Summary of S4-260269: On SA4 Work on AI Traffic Characteristics

1. Introduction and Background

This contribution from Apple addresses SA4's response to consultation requests from RAN2 and SA2 regarding AI traffic patterns and formats. The document establishes the context that SA4 must make application layer assumptions to develop traffic models for AI services, including LLMs and other AI agents. The paper argues for a specific approach to how these assumptions should be treated within the standardization process.

2. General AI Traffic Characterization

2.1 Scenario Diversity

The document identifies multiple dimensions of AI traffic variation:

• Text-to-Text: Current dominant scenario with small data packets (prompts) and token-based responses
• Multimodal (Images/Video): Significantly larger prompt data, e.g., photo uploads
• Interactive: Frequent, closely correlated exchanges requiring real-time performance (conversations, gaming)
• User Type: Agentic vs. human users impacting traffic patterns
• Model Formats: While most LLMs use HTTPS with RESTful APIs and JSON payloads, the data schema varies significantly across different models (references provided to OpenAI, Claude, and Gemini APIs)

2.2 Traffic Nature

AI traffic is characterized as:
- Bursty and unpredictable
- Event-driven rather than steady-state streaming

2.3 Modeling Requirements

To characterize traffic (latency, throughput, periodicity, burstiness), SA4 needs assumptions about data formats. However, the document notes:
- Industry currently uses various transport methods
- The domain is rapidly evolving
- No clear interoperability requirement exists today
- Traffic modeling targets deployment scenarios 5+ years in the future
- Current leading formats may become obsolete by 6G deployment

3. Proposed Approach

The contribution proposes three key principles for SA4:

1. Non-normative Treatment: AI format assumptions for traffic modeling should be treated as guidance only, not as rigid normative standardization targets. SA4 should avoid normative work on AI formats (actual data packet structure) at this stage to prevent locking specifications into constraints that may not suit future technology evolution.

2. Focus on Traffic Characteristics: Work should concentrate on traffic characteristics (latency, throughput, periodicity, burstiness) rather than specific coding or file formats used to generate that traffic.

3. Continuous Review: SA4 should periodically review this approach as the AI traffic and format landscape continues its rapid evolution.

Key Technical Contribution

The main technical contribution is a strategic positioning paper that argues against premature normative standardization of AI data formats while supporting the development of traffic models based on reasonable assumptions. The paper advocates for a pragmatic approach that acknowledges the rapid evolution of AI technologies and focuses SA4 efforts on traffic characteristics that will inform network design rather than application-layer format specifications that may quickly become outdated.