  Reconfigurable Intelligent Surfaces (RISs) comprised of tunable unit elements
have been recently considered in indoor communication environments for focusing
signal reflections to intended user locations. However, the current proofs of
concept require complex operations for the RIS configuration, which are mainly
realized via wired control connections. In this paper, we present a deep
learning method for efficient online wireless configuration of RISs when
deployed in indoor communication environments. According to the proposed
method, a database of coordinate fingerprints is implemented during an offline
training phase. This fingerprinting database is used to train the weights and
bias of a properly designed Deep Neural Network (DNN), whose role is to unveil
the mapping between the measured coordinate information at a user location and
the configuration of the RIS's unit cells that maximizes this user's received
signal strength. During the online phase of the presented method, the trained
DNN is fed with the measured position information at the target user to output
the optimal phase configurations of the RIS for signal power focusing on this
intended location. Our realistic simulation results using ray tracing on a
three dimensional indoor environment demonstrate that the proposed DNN-based
configuration method exhibits its merits for all considered cases, and
effectively increases the achievable throughput at the target user location.
