迈向全自主6G网络：AI驱动的运营效率与优化
Toward Fully Autonomous 6G Networks: AI-driven Operational Efficiency and Optimization
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中文总结 AI 辅助
本研究提出基于智能体的编排框架，融合AI原生RAN与NaaS，实现自主6G RAN管理，提升运营效率与优化。
中文摘要 AI 辅助
移动网络的演进以系统复杂性的显著增加为特征，这一趋势源于在数字基础设施之上容纳日益增长的异构服务的需求。随着网络即服务（NaaS）范式的采用，这种服务容纳的增长预计将加速，该范式已成为加速网络创新并为运营商开辟新收入来源的有前景的方法。尽管抽象网络能力以供第三方开发者使用至关重要，但它对高效网络运营提出了重大挑战。为应对这一增加的复杂性，未来移动网络被设想为本质上是人工智能（AI）原生的。特别是，AI在无线接入网（RAN）中的集成成为优化运营、能耗和自主网络控制的关键推动因素。在此背景下，本研究探索AI原生RAN与NaaS生态系统的融合，以实现自主6G RAN管理。我们提出了一种基于智能体的编排框架，能够解释基于意图的策略。所提出的框架成为整合外部NaaS请求与内部网络管理策略的关键。
英文摘要
Mobile networks evolution is characterized by a substantial increase in system complexity, driven by the need to accommodate a growing number of heterogeneous services on top of the digital infrastructure. This growth in service accommodation is expected to accelerate with the adoption of the Network as a Service (NaaS) paradigm, which has emerged as a promising approach to accelerate network innovation while enabling new revenue streams for operators. Although it is fundamental to abstract network capabilities for third-party developers, it poses significant challenges in terms of efficient network operation. To address this increased complexity, future mobile networks are envisioned to be inherently Artificial Intelligence (AI)-native. In particular, the integration of AI within the Radio Access Network (RAN) becomes a key enabler for optimizing operation, energy consumption, and autonomous network control. In this context, this research explores the convergence of AI-native RAN and NaaS ecosystems to enable autonomous 6G RAN management. We propose an Agentic-based orchestration framework capable of interpreting intent-based policies. The proposed framework becomes key to integrate external NaaS requests with internal network management policies.
发表机构
- UPC, Spain（加泰罗尼亚理工大学）
- i2CAT Foundation, Spain（i2CAT基金会）
机构由 AI 辅助整理，请以论文原文为准。
