  Direct-to-satellite (DtS) communication has gained importance recently to
support globally connected Internet of things (IoT) networks. However,
relatively long distances of densely deployed satellite networks around the
Earth cause a high path loss. In addition, since high complexity operations
such as beamforming, tracking and equalization have to be performed in IoT
devices partially, both the hardware complexity and the need for high-capacity
batteries of IoT devices increase. The reconfigurable intelligent surfaces
(RISs) have the potential to increase the energy-efficiency and to perform
complex signal processing over the transmission environment instead of IoT
devices. But, RISs need the information of the cascaded channel in order to
change the phase of the incident signal. This study proposes graph attention
networks (GATs) for the challenging channel estimation problem and examines the
performance of DtS IoT networks for different RIS configurations under GAT
channel estimation.
