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arXiv AI By Giambattista Amati, Federica Mangiatordi, Pierpaolo Salvo, Emiliano Pallotti, Simone Angelini

Learning-to-Optimize as the Missing Architectural Layer of AI-Native Networks

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The paper proposes that Learning-to-Optimize (L2O) should serve as a missing architectural layer in AI-native communication networks, bridging optimisation and AI intelligence. It redefines optimisation algorithms as offline knowledge generators that produce supervisory data for neural surrogate models, enabling low‑latency inference in dynamic environments. A four‑stage workflow—optimisation, knowledge generation, surrogate learning, and runtime inference—is introduced, and demonstrated on an NR‑V2X relay‑selection problem where a Graph Neural Network learns near‑optimal decisions from MILP solutions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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