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.
@article{arxiv.2507.08164,
title = {KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence},
author = {Yun Tang and Mengbang Zou and Zeinab Nezami and Syed Ali Raza Zaidi and Weisi Guo},
journal= {arXiv preprint arXiv:2507.08164},
year = {2025}
}
Comments
7 pages, 5 figures, submitted for possible publication