Stacked intelligent metasurfaces (SIMs) represent a novel signal processing paradigm that enables over-the-air processing of electromagnetic waves at the speed of light. Their multi-layer architecture exhibits customizable computational capabilities compared to conventional single-layer reconfigurable intelligent surfaces and metasurface lenses. In this paper, we deploy SIM to improve the performance of multi-user multiple-input single-output (MISO) wireless systems through a low complexity manner with reduced numbers of transmit radio frequency chains. In particular, an optimization formulation for the joint design of the SIM phase shifts and the transmit power allocation is presented, which is efficiently tackled via a customized deep reinforcement learning (DRL) approach that systematically explores pre-designed states of the SIM-parametrized smart wireless environment. The presented performance evaluation results demonstrate the proposed method's capability to effectively learn from the wireless environment, while consistently outperforming conventional precoding schemes under low transmit power conditions. Furthermore, the implementation of hyperparameter tuning and whitening process significantly enhance the robustness of the proposed DRL framework.

翻译：堆叠智能超表面（SIM）代表了一种新颖的信号处理范式，能够以光速对电磁波进行空中处理。与传统单层可重构智能表面和超表面透镜相比，其多层架构展现出可定制的计算能力。本文通过部署SIM，以较低复杂度且减少发射射频链数量的方式，提升多用户多输入单输出（MISO）无线系统的性能。具体而言，本文提出了针对SIM相位偏移与发射功率分配的联合设计优化模型，并通过定制的深度强化学习（DRL）方法高效求解。该方法系统探索由SIM参数化的智能无线环境的预设状态。所呈现的性能评估结果表明，所提方法能够有效从无线环境中学习，且在低发射功率条件下持续优于传统预编码方案。此外，超参数调优与白化处理的实施显著增强了所提DRL框架的鲁棒性。