Bridging Network Fragmentation: A Semantic-Augmented DRL Framework for UAV-aided VANETs
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 models network fragmentation over the road topology and aligns a pretrained Large Language Model (LLM) to generate a topology-dependent action prior from dynamic traffic states. The resulting Semantic-Augmented PPO (SA-PPO) algorithm combines this prior ...
| Authors | Gaoxiang Cao, Wenke Yuan, Huasen He et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 March 2026 |
| Last updated at source | 14 August 2026 |
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