  To capture the communications gain of the massive radiating elements with low
power cost, the conventional reconfigurable intelligent surface (RIS) usually
works in passive mode. However, due to the cascaded channel structure and the
lack of signal processing ability, it is difficult for RIS to obtain the
individual channel state information and optimize the beamforming vector. In
this paper, we add signal processing units for a few antennas at RIS to
partially acquire the channels. To solve the crucial active antenna selection
problem, we construct an active antenna selection network that utilizes the
probabilistic sampling theory to select the optimal locations of these active
antennas. With this active antenna selection network, we further design two
deep learning (DL) based schemes, i.e., the channel extrapolation scheme and
the beam searching scheme, to enable the RIS communication system. The former
utilizes the selection network and a convolutional neural network to
extrapolate the full channels from the partial channels received by the active
RIS antennas, while the latter adopts a fully-connected neural network to
achieve the direct mapping between the partial channels and the optimal
beamforming vector with maximal transmission rate. Simulation results are
provided to demonstrate the effectiveness of the designed DL-based schemes.
