mlsanityData to 14 Oct 2026

Connectivity Management in Satellite-Aided Vehicular Networks with Multi-Head Attention-Based State Estimation

arXiv 2508.01060 ↗First posted 1 Aug 2025cs.NI cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 22 references that are in the map. First posted on arXiv 1 Aug 2025.

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6 of 22 references.

References · 6

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  3. 2018Self-Imitation Learningcited by 138
  4. 2017Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learningcited by 117
  5. 2023Convergence Rates for Localized Actor-Critic in Networked Markov Potential Gamescited by 9
  6. 20206G Cellular Networks and Connected Autonomous Vehiclescited by 7

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