Efficient Discrete Position Design for Movable Antenna Systems: Low Complexity and Robustness
2026-08-07 • Information Theory
Information Theory
AI summaryⓘ
The authors study movable antennas (MAs), which can physically change their positions to improve wireless communication. They focus on maximizing data capacity in systems with multiple users and antennas (MU-MIMO) by selecting antenna positions from a fixed set, a problem that is usually very complex to solve. They prove the problem has a special structure (monotone submodular maximization) and develop a fast algorithm that finds good antenna placements efficiently. Their method works well even when the channel information is not perfect and is much faster than traditional approaches while achieving most of the optimal performance.
Movable antennas (MAs)MU-MIMOMutual informationChannel state informationSubmodular maximizationDiscrete positioningUplink communicationAlgorithm complexityBranch-and-boundRobust optimization
Authors
Haonan Wang, Xianghao Yu, Rui Wang, Ang Li, Ying-Jun Angela Zhang
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.