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Intelligent reflecting surface (IRS) has the potential to significantlyenhance the network performance by reconfiguring the wireless propagationenvironments. It is however difficult to obtain the accurate downlink channelstate information (CSI) for efficient beamforming design in IRS-aided wirelessnetworks. In this article, we consider an IRS-aided downlink multiple-inputsingle-output (MISO) network, where the base station (BS) is not required toknow the underlying channel distribution. We formulate an outage probabilityminimization problem by jointly optimizing the beamforming vector at the BS andthe phase-shift matrix at the IRS, while taking into account the transmit powerand unimodular constraints. The formulated problem turns out to be a non-convexnon-smooth stochastic optimization problem. To this end, we employ the sigmoidfunction as the surrogate to tackle the non-smoothness of the objectivefunction. In addition, we propose a data-driven efficient alternatingstochastic gradient descent (SGD) algorithm to solve the problem by utilizingthe historical channel samples. Simulation results demonstrate the performancegains of the proposed algorithm over the benchmark methods in terms ofminimizing the outage probability.

Outage Minimization for Intelligent Reflecting Surface Aided MISO Communication Systems via Stochastic Beamforming