  Intelligent reflecting surfaces (IRSs), consisting of reconfigurable
metamaterials, have recently attracted attention as a promising cost-effective
technology that can bring new features to wireless communications. These
surfaces can be used to partially control the propagation environment and can
potentially provide a power gain that is proportional to the square of the
number of IRS elements when configured in a proper way. However, the
configuration of the local phase matrix at the IRSs can be quite a challenging
task since they are purposely designed to not have any active components,
therefore, they are not able to process any pilot signal. In addition, a large
number of elements at the IRS may create a huge training overhead. In this
paper, we present a deep learning (DL) approach for phase reconfiguration at an
IRS in order to learn and make use of the local propagation environment. The
proposed method uses the received pilot signals reflected through the IRS to
train the deep feedforward network. The performance of the proposed approach is
evaluated and the numerical results are presented.
