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Electronically tunable metasurfaces, or Intelligent Reflective Surfaces(IRSs), are a popular technology for achieving high spectral efficiency inmodern wireless systems by shaping channels using a multitude of tunablepassive reflective elements. Capitalizing on key practical limitations ofIRS-aided beamforming pertaining to system modeling and channelsensing/estimation, we propose a novel, fully data-driven Zeroth-orderStochastic 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 optimalprecoders (e.g., WMMSE-based) via minimal zeroth-order reinforcement and in astrictly model-free fashion, relying solely on the \textit{effective} compoundchannels observed at the terminals, while being independent of channel modelsor network/IRS configurations. Another remarkable feature of ZoSGA is beingamenable to analysis, enabling us to establish a state-of-the-art (SOTA)convergence rate of the order of $\boldsymbol{O}(\sqrt{S}\epsilon^{-4})$ underminimal assumptions, where $S$ is the total number of IRS elements, and$\epsilon$ is a desired suboptimality target. Our numerical results on astandard MISO downlink IRS-aided sumrate maximization setting establish SOTAempirical behavior of ZoSGA as well, consistently and substantiallyoutperforming standard fully model-based baselines. Lastly, we demonstrate thatZoSGA can in fact operate \textit{in the field}, by directly optimizing thecapacitances of a varactor-based electromagnetic IRS model (unknown to ZoSGA)on a multiple user/IRS, compute-heavy network setting, with essentially nocomputational overheads or performance degradation.

Model-Free Learning of Two-Stage Beamformers for Passive IRS-Aided Network Design