  Software-defined metasurfaces (SDMs) comprise a dense topology of basic
elements called meta-atoms, exerting the highest degree of control over surface
currents among intelligent panel technologies. As such, they can transform
impinging electromagnetic (EM) waves in complex ways, modifying their
direction, power, frequency spectrum, polarity and phase. A well-defined
software interface allows for applying such functionalities to waves and
inter-networking SDMs, while abstracting the underlying physics. A network of
SDMs deployed over objects within an area, such as a floorplan walls, creates
programmable wireless environments (PWEs) with fully customizable propagation
of waves within them. This work studies the use of machine learning for
configuring such environments to the benefit of users within. The methodology
consists of modeling wireless propagation as a custom, interpretable,
back-propagating neural network, with SDM elements as nodes and their
cross-interactions as links. Following a training period the network learns the
propagation basics of SDMs and configures them to facilitate the communication
of users within their vicinity.
