In millimeter-wave (mmWave) vehicular networks, dense base station (BS) deployments expand the user association (UA) decision space while dynamic blockages cause link quality fluctuations, posing critical challenges for effective mobility management. Traditional Multi-Armed Bandit (MAB) frameworks assume stationary reward distributions and fail to handle the rapid context-reward mapping shifts caused by vehicle mobility and transient blockages. To address this, we propose Blockage-Aware Non-stationary Dynamic Bandit (BAND), a fully distributed, channel state information (CSI)-free mobility management framework for mmWave vehicular networks, formulating UA as a non-stationary contextual bandit problem, enabling online adaptive optimization without requiring central coordination or offline training. BAND employs a cumulative sum-based change detection (CUSUM-CD) to dynamically narrow the active BS set, reducing exploration overhead while tracking reward distribution shifts. Proactive blockage detection suppresses transient signal degradation in the reward estimation process. Simulations demonstrate over 40% regret reduction and up to 33.1% network communication rate improvement compared with hypercube-based contextual bandit baselines, with robustness validated across varying blockage rates and network configurations.

翻译：在毫米波车联网中，密集基站部署扩展了用户关联决策空间，而动态阻塞引发链路质量波动，这对有效的移动性管理构成了关键挑战。传统多臂老虎机框架假设奖励分布平稳，无法处理车辆移动性和瞬时阻塞导致的快速上下文-奖励映射变化。为此，我们提出阻塞感知非平稳动态老虎机（BAND），这是一种完全分布式、无需信道状态信息的毫米波车联网移动性管理框架，将用户关联建模为非平稳上下文老虎机问题，无需中心协调或离线训练即可实现在线自适应优化。BAND采用基于累积和的变化检测动态缩小活跃基站集，在跟踪奖励分布变化的同时降低探索开销。主动阻塞检测可抑制奖励估计过程中的瞬时信号衰减。仿真表明，与基于超立方体的上下文老虎机基线相比，遗憾值降低超过40%，网络通信速率提升最高达33.1%，并且在不同阻塞率和网络配置下验证了其鲁棒性。