AI 中文总结
针对毫米波异构车联网的阻塞与信道波动问题，提出BAND及半分布式S-BAND算法，结合TAK区域与KIF度量，在10%-50%阻塞率下较集中式MAB基线实现34.9%-59.4%的遗憾降低，TAK区域性能优于K-means聚类。
AI 中文摘要
6G车万物联网（V2X）通信的愿景要求为复杂动态环境中的完全自动驾驶提供可靠、自适应的连接。毫米波（mmWave）异构车联网中的用户关联（UA）是该问题的一个极具挑战性的实例，其中动态阻塞和快速信道变化持续破坏传统多臂老虎机（MAB）框架的平稳奖励假设。本文提出了一种完全分布式的感知阻塞的非平稳动态多臂老虎机算法（BAND）及其半分布式扩展S-BAND，用于车辆间的协作学习。将阻塞预测纳入变化检测（CD）机制以抑制虚警，同时动态基站（BS）集管理方案在大规模BS部署中平衡探索与利用，无需集中式信道状态信息（CSI）获取或离线训练。在S-BAND中，车辆将BS奖励估计值积累为本地知识，并定期将其上传至宏基站（MBS），MBS将其聚合为基于集群的中心知识。提出轨迹对齐知识（TAK）区域以捕获毫米波信道特性的空间相关性，引入知识继承保真度（KIF）度量以量化知识转移质量。在真实城市拓扑上的仿真结果表明，与集中式MAB基线相比，BAND和S-BAND分别实现了34.9%和59.4%的遗憾降低，且在10%至50%的阻塞率范围内性能增益持续存在。所提出的TAK区域在两种保真度准则下均始终优于传统的K-means聚类方案。
英文摘要
The vision for 6G vehicle-to-everything (V2X) communications demands reliable, adaptive connectivity for fully autonomous driving across complex dynamic environments. Millimeter-wave (mmWave) user association (UA) in heterogeneous vehicular networks presents a particularly demanding instance of this problem, where dynamic blockages and rapid channel variations continuously undermine the stationary reward assumptions of traditional multi-armed bandit (MAB) frameworks. This paper proposes a fully distributed blockage-aware non-stationary dynamic bandit algorithm (BAND) and its semi-distributed extension S-BAND for cooperative learning across vehicles. Blockage prediction is incorporated into the change-detection (CD) mechanism to suppress false alarms, while a dynamic base station (BS) set management scheme balances exploration and exploitation across large-scale BS deployments without requiring centralized channel state information (CSI) acquisition or offline training. In S-BAND, vehicles accumulate BS reward estimates as local knowledge and periodically upload them to the macro base station (MBS), which aggregates them into cluster-based central knowledge. A trajectory-aligned knowledge (TAK) region is proposed to capture the spatial correlation of mmWave channel characteristics. A knowledge inheritance fidelity (KIF) metric is introduced to quantify knowledge transfer quality. Simulation results on a realistic urban topology show that BAND and S-BAND achieve 34.9% and 59.4% regret reduction relative to a centralized MAB baseline, with performance gains sustained across blockage rates ranging from 10% to 50%. The proposed TAK region consistently outperforms the traditional K-means clustering scheme under both fidelity criteria.
