学习优化：AI原生网络中缺失的架构层
Learning-to-Optimize as the Missing Architectural Layer of AI-Native Networks
- Fondazione Ugo Bordoni（乌戈·博尔多尼基金会）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出将学习优化（L2O）作为AI原生网络缺失的架构层，通过四阶段流程将优化算法转为知识生成器，训练神经代理模型实现低延迟推理，并以NR-V2X中继选择为例验证其有效性。
AI中文摘要：
人工智能（AI）正成为未来AI原生通信网络的基本设计原则，能够实现自主资源管理、自适应控制和零接触网络运营。虽然当前的AI原生架构越来越多地将智能嵌入到网络功能中，但它们对于优化知识应如何被AI模型系统地生成、转移和利用提供的指导甚少。本文认为，学习优化（L2O）代表了优化与AI原生智能之间缺失的架构层。所提出的范式并非将优化仅仅视为在线决策引擎，而是将优化算法重新定义为离线知识生成器，为神经代理模型产生高质量的监督信息。由此产生的模型继承了优化专业知识，同时实现了适用于动态网络环境的低延迟运行时推理。本文介绍了一个通用的四阶段L2O工作流程，包括优化、知识生成、代理学习和运行时推理。与现有的主要关注算法加速的学习优化方法不同，所提出的框架将L2O确立为一种适用于异构通信和计算系统的架构抽象。所提出的范式通过一个NR-V2X中继选择问题加以说明，在该问题中，来自混合整数线性规划（MILP）求解器的优化生成解决方案被用于训练一个图神经网络，该网络能够实时复现近最优决策。所呈现的观点将学习优化定位为未来AI原生网络的关键架构推动因素。
英文摘要：
Artificial Intelligence (AI) is becoming a fundamental design principle of future AI-native communication networks, enabling autonomous resource management, adaptive control, and zero-touch network operation. While current AI-native architectures increasingly embed intelligence across network functions, they provide little guidance on how optimisation knowledge should be systematically generated, transferred, and exploited by AI models. This paper argues that the Learning-to-Optimize (L2O) represents the missing architectural layer between optimisation and AI-native intelligence. Rather than viewing optimisation merely as an online decision engine, the proposed paradigm redefines optimisation algorithms as offline knowledge generators that produce high-quality supervisory information for neural surrogate models. The resulting models inherit optimisation expertise while enabling low-latency runtime inference suitable for dynamic network environments. A generic four-stage L2O workflow is introduced, comprising optimisation, knowledge generation, surrogate learning, and runtime inference. Unlike existing Learning-to-Optimize approaches, which primarily focus on algorithm acceleration, the proposed framework establishes L2O as an architectural abstraction applicable across heterogeneous communication and computing systems. The proposed paradigm is illustrated by an NR-V2X relay-selection problem, in which optimisation-generated solutions from a Mixed-Integer Linear Programming (MILP) solver are used to train a Graph Neural Network that can reproduce near-optimal decisions in real time. The presented perspective positions Learning-to-Optimize as a key architectural enabler for future AI-native networks.
