Bridging Network Fragmentation: A Semantic-Augmented DRL Framework for UAV-aided VANETs

Excerpt

arXiv:2603.18871v2 Announce Type: replace Abstract: Urban Vehicular Ad-Hoc Networks (VANETs) can become fragmented because buildings obstruct wireless links and vehicle mobility continuously changes the network topology. Unmanned Aerial Vehicles (UAVs) can serve as mobile relays, but Deep Reinforcement Learning (DRL)-based deployment often suffers from inefficient exploration because it lacks road-topology guidance. To address this problem, we propose Semantic-Augmented DRL (SA-DRL), which model