Problem
Research questions and friction points this paper is trying to address.
Learning-to-Optimize
AI-native networks
optimisation knowledge
neural surrogate models
dynamic network environments
Learning-to-Optimize as the Missing Architectural Layer of AI-Native Networks
🤖 AI Summary
论文提出将Learning-to-Optimize视为AI原生网络中缺失的架构层，通过优化算法生成高质量监督信息训练神经模型，以解决动态网络环境中的资源管理和控制问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Learning-to-Optimize
AI-Native Networks
Neural Surrogate Models
Low-Latency Inference
Graph Neural Network
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