流体天线阵列中互耦下的离网位置优化
Off-Grid Position Optimization under Mutual Coupling in Fluid Antenna Arrays
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中文总结 AI 辅助
本文针对流体天线阵列的互耦问题，提出电磁感知波束成形框架，结合相位检索、OMP方法与Adam优化，可提升双波束目标的主瓣SNR并降低PSLL。
中文摘要 AI 辅助
流体天线阵列利用有限孔径内的连续天线重定位，提供超出网格约束端口选择的几何多样性。然而，每次位移都会改变辐射响应和多端口互阻抗网络，将几何优化与源电压约束耦合起来。本文提出一种面向平面流体天线阵列的电磁感知（EM感知）波束成形框架：相位检索将仅含幅度的成形波束规范转换为孔径兼容的复目标，EM感知正交匹配追踪（OMP）方法选择网格约束的初始天线位置；连续优化交替进行精确的电压约束电流优化，以及所有物理天线位置的运动约束投影自适应矩估计（Adam）更新。在独立扰动的对称双波束目标上，与均匀阵列和离散端口选择相比，所提方法持续提升平均主瓣信噪比（SNR）并降低峰值旁瓣电平（PSLL）。
英文摘要
Fluid antenna arrays exploit continuous antenna repositioning within a finite aperture to provide geometry diversity beyond grid-constrained port selection. Every displacement, however, changes both the radiation response and the multiport mutual-impedance network, coupling geometry optimization with the source-voltage constraint. This paper develops an electromagnetic-aware (EM-aware) beamforming framework for planar fluid antenna arrays. Phase retrieval converts an amplitude-only shaped-beam specification into an aperture-compatible complex target, and an EM-aware orthogonal matching pursuit (OMP) method selects grid-constrained initial antenna positions. Continuous refinement then alternates exact voltage-constrained current optimization with movement-constrained projected adaptive moment estimation (Adam) updates of all physical antenna positions. Across independently perturbed symmetric dual-beam targets, the proposed method consistently improves the average mainlobe signal-to-noise ratio (SNR) and reduces the peak sidelobe level (PSLL) over a uniform array and discrete port selection.
