KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence
This work addresses inefficiencies in implementing intelligence tasks for autonomous networks, offering a solution that reduces maintenance complexity and improves interoperability for network intelligence engineers and agents.
The paper tackles the problem of redundant data flows and inconsistent interpretations in autonomous 6G networks by proposing KP-A, a unified Network Knowledge Plane for agentic intelligence, which streamlines development and enhances interoperability, as demonstrated in tasks like live network knowledge Q&A and edge AI service orchestration.
The emergence of large language models (LLMs) and agentic systems is enabling autonomous 6G networks with advanced intelligence, including self-configuration, self-optimization, and self-healing. However, the current implementation of individual intelligence tasks necessitates isolated knowledge retrieval pipelines, resulting in redundant data flows and inconsistent interpretations. Inspired by the service model unification effort in Open-RAN (to support interoperability and vendor diversity), we propose KP-A: a unified Network Knowledge Plane specifically designed for Agentic network intelligence. By decoupling network knowledge acquisition and management from intelligence logic, KP-A streamlines development and reduces maintenance complexity for intelligence engineers. By offering an intuitive and consistent knowledge interface, KP-A also enhances interoperability for the network intelligence agents. We demonstrate KP-A in two representative intelligence tasks: live network knowledge Q&A and edge AI service orchestration. All implementation artifacts have been open-sourced to support reproducibility and future standardization efforts.