Towards AI-Native 6G Systems: Standards Enablers for 6G Network Automation

Artificial Intelligence/Machine Learning (AI/ML) methods for network automation have been widely adopted by the standardization community since 5G, due to its capability to learn patterns from continuously changing network data. Presently, AI in 3GPP is specified to be deployed as an overlay or add-on feature on top of conventional Network Functions (NFs), emitting a few limitations. First, NFs need to request the AI-overlay to derive intelligent actions, thereby lacking intelligent autonomy, that may degrade system and AI performance. Second, the capability of NFs and NF instances to added or removed based on changing network conditions is severely hindered, resulting in limited flexibility of NF deployment. To mitigate these limitations, this article explores architectural enablers for standards evolution from AI-overlay in 5G to native AI in 6G, where AI processing and communication is considered part of 6G system and architecture design. Via envisioned enablers – 6G-NF as an automated, intelligent entity, AI data and AI control – we conceive an Integrated Distributed AI Automation Layer (INTDAI), where each 6G-NF is equipped with the capabilities of an Intelligent Agent (IA) – independent data collection, intelligent decision-making and independent action execution. A special 6G-NF, the AI Controller, controls and manages a group of IAs to ensure overall system performance. The behavior and benefits of INTDAI are illustrated using a dedicated network automation use case, Dynamic User Plane Task Migration. By means of numerical analysis, we demonstrate the gain of INTDAI, motivating the need for future standardization work for AI-native 6G.