Reconfigurable intelligent surface (RIS) devices have emerged as an effective way to control the propagation channels for enhancing the end users' performance. However, RIS optimization involves configuring the radio frequency (RF) response of a large number of radiating elements, which is challenging in real-world applications due to high computational complexity. In this paper, a model-free cross-entropy (CE) algorithm is proposed to optimize the binary RIS configuration for improving the signal-to-noise ratio (SNR) at the receiver. One key advantage of the proposed method is that it only needs system performance parameters, e.g., the received SNR, without the need for channel models or channel estimation. Both simulations and experiments are conducted to evaluate the performance of the proposed CE algorithm. The results demonstrate that the CE algorithm outperforms benchmark algorithms, and shows stronger channel hardening with increasing numbers of RIS elements.

翻译：可重构智能表面（RIS）设备已成为控制传播信道以提升终端用户性能的有效手段。然而，RIS优化涉及配置大量辐射单元的射频响应，这在现实应用中因高计算复杂度而面临挑战。本文提出一种模型无关的交叉熵算法，用于优化二元RIS配置以提升接收端信噪比。该方法的关键优势在于仅需系统性能参数（如接收信噪比），而无须信道模型或信道估计。通过仿真与实验对所提CE算法的性能进行评估，结果表明该算法优于基准算法，并且随着RIS单元数量增加，信道硬化效应更加显著。