3 papers
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 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…
cs.LG2026
Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features
Giambattista Amati, Federica Mangiatordi, Emiliano Pallotti +3
Reliable, low-latency uplink connectivity is a key requirement for C-V2X networks in dense urban environments, where fast channel variations and blockages often degrade direct vehi…