-level energy information via the EIF supports FLECON-6G’s sustainability objective and the KPI/KVI energy-efficiency targets defined in Task 2.2. The exposure of energy assets to consumers also feeds the Smart Marketplace logic of Task 3.1, reinforcing the project’s goal of drastically reducing the network energy footprint. | T2.2, T3.1 |
32 | S6-260342 | 3GPP SA6#71 /
Feb. 9-13, 2026 LMI | New KI on GenAI in AIML Aspects (WT#3.3 and #3.4) | A new Key Issue on Generative AI in the application-enablement layer aligns with FLECON-6G’s Native AI via Foundation Models pillar (Task 4.1), which focuses on developing LLM-driven Foundation Models for task planning and intent decomposition. It directly informs the standardisation baseline for the project’s agentic, FM-driven network reasoning. | T4.1 |
33 | S2-2600629 | 3GPP SA2#173 /
Feb. 9-13, 2026 ORO | Solution for NAS-based LLM Service Commanding in 6G Core Network. | NAS-based LLM service, commanding orchestration and monetisation of distributed AI services, combines three FLECON-6G threads: Native AI/Foundation Models (Task 4.1), extreme-edge integration (Task 3.3), and monetisation of AIaaS assets via the Smart Marketplace. It exemplifies the project’s vision of operator-controlled, distributed AI across the 6G core and edge. | T3.3, T4.1 |
34 | S2-2600470 | 3GPP SA2#173 /
Feb. 9-13, 2026
ADS | [KI#17]: Key Issue Update. | Updating the NTN key issue with 5G NTN-access-based backhaul interworking supports FLECON-6G’s Non-Terrestrial Networks digital twin (Task 4.2) and PoC3 (NDT with NTN), which validates end-to-end twins across terrestrial and satellite segments. Submitted by the project partner Airbus, it advances the project’s satellite integration objectives. | T4.2, PoC3 |
35 | S5-260425 | 3GPP SA5#165 /
Feb. 9-13, 2026 ORO | Concepts and use cases on NDT For Mobile networks – Alignment with IETF | Analysis of NDT concepts/use cases for mobile networks (IETF alignment) supports FLECON-6G’s multi-layer, multi-vendor NDT framework (Task 4.2) and interoperable twins across RAN, Transport and NTN. | T4.2 |
36 | S6-260040 | 3GPP SA6#71 /
Feb. 9-13, 2026 ADS | Revised SID on logging and recording of mission-critical services, Phase 2 | This revised SID on logging and recording of mission-critical services aligns with FLECON-6G’s PoC4 (Public Safety Services), which tests ultra-reliable, low-latency mission-critical communication, and with the trustworthy-AI mechanisms of Task 4.1. Submitted by project partner Airbus, it underpins the project’s public-safety validation scenario. | PoC4, T4.1 |
37 | S2-2602377 | 3GPP SA2#174 /
Apr. 13-17, 2026 LNV | [KI#18] Enhancing NEF for handling Intent-based requests | Proposes embedding native AI (an AI-capable NF or AI Agent) in the NEF to interpret intent-based requests from untrusted AFs, with reasoning and intent-conflict resolution within a framework that provides guardrails and verifiability. Directly supports FLECON-6G’s Native and Trustworthy AI (Task 4.1) and the agentic Cognitive Intent Management Function (Task 4.4), and automates policy provisioning for external applications via the capability-exposure/Smart-Marketplace pillar (Task 3.1). | T4.1, T4.4 |
38 | S2-2602386 | 3GPP SA2#174 /
Apr. 13-17, 2026 LNV | [KI#18 bullets 1,2,3] Supporting distributed AI and reinforcement learning to support intent-driven operations | Proposes a distributed-AI 6G core where each NF is AI-enabled (AI agents producing local insights), plus reinforcement learning to make intent-driven operation proactive rather than reactive. Maps to FLECON-6G’s Native and Trustworthy AI algorithms (Task 4.1) and closed-loop, end-to-end DT-assisted automation (Task 4.4). | T4.1, T4.4 |
39 | S2-2602360 | 3GPP SA2#174 /
