Beyond Link-Level Enhancements: System-Level Optimizations in AI-Native 6G RANs

The increasing complexity of dense 6G RAN environments necessitates a paradigm shift from conventional linklevel optimization toward system-level control mechanisms that ensure intelligent coordination of radio resources, mobility, and device management across multiple coexisting RANs. Traditional optimization techniques, including advanced beamforming, modulation schemes, and interference mitigation, operate within isolated link boundaries, lacking the global awareness necessary for dense deployments. In contrast, system-wide AI-driven control frameworks enable holistic optimization, dynamically adjusting network parameters to adapt to fluctuating demand, user mobility patterns, and energy efficiency constraints. This paper proposes an AI-native system-level RAN control architecture, which redistributes core network functionalities to the edge and introduces a flexible deployment model: as part of C-RAN, as an independent edge core, or within the centralized network infrastructure. The proposed approach enhances power and frequency management, optimizes paging and mobility execution at the edge, and reduces core signaling overhead by executing localized resource coordination and interference control. AI plays a fundamental role in this transition, enabling real-time adaptive decision-making tailored to localized network conditions. By shifting from isolated, link-level improvements to a coordinated, AI-driven system-level optimization paradigm, the proposed framework enables efficient, scalable, and resilient dense RAN deployments. Future work will focus on prototype development and real-world validation, assessing the impact of AI-based RAN management and local mobility execution while ensuring seamless integration with existing 6G network architectures.