  This paper presents a deep learning (DL) approach for estimating and
detecting symbols in signals transmitted through reconfigurable intelligent
surfaces (RIS). The proposed network utilizes fully connected layers to
estimate channels and phase angles from a reflected signal received through an
RIS. Because the proposed network can estimate and detect symbols without any
pilot signaling, this method reduces the overhead required for transmission.
The improvements achieved by this method are quantified in terms of the
bit-error rate, outperforming traditional detectors.
