Data-Driven Two-Stage IRS-Aided Sumrate Maximization with Inexact Precoding

We propose iZoSGA, a data-driven learning algorithm for joint passive long-term intelligent reflective surface (IRS)-aided beamforming and active short-term precoding in wireless networks. iZoSGA is based on a zeroth-order stochastic quasigradient ascent methodology designed for tackling two-stage nonconvex stochastic programs with continuous uncertainty and objective functions with "black-box" terms, and where second-stage optimization is inexact. As such, iZoSGA utilizes inexact precoding oracles, enabling practical implementation when short-term (e.g., WMMSE-based) beamforming is solved approximately. The proposed method is agnostic to channel models or statistics, and applies to arbitrary IRS/network configurations. We prove non-asymptotic convergence of iZoSGA to a neighborhood of a stationary solution of the original exact problem under minimal assumptions. Our numerics confirm the efficacy iZoSGA in several "inexact regimes", enabling passive yet fully effective IRS operation in diverse and realistic IRS-aided scenarios.