Robust beamforming and position optimization for movable antenna enabled 6G wireless communications

Although movable antenna (MA) systems offer significant performance gains via spatial reconfiguration, they suffer from severe degradation under practical imperfect channel state information (CSI). To address this, this paper proposes a robust joint design of antenna position optimization and precoding. We model stochastic errors in angular parameters and path gains and develop a Robust Monte Carlo Orthogonal Matching Pursuit (RMC-OMP) algorithm. The algorithm effectively suppresses pseudo-correlation peak interference caused by CSI errors through statistically averaging matching scores across multiple channel error realizations, while strictly ensuring the half-wavelength spacing constraint. Simulation results demonstrate that the RMC-OMP retains $$85.6%$$ of the sum rate achievable with perfect CSI. This validates the proposed algorithm’s strong robustness for 6G MA systems.