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cs.AI2026
Learning-to-Optimize as the Missing Architectural Layer of AI-Native Networks
Giambattista Amati, Federica Mangiatordi, Pierpaolo Salvo +2
Artificial Intelligence (AI) is becoming a fundamental design principle of future AI-native communication networks, enabling autonomous resource management, adaptive control, and z…
cs.AI2026
AI-Driven Real-Time Relay Optimisation in Smart Urban NR-V2X Networks via Learning-to-Optimise Graph Neural Networks
Giambattista Amati, Federica Mangiatordi, Emiliano Pallotti +1
Reliable and low-latency communication is a fundamental requirement for smart city services and Industry 4.0 applications enabled by NR-V2X networks. However, limited Road-Side Uni…
cs.AI2026
AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks
Giambattista Amati, Federica Mangiatordi, Simone Angelini +2
Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited Road-Side Unit (RSU) density, frequent non-line-of-sig…