ML-driven network orchestrator for 6G O-RAN: Resolving multi-xApp conflicts in near-RT RIC

Le résumé fourni par la source

The evolution toward 6G networks necessitates a shift toward fully automated, AI-native, and zero-touch operational solutions to handle unprecedented network complexity. Within the Open RAN (O-RAN) architecture, the near-Real-Time RAN Intelligent Controller (near-RT RIC) plays a pivotal role in hosting third-party xApps for radio resource management. However, the deployment of multiple xApps from different vendors often leads to operational conflicts, such as overlapping control commands and resource competition, which can degrade network performance. This paper addresses this challenge by proposing an ML-driven network orchestrator specifically designed for conflict detection and resolution in a multi-xApp 6G O-RAN environment. Our framework utilizes advanced machine learning techniques to autonomously identify potential steering conflicts and execute resolution strategies in real time. Experimental results demonstrate that the ML-driven orchestrator effectively mitigates performance degradation caused by xApp collisions, maintaining high-fidelity service delivery even under dense traffic conditions. This study contributes to the realization of self-optimizing 6G networks by providing a scalable and resilient orchestration logic for future autonomous RAN operations.

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