Apr. 13-17, 2026 LNV | [KI#19, bullet 3, solution variant 19.3] Update of solution variant 19.3 | Introduces an Agentic Interface Function (AIF) that bridges the AI-agent layer and the SBA control plane: AI agents on AFs invoke 6G exposure services via agentic protocols that a translation function (NEF or a new AI-agent proxy NF) maps to 3GPP API calls. Maps to FLECON-6G’s capability exposure / intent APIs (Task 3.1) and the agentic IMF (Task 4.4). | T3.1, T4.4 |
40 | S2-2602422 | 3GPP SA2#174 /
Apr. 13-17, 2026 ORO | KI#21, Enhancements of 5GS SBA for AI/ML enablement via Publish/Subscribe messaging | Proposes a data-centric SBA evolution with a Data Distribution Function (publish/subscribe over the SBI) giving unified, low-latency access to network context for advanced AI/ML; a PoC shows ~4x faster QoS-modification. Maps to FLECON-6G’s data framework for native AI (Task 4.4) and low-latency edge/data-plane efficiency (Task 3.3, APEC). | T3.3, T4.4 |
41 | S2-2603354 | 3GPP SA2#174 /
Apr. 13-17, 2026 LNV | Obtaining the Energy Saving Indicator for policy provision (CR TS 23.501) | Adds mechanisms for the PCF to obtain the UE Energy Saving Indicator for AF-triggered and SM policy provisioning. Supports FLECON-6G’s sustainability and energy-efficiency KPI objective (Task 2.2) and the exposure of energy-aware policy (Task 3.1). | T2.2, T3.1 |
42 | S5-261738 | 3GPP SA5#166 /
Apr. 13-18, 2026 ORO | PseudoCR on TR 32.801-01 Enhance the use case on 6G network digital twin | Adds/enhances the 6G NDT management use case (based on TR 22.870) for Autonomous Networks – simulation-based what-if validation combined with AI/ML prediction and decision. Maps to FLECON-6G’s Network Digital Twins framework and orchestration (Tasks 4.2 and 4.3). | T4.2, T4.3 |
43 | S5-261703 | 3GPP SA5#166 /
Apr. 13-18, 2026 ORO | pCR TR 32.801-01 High-level Management Scenarios – Requirements | Consolidates high-level 6G OAM management scenarios and requirements (energy saving & efficiency, mobile service delivery & assurance, network issues and complaint handling). Maps to FLECON-6G’s energy-efficiency KPIs (Task 2.2) and closed-loop service assurance/automation (Task 4.4). | T2.2, T4.4 |
44 | S5-261734 | 3GPP SA5#166 /
Apr. 13-18, 2026 ORO | Rel-20 pCR TR 32.801-01 New use case on Observability Framework for Network Functions | Adds an NF observability framework (distributed tracing, metrics collection, log aggregation) for anomaly detection, root-cause analysis and QoS assurance across distributed 5G/6G NFs. Underpins FLECON-6G’s closed-loop automation (Task 4.4) and the data foundation for Network Digital Twins (Task 4.2). | T4.4, T4.2 |
45 | S5-262345 | 3GPP SA5#167 /
May 18-22, 2026 LMI | Rel-20 pCR TR 32.801-01 Adding uncertainty quantification to NDT | Enhances the NDT management scenario with uncertainty quantification, enabling the 6G OAM to evaluate, request, and interpret the uncertainty of NDT outputs, making automated and human decisions more robust, explainable, and trustworthy. A direct double match to FLECON-6G’s Network Digital Twins framework (Task 4.2) and Trustworthy AI with uncertainty quantification (Task 4.1). | T4.1, T4.2 |
46 | S5-262214 | 3GPP SA5#167 /
May 18-22, 2026 LMI | pCR TR 32.801-01 Use case on DMFW and Knowledge usage for NDT Instance creation and synchronisation | Adds NDT instance creation/synchronisation that leverages the unified 6G Data Management Framework plus semantically-enriched knowledge (e.g. impact maps). Maps to FLECON-6G’s Network Digital Twins framework (Task 4.2) and data-driven native-AI integration (Task 4.4). | T4.2, T4.4 |
47 | S5-262348 | 3GPP SA5#167 /
