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This research introduces a Deep Reinforcement Learning framework enhanced by Large Language Models to optimize UAV relay deployment in fragmented vehicular networks, significantly reducing training episodes and energy consumption while improving connectivity. By integrating semantic road topology with dynamic traffic states via Logit Fusion, the proposed SA-PPO algorithm offers a robust solution for maintaining network cohesion in complex urban environments without relying on excessive computational resources.
Bridging Network Fragmentation: A Semantic-Augmented DRL Framework for UAV-aided VANETs
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 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 with the PPO policy through Logit Fusion, guiding exploration toward promising intersections while retaining adaptation through environmental returns. Simulations driven by real-world urban trajectories show that SA-PPO reaches the final converged reward of Vanilla PPO using only 28.6% of its training episodes. It improves the average number of vehicles in connected components and the average connected-component size by 7.9% and 8.7%, respectively, while reducing UAV energy consumption by 21.3%.
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 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 with the PPO policy through Logit Fusion, guiding exploration toward promising intersections while retaining adaptation through environmental returns. Simulations driven by real-world urban trajectories show that SA-PPO reaches the final converged reward of Vanilla PPO using only 28.6% of its training episodes. It improves the average number of vehicles in connected components and the average connected-component size by 7.9% and 8.7%, respectively, while reducing UAV energy consumption by 21.3%.
Revised version. This update includes substantial improvements to the methodology, ablation studies, temporal robustness analysis, and cross-city generalization experiments. Submitted to IEEE Transactions on Cognitive Communications and Networking. 15 pages, 14 figures
Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
arXiv:2603.18871 [cs.AI]
(or arXiv:2603.18871v2 [cs.AI] for this version)
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