AI 中文总结
研究元件级变化对RIS辐射特性的影响，提出统计模型捕捉变容二极管电容波动影响，用低复杂度贪婪优化方法优化预期辐射功率，可量化元件敏感性，提升系统性能，典型尺寸下有较好增益和旁瓣抑制。
AI 中文摘要
本文介绍了一种新颖的分析框架，用于表征元件级变化对可重构智能表面（RIS）辐射特性的影响。具体而言，提出了一个统计模型来捕捉变容二极管电容波动对RIS反射系数的影响，进而影响功率辐射方向图。研究了低方差和大方差独立扰动场景。利用该统计模型，提出了一种低复杂度贪婪优化方法，旨在优化预期的RIS辐射功率，从而生成本质上稳健的配置。此外，所提出的分析模型是计算成本高昂的蒙特卡罗模拟的有效替代方法，能够量化元件对制造和操作公差的敏感性。如所示，优化平均功率方向图可显著提高元件级变化下的系统性能。对于典型的RIS尺寸（如32x32或64x64），实现了超过2 dB的主瓣增益和约10 dB的旁瓣抑制。
英文摘要
In this paper, a novel analytical framework to characterize the impact of element-level variations on the radiation characteristics of reconfigurable intelligent surfaces (RISs) is introduced. Specifically, a statistical model is proposed to capture the effects of varactor capacitance fluctuations on the RIS reflection coefficients, and, subsequently, on the resulting power radiation pattern; both low- and large-variance independent perturbation scenarios, are investigated. Leveraging the proposed statistical model, a low complexity greedy optimization methodology is presented, having the goal to optimize the expected RIS radiation power, thereby, generating inherently robust configurations. Furthermore, the analytical proposed model serves as an efficient alternative to computationally expensive Monte Carlo simulations, enabling the quantification of element sensitivity to manufacturing and operational tolerances. As demonstrated, optimizing the mean power pattern significantly enhances system performance under element-level variations. For typical RIS sizes (e.g., 32x32 or 64x64), a main lobe gain exceeding 2 dB and a sidelobe suppression of approximately 10 dB are achieved.