May 18-22, 2026 LMI | Rel-20 pCR TR 32.801-01 Add management scenario for Intent explainability | Adds an intent-explainability scenario so MnS consumers can obtain human-understandable reasons for intent-handling decisions (e.g. why an intent was rejected or degraded), studying extensions to TS 28.312. Maps to FLECON-6G’s Trustworthy/Explainable AI (Task 4.1) and intent-driven automation (Task 4.4). | T4.1, T4.4 |
48 | S2-2605420 | 3GPP SA2#175 /
May 18-22, 2026 LNV, LMI, Nvidia | [KI#18 18.21] Closed-loop operation | Consolidates the high-level principles and solution variants for AI closed-loop operation in the 6G core (intent assurance, explainability-gated closed loop, AI agents). Maps directly to FLECON-6G’s Closed-Loop Decision Automation (Task 4.4) and Native/Trustworthy AI (Task 4.1). | T4.1, T4.4 |
49 | S2-2605619 | 3GPP SA2#175 /
May 18-22, 2026 LMI, NVD | [KI#18, Solution Variant #3] New separated domain from PS domain, within 6G CN | Defines a separate AI domain within the 6G core network (distinct from the packet-switched domain) for agentic, AI-native operation. Maps to FLECON-6G’s native-AI architecture for the Intelligent 6G Network of Networks (Tasks 4.1 and 4.4). | T4.1, T4.4 |
50 | S2-2605567 | 3GPP SA2#175 /
May 18-22, 2026 LNV | [KI#19, solution variant 19.1 update] 6G CN support for the UE AI agent and AF AI Agents | Baseline for AI Agent ID, registration, discovery and communication between UE and AF AI agents (signalling via the user plane) in a 6G CN AI domain. Maps to FLECON-6G’s agentic AI and the federation/exposure of AI agents (Tasks 4.1, 4.4 and 3.1). | T4.1, T4.4 |
51 | S2-2605387 | 3GPP SA2#175 /
May 18-22, 2026 LNV | [KI#5, bullet 4, Solution Variant 5.4 Baseline] Update to Solution Variant 5.4 (dynamic QoS) | Defines a 6G QoS flow with dynamic/multiple QoS requirements where the 6G RAN adapts QoS based on network conditions and UE/UPF input. Supports FLECON-6G’s adaptive three-layer architecture (Task 2.3) and closed-loop QoS assurance for demanding verticals such as immersive reality and autonomous robots (PoC1/PoC2, Task 4.4). | T2.3, T4.4 |
52 | S2-2605434 | 3GPP SA2#175 /
May 18-22, 2026 LMI, LNV | [KI#21, bullet 2, new] Data island solution for the data framework | Defines a modular ‘data island’ (Data Control/Transfer/Processing/Storage bundled into a single standardised entity, instantiable per operator’s needs) for the 6G data framework. Maps to FLECON-6G’s data framework underpinning native AI and Network Digital Twins, and to flexible, exposable data services (Tasks 4.4 and 3.1). | T3.1, T4.4 |
53 | S6-262086 | 3GPP SA6#73 /
May 18-22, 2026 TID | Spec 23.947: Clause 5.2, Edge-assisted incident response use case | This contribution aligns with FLECON-6G’s capability exposure and Smart Marketplace pillar (Task 3.1), Accelerated Programmable Edge Computing/extreme-edge integration (Task 3.3), and the project’s Public Safety Services validation scenario (PoC4). The edge-assisted incident-response use case shows how an Edge Application Server, deployed close to the incident area for low-latency processing, consumes SEAL service enablers (Location, Group and Configuration Management) exposed and authorised through CAPIF – exemplifying the secure exposure of network capabilities to context-aware vertical applications that FLECON-6G targets for first-responder and mission-critical services. | T3.1, T3.3, PoC4 |
54 | draft-contreras-tvr-interco-scheduling-00 | TID | Time-Variant Routing Scheduling for Interconnection Scenarios | This contribution specifies the applicability of Time-Variant Routing (TVR) information in interconnection scenarios. In particular, it discusses how scheduled changes affecting BGP sessions, routing policies, and effective interconnection capacity between administrative domains can be applied. Dynamic composition and coordinated operation of multi-domain networks enabling the concept of Network of Networks (NoN), for instance, for disaster recovery. | NoN |