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A fundamental challenge for millimeter wave (mmWave) communications lies inits sensitivity to the presence of blockages, which impact the connectivity ofthe communication links and ultimately the reliability of the entire network.In this paper, we analyze a reconfigurable intelligent surface (RIS)-aidedmmWave communication system for enhancing the network reliability andconnectivity in the presence of random blockages. To enhance the robustness ofhybrid analog-digital beamforming in the presence of random blockages, weformulate a stochastic optimization problem based on the minimization of thesum outage probability. To tackle the proposed optimization problem, weintroduce a low-complexity algorithm based on the stochastic block gradientdescent method, which learns sensible blockage patterns without searching forall combinations of potentially blocked links. Numerical results confirm theperformance benefits of the proposed algorithm in terms of outage probabilityand effective data rate.

Stochastic Learning-Based Robust Beamforming Design for RIS-Aided Millimeter-Wave Systems in the Presence of Random Blockages