Polarization- and Mutual-Coupling-Aware Rotatable Antennas Enabled Wireless Communications: Modeling, Orientation Optimization, and Prototyping

Rotatable antennas (RAs) expand the spatial degrees of freedom of multiple-input multiple-output (MIMO) systems beyond conventional fixed-panel arrays. However, existing evaluations often model rotation merely as a scalar gain adjustment and overlook three hardware-critical effects: 1) the phase carried by the antenna pattern, 2) rotation-induced changes in the polarization, and 3) orientation-dependent mutual coupling variations. In this context, we develop a unified modeling-evaluation-optimization framework that explicitly incorporates phase, polarization, and mutual coupling effects through a rotation-explicit complex vector element pattern (CVEP). Polarization coupling and mismatch are captured by embedding the rotated CVEPs into a 2 × 2 per-ray polarization structure, while mutual coupling variation is modeled via power-consistent interpolation of embedded radiation patterns under joint orientation states. Additionally, a compact RA base-station (RA-BS) element and a 6 × 3 RA-BS prototype supporting ±60° mechanical rotation with a high front-to-back ratio are designed, assembled, and measured. The simulation and measurement results validate that the proposed electromagnetic (EM)-consistent RA system model accurately characterizes rotation-induced variations in gain, polarization, and radiation patterns. Furthermore, an alternating feasible-direction optimization algorithm, tailored to the jointly programmable BS- and subarray-level rotations, is developed to maximize the downlink sum-rate in multi-user MIMO deployments. Our simulations demonstrate that the proposed rotation-aware RA-BS architecture significantly outperforms conventional gain-only and mutual-coupling-neglected baselines. These findings highlight the necessity of incorporating phase, polarization, and mutual coupling effects into RA modeling and optimization to ensure robust performance under uniform user distributions and maximize achievable throughput in hotspot deployments.