In this paper, a worst-user outage probability minimization problem in active reconfigurable intelligent surface (RIS)-aided systems is addressed. Since the resulting problem involves a discontinuous objective function over an unknown channel distribution, a sequence of reformulations is applied by integrating a probability-simplex user weighting, a softplus surrogate for differentiability, and entropic tail-risk aggregation for rare but severe outage events. Based on the reformulated problem, a data-driven stochastic optimization technique that relies on historical channel samples is adopted to jointly optimize the beamformer and reflection coefficients with projected adaptive moment estimation. Simulation results show advantages of the proposed technique for reliability-oriented active RIS transmission design.

翻译：暂无翻译