Al-Native 6G Network Management
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I1P103Keywords:
AI-native network management, 6G wireless networks, Network automation, Machine learning in networks, Multi-agent reinforcement learning, Network digital twins, Intent-based orchestration, Semantic communication, Self-optimizing networksAbstract
The evolution toward sixth-generation (6G) wireless systems demands a shift from traditionally engineered networks to AI-native network management, where intelligence is embedded across the entire architectural stack. As 6G targets extreme performance metrics sub-millisecond latency, terabit-level throughput, integrated sensing, and massive device density, legacy management approaches become insufficient (Dang et al., 2020; Zhang et al., 2021). Recent studies argue that embedding machine learning, distributed intelligence, and real-time automation into network control is essential for managing future complexity and enabling autonomous operation (Saad et al., 2020; Chen et al., 2022). AI-native management integrates technologies such as multi-agent reinforcement learning, network digital twins, semantic communication, and intent-based orchestration to create self-optimizing, self-healing, and context-aware networks (Flórez et al., 2023; Mahmood et al., 2024). This paper examines the conceptual foundations, architectural principles, and enabling technologies for AI-native 6G network management. It further analyzes emerging challenges—including data sparsity, model explainability, cross-domain trust, and scalability and outlines open research directions critical for achieving fully autonomous, resilient, and adaptive 6G networks. The findings suggest that AI-native design is not an enhancement but a fundamental requirement for realizing the operational ambitions of next-generation communication systems
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