Summary of S4-260115: Test Scenarios for AI Traffic Characterization based on SA1 Use Cases

1. Introduction

This contribution from Qualcomm proposes test scenarios for characterizing AI traffic patterns in support of the 3GPP SA4 6G Media Study objectives. The work is based on AI-related use cases defined in TR 22.870 "Study on 6G Use Cases and Service Requirements", covering AI Agents, Large Language Models (LLMs), Generative AI, and real-time AI inference services.

A 6G AI Traffic Characterization Testbed has been developed to measure traffic characteristics of generative AI services, analyze agentic AI patterns, and evaluate QoE metrics under various network conditions.

2. Relevant SA1 Use Cases from TR 22.870

The contribution identifies and categorizes relevant AI use cases from TR 22.870 into four main groups:

2.1 AI Agent Communication Use Cases

• Clause 6.6: 6G AI Agent collaboration with third-party AI using LLM
• Clause 6.7: AI Agents communication (multi-group task-oriented communication)
• Clause 6.8: 6G system assisted AI Agent service
• Clause 6.9: Collaborative AI Agents
• Clause 6.11: Built-in Intelligent Communication Assistant

2.2 Generative AI and LLM Use Cases

• Clause 6.13: Retrieval Augmented Generation for LLM
• Clause 6.26: Optimizing user experience for GenAI applications
• Clause 6.31: UE-Network collaboration with AI capabilities (LLM task offloading)
• Clause 6.33: AI text-to-video generation supported by computing
• Clause 6.59: 6G provided communication service for AI traffic

2.3 Real-time AI Inference Use Cases

• Clause 6.3: End-to-end AI for connected cars
• Clause 6.17: Intelligent communication assistant
• Clause 6.22: Intelligent calling services
• Clause 6.38: AI for disability support (real-time video/audio analysis and enhancement)
• Clause 6.49: 6GS providing low-latency AI inference service

2.4 Computing and Resource Exposure Use Cases

• Clause 6.2: Optimizing 6G infrastructure utilisation via resource exposure
• Clause 6.24: Distributed 6G network for AI computing
• Clause 6.28: Network-assisted video-based AI inference task offloading for mobile embodied AI
• Clause 6.34: 6G computing support for AI model inference
• Clause 6.50: Real time video super-resolution service

3. Test Scenarios for AI Traffic Characterization

The contribution proposes 10 test scenarios with explicit mapping to TR 22.870 use cases:

| Scenario | Description | TR 22.870 Mapping |
|----------|-------------|-------------------|
| chat_basic | Basic single-turn LLM chat interaction | 6.11, 6.17, 6.22, 6.59 |
| chat_streaming | Multi-turn chat with streaming responses | 6.11, 6.17, 6.26, 6.31, 6.59 |
| shopping_agent | AI Agent with tool calling (MCP) | 6.6, 6.7, 6.8, 6.11 |
| web_search_agent | Research agent with web search capability | 6.6, 6.13, 6.21 |
| realtime_text | Real-time conversational AI via WebSocket | 6.3, 6.17, 6.22, 6.38, 6.49 |
| realtime_audio | Audio-based real-time conversation | 6.17, 6.22, 6.38, 6.49 |
| image_generation | Image generation using Generative AI | 6.26, 6.31, 6.33, 6.34, 6.50 |
| multimodal_analysis | Multimodal input analysis (image + text) | 6.3, 6.15, 6.26, 6.28, 6.38, 6.50 |
| video_streaming | Video upload for AI inference offloading | 6.28, 6.38, 6.50 |
| computer_control_agent | Computer use agent via GUI automation | 6.8, 6.9, 6.21, 6.28 |

4. Relevant Metrics for the Selected Scenarios

4.1 Chat and Conversational AI Scenarios

Addresses TR 22.870 clauses 6.11, 6.17, 6.22, and 6.31.

Key Metrics:
- Time-to-First-Token (TTFT): Critical QoE metric for perceived responsiveness
- Time-to-Last-Token (TTLT): Total response generation time
- Token streaming rate: Throughput in tokens per second
- Uplink/Downlink byte volumes: Traffic volume for network dimensioning

4.2 AI Agent Scenarios

Addresses TR 22.870 clauses 6.6, 6.7, 6.8, and 6.11. Uses Model Context Protocol (MCP) for tool calling.

Key Metrics:
- Agent loop factor: Number of API calls per user prompt (agentic iterations)
- Tool call latency: Time for external tool execution
- Multi-step task completion time: End-to-end task duration
- Burstiness patterns: Peak-to-mean traffic ratio and ON/OFF periods

4.3 Real-time AI Scenarios

Addresses TR 22.870 clauses 6.49, 6.38, and 6.3 (low-latency requirements).

Key Metrics:
- WebSocket/WebRTC connection setup time
- Streaming chunk delivery patterns
- Stall detection metrics (rate, duration)
- Audio byte volumes and durations (for voice scenarios)

4.4 Generative AI Media Scenarios

Addresses TR 22.870 clauses 6.26, 6.28, 6.31, 6.33, and 6.50.

Key Metrics:
- Image generation latency and payload sizes
- Multimodal input processing requirements
- UL/DL asymmetry ratios for different content types
- Video upload bandwidth for AI inference offloading (20-100 Mbps per clause 6.28)
- Frame-level packet error tolerance characteristics

5. Proposal

The contribution proposes:
1. Adopt the identified test scenarios as described in this contribution and implemented in the AI testbed
2. Document the relevant AI use cases from TR 22.870 in an Annex in TR 26.870

Technical Contributions Summary

This contribution provides a comprehensive framework for AI traffic characterization in 6G systems by:
- Systematically mapping 10 test scenarios to specific SA1 use cases from TR 22.870
- Defining scenario-specific metrics covering QoE (TTFT, TTLT), traffic patterns (burstiness, asymmetry), and performance (latency, throughput)
- Introducing AI-specific traffic characteristics such as agent loop factors, token streaming rates, and agentic iteration patterns
- Addressing diverse AI service types including conversational AI, agentic AI with tool calling, real-time inference, and generative media services
- Providing a testbed-based approach for empirical traffic characterization to support network dimensioning and QoS specification for 6G AI services