arXiv:2607. 20554v1 Announce Type: new Abstract: Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments.
By Giambattista Amati, Federica Mangiatordi, Simone Angelini, Emiliano Pallotti, Pierpaolo Salvo
The paper introduces an AI-driven Learning-to-Optimise framework that uses Graph Neural Networks to select real‑time multi‑hop relays in NR‑V2X networks for smart cities. By modelling the vehicular network as a graph and training a GINE model with MILP‑derived optimal decisions, the method achieves near‑optimal connectivity, improving connectivity by up to 11.3% and speeding up computation by up to 100×. This enables scalable, real‑time network control suitable for Industry 4.0 and smart‑city deployments.
By Giambattista Amati, Federica Mangiatordi, Emiliano Pallotti, Simone Angelini
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.
By Giambattista Amati, Federica Mangiatordi, Pierpaolo Salvo, Emiliano Pallotti, Simone Angelini
arXiv:2608.28627v1 Announce Type: new
Abstract: Designing high-performance tactical wireless networks under realistic operational constraints gives rise to challenging combinatorial optimization prob...
By Wissem Ahmed Zaid, Alain Hertz, Defeng Liu
arXiv:2503. 03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal solutions, thereby reducing reliance on expert knowledge for algorithm design.
By Changliang Zhou, Xi Lin, Zhenkun Wang, Qingfu Zhang
arXiv:2609.26275v1 Announce Type: new
Abstract: The vehicle routing problems with real-world constraints (we consider vehicles capacity limits, time windows constrains, pickup-and-delivery multi-depo...
By Andrew Soroka, Alex Meshcheryakov