AI 中文总结
本文针对离散可移动天线辅助的MU-MIMO上行链路的MI最大化问题，提出低复杂度鲁棒次模位置搜索算法，复杂度大幅降低且MI增益接近最优，适用于完美与不完美CSI场景。
AI 中文摘要
基于可重构天线技术的进展，可移动天线（MAs）能够动态重塑天线阵列并引入额外的空间自由度（DoFs），从而进一步提升通信性能。尽管具备这些优势，现有的MA设计算法往往因离散位置选择而产生过高的计算复杂度，阻碍了MA的实际应用。本文研究由离散MA辅助的多用户多输入多输出（MU-MIMO）上行链路通信系统的互信息（MI）最大化问题的高效解决方案。为此，我们首先在完美信道状态信息（CSI）假设下构建离散MA位置设计问题，随后证明该设计问题属于受双系统约束的单调次模最大化问题。据此，我们提出一种低复杂度的距离约束次模位置搜索算法，理论上证明该算法至少能达到最优解的1/3性能。此外，我们将该方法扩展至不完美CSI场景，结果表明所提出的基于次模优化的设计对信道估计误差仍具有鲁棒性。数值结果显示，在完美和不完美CSI假设下，所提方案均能达到最优解至少90%的MI增益，且算法复杂度实现了数量级的降低（例如，比分支定界方法快34.4倍），同时保持显著的MI增益。
英文摘要
Building on advances in reconfigurable antenna techniques, movable antennas (MAs) can dynamically reshape antenna arrays and introduce additional spatial degrees of freedom (DoFs), thereby further improving communication performance. Despite these benefits, existing MA design algorithms often entail prohibitively high computational complexity from discrete positioning selection, which prevents practical implementations of MAs. In this paper, we investigate efficient solutions for the mutual information (MI) maximization problem of a multi-user multiple-input multiple-output (MU-MIMO) uplink communication system aided by discrete MAs. To this end, we first formulate the discrete MA positioning problem with the assumption of perfect channel state information (CSI). Then, we prove that the design problem falls into the category of monotone submodular maximization subject to a 2-system constraint. Accordingly, we propose a low-complexity distance-constrained submodular position search algorithm, which is theoretically shown to achieve at least 1/3 of the optimum. Furthermore, we extend our approach to scenarios with imperfect CSI, and show that the proposed submodular optimization-based design remains robust against channel estimation errors. Numerical results demonstrate that the proposed scheme can achieve at least 90% of the optimal solution's MI gain under both perfect and imperfect CSI assumptions. Remarkably, the algorithm achieves orders-of-magnitude complexity reduction (e.g., 34.4x faster than the branch-and-bound approach) while maintaining significant MI gains.
Comments16 pages, 17 figures, 1 table. Part of this work was presented at IEEE ICC 2026, Glasgow, UK
