Electronically tunable metasurfaces, or Intelligent Reflective Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflective elements. Capitalizing on key practical limitations of IRS-aided beamforming pertaining to system modeling and channel sensing/estimation, we propose a novel, fully data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for general two-stage (i.e., short/long-term), fully-passive IRS-aided stochastic utility maximization. ZoSGA learns long-term optimal IRS beamformers jointly with short-term optimal precoders (e.g., WMMSE-based) via minimal zeroth-order reinforcement and in a strictly model-free fashion, relying solely on the \textit{effective} compound channels observed at the terminals, while being independent of channel models or network/IRS configurations. Another remarkable feature of ZoSGA is being amenable to analysis, enabling us to establish a state-of-the-art (SOTA) convergence rate of the order of $\boldsymbol{O}(\sqrt{S}\epsilon^{-4})$ under minimal assumptions, where $S$ is the total number of IRS elements, and $\epsilon$ is a desired suboptimality target. Our numerical results on a standard MISO downlink IRS-aided sumrate maximization setting establish SOTA empirical behavior of ZoSGA as well, consistently and substantially outperforming standard fully model-based baselines. Lastly, we demonstrate that ZoSGA can in fact operate \textit{in the field}, by directly optimizing the capacitances of a varactor-based electromagnetic IRS model (unknown to ZoSGA) on a multiple user/IRS, compute-heavy network setting, with essentially no computational overheads or performance degradation.

翻译：电子可调谐超表面，或称智能反射面（IRS），是一种在现代无线系统中通过大量可调谐无源反射元件塑造信道以实现高频谱效率的热门技术。针对IRS辅助波束成形在系统建模与信道感知/估计方面的关键实际限制，我们提出了一种全新的、完全数据驱动的零阶随机梯度上升（ZoSGA）算法，用于通用的两阶段（即短/长期）、全无源IRS辅助随机效用最大化。ZoSGA通过最小的零阶强化，以严格无模型的方式联合学习长期最优IRS波束成形器与短期最优预编码器（例如基于WMMSE的），仅依赖于终端观测到的\textit{有效}复合信道，而与信道模型或网络/IRS配置无关。ZoSGA的另一显著特点是易于分析，这使我们能够在最小假设下建立阶为$\boldsymbol{O}(\sqrt{S}\epsilon^{-4})$的最先进（SOTA）收敛速率，其中$S$是IRS元素总数，$\epsilon$是期望的次优性目标。我们在标准MISO下行IRS辅助和速率最大化场景上的数值结果表明，ZoSGA同样具有SOTA经验性能，持续且显著优于标准的全基于模型的基线方法。最后，我们证明ZoSGA实际上可以\textit{在现场}运行，通过直接优化基于变容二极管的电磁IRS模型（ZoSGA未知）的电容，在多用户/IRS、计算密集的网络设置中，基本不引入计算开销或性能损失。