  Although Intelligent Reflective Surfaces (IRSs) are a cost-effective
technology promising high spectral efficiency in future wireless networks,
obtaining optimal IRS beamformers is a challenging problem with several
practical limitations. Assuming fully-passive, sensing-free IRS operation, we
introduce a new data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA)
algorithm for sumrate optimization in an IRS-aided downlink setting. ZoSGA does
not require access to channel model or network structure information, and
enables learning of optimal long-term IRS beamformers jointly with standard
short-term precoding, based only on conventional effective channel state
information. Supported by state-of-the-art (SOTA) convergence analysis,
detailed simulations confirm that ZoSGA exhibits SOTA empirical behavior as
well, consistently outperforming standard fully model-based baselines, in a
variety of scenarios.
