有保障的AI原生网络控制环路：最新进展、研究挑战与缺失的运行时保障层
Assured AI-Native Network Control Loops: State of the Art, Research Challenges and the Missing Runtime Assurance Layer
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- Poznan Supercomputing and Networking Center（波兹南超级计算与网络中心）
- Poznan University of Technology（波兹南理工大学）
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中文总结 AI 辅助
本文综述AI原生网络控制环路，指出缺乏统一运行时保障机制，提出依赖感知的运行时保障方向，以支持异构控制环路的有保障组合。
中文摘要 AI 辅助
向自主和AI原生电信网络的演进，正在将网络控制从预定义的自动化转变为分布式智能决策。闭环自动化、O-RAN、网络数字孪生、AI驱动的编排和自主智能体方面的进展，使得多个专用控制功能能够在网络域和时间尺度上并发运行。这引入了系统级的保障挑战：单独可接受的决策可能通过共享资源和网络状态相互作用，而其运行上下文的变化可能使评估这些决策时所依据的假设失效。本文对AI原生网络控制进行了最新综述，重点关注自主控制环路的组合与运行时保障。它考察了闭环和零接触自动化、智能控制器、网络数字孪生、AI驱动的编排、自主智能体、可信AI和运行时保障。分析表明，这些方向为自主运行提供了重要基础，但缺乏一种统一机制来保障异构控制环路，这些环路的决策依赖于共享且动态变化的网络状态。为解决这一空白，本文提出将依赖感知的运行时保障作为AI原生网络控制环路有保障组合的研究方向。所提出的视角将决策与其有效性所依赖的假设和依赖关联起来，监控可能使已接受决策失效的变化，并支持并发控制交互的运行时解决。一个电信用例以及初步架构和形式模型说明了这一概念，并识别了依赖表示、运行时验证、冲突解决、延迟感知保障和实验评估方面的开放挑战。
英文摘要
The evolution towards autonomous and AI-native telecommunication networks is transforming network control from predefined automation towards distributed and intelligent decision-making. Advances in closed-loop automation, O-RAN, Network Digital Twins, AI-driven orchestration, and autonomous agents enable multiple specialized control functions to operate concurrently across network domains and timescales. This introduces a system-level assurance challenge: individually acceptable decisions may interact through shared resources and network state, while changes in their operational context may invalidate assumptions under which they were evaluated. This paper presents a state-of-the-art review of AI-native network control, focusing on the composition and runtime assurance of autonomous control loops. It examines closed-loop and zero-touch automation, intelligent controllers, Network Digital Twins, AI-driven orchestration, autonomous agents, trustworthy AI, and runtime assurance. The analysis shows that these directions provide important foundations for autonomous operation but lack a unified mechanism for assuring heterogeneous control loops whose decisions depend on shared and dynamically changing network state. To address this gap, the paper identifies dependency-aware runtime assurance as a research direction for assured composition of AI-native network control loops. The proposed perspective associates decisions with assumptions and dependencies on which their validity relies, monitors changes that may invalidate accepted decisions, and supports runtime resolution of concurrent control interactions. A telecom use case and an initial architecture and formal model illustrate the concept and identify open challenges in dependency representation, runtime validation, conflict resolution, latency-aware assurance, and experimental evaluation.
