Bayesian Optimization-Based Antenna Position Training for Movable Antenna Enhanced Wireless Communications

Movable antennas (MAs) have garnered significant attention in communication systems due to their flexible geometry arrays, which introduce a new degree of freedom. Numerous studies demonstrate the potential of MAs to enhance communication performance. However, existing approaches rely on available channel state information (CSI) for joint antenna positioning and beamforming optimization, which poses practical challenges. In real-world systems, acquiring the necessary spatial channel information, such as path angles, with low pilot overhead is difficult. Additionally, time-varying channels further complicate MA channel estimation. To address these challenges, this letter proposes a Bayesian optimization-based approach to search for optimal antenna positions without requiring CSI. The method achieves efficient position training with minimal pilot overhead. Furthermore, we extend the framework to enable Bayesian optimization-based position tracking in time-varying channel conditions.