  Stacked intelligent metasurfaces (SIM) represents an advanced signal
processing paradigm that enables over-the-air processing of electromagnetic
waves at the speed of light. Its multi-layer structure exhibits customizable
increased computational capability 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 with low complexity transmit radio
frequency (RF) 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 solved via a customized deep reinforcement learning (DRL)
approach that continuously observes pre-designed states of the SIM-parametrized
smart wireless environment. The presented performance evaluation results
showcase the proposed method's capability to effectively learn from the
wireless environment while outperforming conventional precoding schemes under
low transmit power conditions. Finally, a whitening process is presented to
further augment the robustness of the proposed scheme.
