Joint Channel Scanning and Geometry-Aware Codebook Design for Movable Antenna Arrays

Abstract

Multiple-input multiple-output (MIMO) systems rely on spatial degrees of freedom, but fixed antenna geometries cannot adapt to propagation environments. Movable antenna (MA) systems address this by adapting antenna positions to the channel. In discrete MA architectures, active antennas are selected from candidate positions, requiring channel state information (CSI) at those positions. With limited radio-frequency (RF) chains, CSI is acquired through successive pilot rounds, or antenna scanning, whose overhead can offset reconfiguration gains. This paper studies discrete MA geometry optimization when only a fraction of candidate positions is scanned. We show that a geometry-aware (GA) steering-vector codebook, constructed from the actual antenna coordinates, reconstructs channels more accurately than a conventional discrete Fourier transform (DFT) codebook when the optimized array departs from a uniform grid. We then show that the scanning fraction required to approach full-scan sum-rate performance depends on the CSI acquisition method: least-squares (LS) estimation degrades sharply under sparse scanning, while codebook-based acquisition remains robust. We also propose an adaptive scanning procedure that probes candidates in batches and stops automatically using the acquired pilots, avoiding a preset scanning budget. Finally, we quantify sum-rate losses as users move from their initial positions and evaluate recovery through re-scanning and geometry re-selection, relative to fixed-aperture operation with outdated or fresh CSI. Numerical results show that adaptive scanning achieves about 99.5% of the full-scan sum rate while scanning only 11% of candidate positions.