The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applying 6DMA to high-mobility Internet of Vehicles (IoV) scenarios faces significant challenges, primarily due to the difficulty of acquiring instantaneous Channel State Information (CSI) and the risk of service interruptions caused by mechanical reconfiguration delays. To address these issues, this paper proposes a low-complexity, CSI-free single-step reconfiguration framework. First, we design a deterministic discrete position generation scheme based on a latitude-longitude grid with inherent topological structures. Leveraging graph theory, we explicitly model and theoretically derive the lower bounds of movement and time costs for antenna reconfiguration. Subsequently, utilizing the directional sparsity of 6DMA channels, we develop an adaptive optimization strategy that fuses offline environmental priors with online historical feedback. Furthermore, a periodic reconfiguration mechanism based on predicted cumulative vehicle distributions is introduced. By strictly restricting antenna adjustments to the first-order spatial neighborhood, the proposed single-step method effectively eliminates service interruptions. Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed and global-search-based benchmarks in terms of uplink sum rate, while incurring negligible mechanical overhead and latency, thereby validating its feasibility and robustness in highly dynamic vehicular networks.

翻译：六维可移动天线（6DMA）系统通过充分利用空间自由度，已成为提升无线容量的前沿技术。然而，将6DMA应用于高速移动车联网场景面临重大挑战，主要源于难以获取瞬时信道状态信息（CSI）以及机械重构时延可能导致服务中断。针对上述问题，本文提出一种低复杂度、无需CSI的单步重构框架。首先，设计基于固有拓扑结构的经纬度网格确定性离散位置生成方案；借助图论方法，理论推导天线重构移动与时间代价的下界。其次，利用6DMA信道方向稀疏性，开发融合离线环境先验与在线历史反馈的自适应优化策略。进一步引入基于预测累积车辆分布的周期性重构机制。通过严格限制天线调整至一阶空间邻域，所提单步方法有效消除服务中断。仿真结果表明，与传统固定基准和全局搜索基准相比，本方案在上行和速率方面显著更优，同时仅引入可忽略的机械开销与延迟，验证了其在高度动态车辆网络中的可行性与鲁棒性。