AI-Native O-RAN Architectures for 6G: Toward Real-Time Adaptation, Conflict Resolution, and Efficient Resource Management
Sif Eddine Salmi, Messaoud Ahmed Ouameur, Miloud Bagaa, George C. Alexandropoulos, ABDELLAH TAHENNI, Daniel Massicotte, Adlen Ksentini · IEEE Transactions on Network and Service Management · 2026
Open Radio Access Network (O-RAN) enables modular and intelligent control of radio resources through open interfaces and programmable RAN components. As networks evolve toward sixth-generation (6G) systems, the proliferation of autonomous xApps and rApps introduces a critical challenge: Coordinating concurrent AI-driven control actions under tight near-real-time constraints while avoiding instability and conflicting decisions. This paper focuses on two tightly coupled enablers for AI-native O-RAN orchestration: Conflict-aware control and intent-driven automation. We propose an AI-native orchestration framework centered on a CME integrated into the Near-RT RIC, and a complementary LLM-based intent orchestration module deployed in the Non-RT RIC. The CME is designed to autonomously arbitrate conflicting xApp actions by learning adaptive mitigation policies from structured conflict signals, system context, and performance feedback, rather than relying on static priorities or predefined conflict classes. The LLM module translates high-level operator intents into policy constraints and control objectives that guide conflict resolution and xApp behavior. Overall, this work advances AI-native O-RAN orchestration by grounding conflict-aware control and LLM-assisted intent translation in practical measurements, and by outlining a clear path toward scalable, adaptive, and resilient control mechanisms required for future 6G RIC deployments.