AI-Native Hierarchical Orchestration for Autonomous 6G RAN, Core, and Edge Systems

Abstract

AI-native networking establishes a complete new approach for designing and operating wireless systems which works differently than traditional closed-loop optimization. The research presents a structured orchestration system which uses AI agents to control all three network areas of RAN core and edge in order to achieve automated 6G system operation. The system replaces traditional automation systems which depend on fixed rules and response-based controls with intelligent agents who can observe their environment make decisions through logical thinking and learn to adapt their behavior throughout different network environments.

The system uses knowledge-based reasoning mechanisms together with policy abstraction techniques and programmable application interfaces to achieve real-time coordination of different network functions which come from multiple equipment vendors. The system structure uses hierarchical intelligence to process time-critical decisions at both edge and RAN points while central control takes care of organization-wide policy implementation together with cross-domain resource optimization. The system employs data-driven learning methods together with intent-based orchestration to automatically adjust itself according to changing traffic patterns and service needs and current network state.

The results of simulation tests together with pilot testing show that our system achieves better adaptability while doing fault recovery in an accelerated manner and decreasing operational costs in comparison to traditional orchestration methods. The results demonstrate increased resource efficiency together with maintenance of service availability during changing operational circumstances. The proposed framework creates a scalable base which enables AI-driven 6G networks to evolve towards complete autonomous operation of self-optimizing wireless networks that can handle new use cases and extremely crowded areas.