用注意力机制让车辆自主管理卫星车联网连接，提升通信稳定性。
Connectivity Management in Satellite-Aided Vehicular Networks with Multi-Head Attention-Based State Estimation
- 引入多头注意力机制，解决车辆间信息共享不足时的状态估计问题。
- 仿真显示传输效率比现有方法最高提升14%，适配不同车流密度。
- 适合6G车联网、智能交通系统研究者关注，尤其关心动态连接管理的场景。
在集成式星地车载网络中，连接管理对6G至关重要，但面临动态环境与信息不完全可观测的挑战。本文提出多智能体强化学习框架MAAC-SAM，结合星地辅助的多头自注意力机制，使车辆能自主管理车-星（V2S）、车-基础设施（V2I）和车-车（V2V）链路。核心创新在于多头注意力机制，可在车辆间信息波动且有限的情况下实现鲁棒状态估计。框架还融合自模仿学习（SIL）与指纹技术，提升学习效率与实时决策能力。基于真实SUMO交通模型和3GPP兼容配置的仿真结果表明，MAAC-SAM在传输效用上相比先进基线最高提升14%，并在不同车辆密度与信息共享水平下保持高估计算精度。
原文摘要 · Abstract (English)
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.
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