XL-MIMO AGV 车队中的可见性区域耦合：三重角色建模与掩码波束成形
Visibility-Region Coupling in XL-MIMO AGV Fleets: Triple-Role Modeling and Masked Beamforming
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中文总结 AI 辅助
研究 XL-MIMO AGV 车队中因金属环境导致的可见性区域耦合问题，提出基于 VR 耦合信道模型的三重角色效应，用掩码 WMMSE 开发 VR 感知下行链路波束成形框架，仿真显示可显著提升总和速率。
中文摘要 AI 辅助
超大规模多输入多输出（XL-MIMO）是支持智能港口终端中自动导引车（AGV）车队的一项有前景的技术。然而，金属容器环境会导致空间非平稳性，使得每个 AGV 仅对阵列的一个子集可见，即其可见性区域（VR）。与现有假设用户独立 VR 的 XL-MIMO 模型不同，我们表明每个 AGV 同时充当通信用户、金属散射体和阻挡器，从而导致用户信道和 VR 耦合。我们通过 VR 耦合信道模型来阐述这种“三重角色”效应，并基于掩码加权最小均方误差（WMMSE）开发了一个 VR 感知下行链路波束成形框架，其中掩码操作在显著降低计算复杂度的同时精确地强制执行 VR 支持约束。在实际智能港口场景中的仿真结果表明，与无 VR 感知的基线相比，总和速率提高了三倍多，并且随着车队密度的增加，增益变得越来越明显。
英文摘要
Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for supporting automated guided vehicle (AGV) fleets in smart port terminals. However, the metallic container environment induces spatial non-stationarity, whereby each AGV is visible to only a subset of the array, referred to as its visibility region (VR). Unlike existing XL-MIMO models that assume user-independent VRs, we show that each AGV simultaneously acts as a communication user, a metallic scatterer, and a blocker, resulting in coupled user channels and VRs. We formulate this \emph{triple-role} effect through a VR-coupled channel model and develop a VR-aware downlink beamforming framework based on masked weighted minimum mean-square error (WMMSE), where the masking operation exactly enforces VR support constraints while significantly reducing computational complexity. Simulation results in a realistic smart port scenario demonstrate more than a threefold sum-rate improvement over VR-unaware baselines, with the gains becoming increasingly pronounced as fleet density increases.
