{"ID":2888125,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01060","arxiv_id":"2508.01060","title":"Connectivity Management in Satellite-Aided Vehicular Networks with Multi-Head Attention-Based State Estimation","abstract":"Managing connectivity in integrated satellite-terrestrial vehicular networks is critical for 6G, yet is challenged by dynamic conditions and partial observability. This letter introduces the Multi-Agent Actor-Critic with Satellite-Aided Multi-head self-attention (MAAC-SAM), a novel multi-agent reinforcement learning framework that enables vehicles to autonomously manage connectivity across Vehicle-to-Satellite (V2S), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Vehicle (V2V) links. Our key innovation is the integration of a multi-head attention mechanism, which allows for robust state estimation even with fluctuating and limited information sharing among vehicles. The framework further leverages self-imitation learning (SIL) and fingerprinting to improve learning efficiency and real-time decisions. Simulation results, based on realistic SUMO traffic models and 3GPP-compliant configurations, demonstrate that MAAC-SAM outperforms state-of-the-art terrestrial and satellite-assisted baselines by up to 14% in transmission utility and maintains high estimation accuracy across varying vehicle densities and sharing levels.","short_abstract":"Managing connectivity in integrated satellite-terrestrial vehicular networks is critical for 6G, yet is challenged by dynamic conditions and partial observability. This letter introduces the Multi-Agent Actor-Critic with Satellite-Aided Multi-head self-attention (MAAC-SAM), a novel multi-agent reinforcement learning fr...","url_abs":"https://arxiv.org/abs/2508.01060","url_pdf":"https://arxiv.org/pdf/2508.01060v1","authors":"[\"Ibrahim Althamary\",\"Chen-Fu Chou\",\"Chih-Wei Huang\"]","published":"2025-08-01T20:29:03Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}