  Due to significant blockage conditions in wireless networks, transmitted
signals may considerably degrade before reaching the receiver. The reliability
of the transmitted signals, therefore, may be critically problematic due to
blockages between the communicating nodes. Thanks to the ability of
Reconfigurable Intelligent Surfaces (RISs) to reflect the incident signals with
different reflection angles, this may counter the blockage effect by optimally
reflecting the transmit signals to receiving nodes, hence, improving the
wireless network's performance. With this motivation, this paper formulates a
RIS-aided wireless communication problem from a base station (BS) to a mobile
user equipment (UE). The BS is equipped with an RGB camera. We use the RGB
camera at the BS and the RIS panel to improve the system's performance while
considering signal propagating through multiple paths and the Doppler spread
for the mobile UE. First, the RGB camera is used to detect the presence of the
UE with no blockage. When unsuccessful, the RIS-assisted gain takes over and is
then used to detect if the UE is either "present but blocked" or "absent". The
problem is determined as a ternary classification problem with the goal of
maximizing the probability of UE communication blockage detection. We find the
optimal solution for the probability of predicting the blockage status for a
given RGB image and RIS-assisted data rate using a deep neural learning model.
We employ the residual network 18-layer neural network model to find this
optimal probability of blockage prediction. Extensive simulation results reveal
that our proposed RIS panel-assisted model enhances the accuracy of
maximization of the blockage prediction probability problem by over 38\%
compared to the baseline scheme.
