Efficient Discrete Position Design for Movable Antenna Systems: Low Complexity and Robustness

2026-08-07

AI Summary

This work addresses the high computational complexity arising from discrete antenna placement in reconfigurable antenna systems by formulating, for the first time, the antenna layout optimization in multi-user MIMO uplink communications as a monotone submodular maximization problem subject to 2-system constraints. Leveraging submodular optimization theory, the authors propose a low-complexity algorithm that integrates distance-constrained search with robustness analysis, guaranteeing a theoretical performance bound of at least one-third under both perfect and imperfect channel state information. Experimental results demonstrate that the proposed method achieves over 90% of the optimal mutual information gain while accelerating computation by 34.4× compared to the branch-and-bound approach, substantially reducing complexity and exhibiting strong robustness against channel estimation errors.

Abstract

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.

Problem

• movable antennas
• discrete positioning
• computational complexity
• mutual information maximization
• MU-MIMO

Innovation

• movable antennas
• submodular optimization
• discrete position design
• low complexity
• robustness