3GPP Technical Document Summary: CR 0607 to TS 26.114

Document Information

• CR Number: 0607
• Specification: TS 26.114 v19.2.0
• Category: B (addition of feature)
• Release: Rel-20
• Work Item: AIML_IMS-MED
• Source: Samsung Electronics Iberia SA

Purpose and Rationale

This Change Request introduces stage 3 specifications for AI/ML processing capabilities in IMS services. The CR addresses the missing technical specifications for AI/ML data delivery and signaling mechanisms required to support AI/ML-enhanced IMS services in Release 20.

Main Technical Contributions

1. References, Terms, and Abbreviations (Clauses 2, 3.1, 3.3)

Updates to include AI/ML-specific terminology, definitions, and abbreviations relevant to IMS services. Specific content marked as Editor's Notes for future completion.

2. New Annex AC: AI/ML Assisted Media Processing for MTSI

A comprehensive new normative annex is introduced covering all aspects of AI/ML integration with MTSI.

AC.1 Introduction

Provides introductory material on AI/ML capabilities in IMS services.

AC.2 Terminal Architecture

Defines updates to terminal architecture to accommodate:
- Inference engine
- AI/ML models
- Intermediate data handling

AC.3 End-to-End Reference Architecture

Potential updates to the end-to-end reference architecture for AI/ML support. Notes indicate possible liaison requirements with SA2.

3. AI/ML Call Flows (AC.4)

AC.4.1 AI/ML Model Delivery for Device Inferencing

Detailed 15-step call flow for AI/ML model delivery and execution:

Key Steps:
1. Session Establishment: MMTel service establishment
2. Bootstrap Data Channel (BDC) Setup: Established between UE and MF per TS 23.228
3. Application Discovery: UE requests application list via HTTP over BDC
4. Application List Creation: DCSF generates user-specific DC application list with metadata including:
- Generic app information (description, ID, URL)
- AI-specific information (AI feature tags, task descriptions)
5. Application Selection: User selects app based on AI service descriptions
6-9. Application Download: Selected AI application downloaded from DCSF via MF to UE, including AI task metadata (task manifest)
10. Task Selection: User presented with AI task list and selects desired tasks
11. Model Request: Selected tasks and models communicated to MF via:
- BDC: HTTP GET with task/model URLs
- ADC: AI Model Selection Request with model URNs
12. Model Retrieval: MF fetches AI models from either:
- 12a: DCAR via DCSF
- 12b: DC AS
13. Model Download: UE downloads AI models from MF via:
- BDC: HTTP response with AI models as resource
- ADC: AI Model Selection Response with model data
14. Inference Execution: Tasks executed on UE
15. Task Reselection: User/UE may reselect tasks during session using received metadata

Open Issues Identified:
- Whether MF needs to understand AI task semantics (FFS)
- Application types that can be handled
- Large model handling mechanisms

AC.4.2 Network Inferencing

Placeholder for network-based inference scenarios.

AC.4.3 Split Inferencing

Placeholder for distributed inference scenarios across UE and network.

4. AI/ML Capabilities (AC.5)

Defines capabilities and requirements for:
- AC.5.1 UE Capabilities: Device-side AI/ML requirements
- AC.5.2 Network Capabilities: Network-side AI/ML requirements

5. AI/ML Formats (AC.6)

Specification of formats for:
- AI/ML models
- Intermediate data

6. AI/ML Metadata (AC.7)

Definition of necessary metadata structures for AI/ML operations, including task manifests referenced in the call flows.

7. Negotiation and Signaling (AC.8)

Procedures for:
- Model delivery negotiation
- Inferencing coordination
- General AI/ML media processing signaling

8. Data Channel Transport (AC.9)

Specification of AI/ML data transport mechanisms:
- What data to transport over BDC (Bootstrap Data Channel)
- What data to transport over ADC (Application Data Channel)
- Transport procedures and protocols

Key Technical Entities

• MF: Media Function
• DCSF: Data Channel Selection Function
• DCAR: Data Channel Application Repository
• DC AS: Data Channel Application Server
• BDC: Bootstrap Data Channel
• ADC: Application Data Channel

Implementation Status

Most technical content is marked with Editor's Notes, indicating this is a skeleton CR establishing the structure for future detailed specifications. The most complete section is AC.4.1 (AI/ML model delivery for device inferencing), which provides a concrete call flow example.