  The fifth generating (5G) of wireless networks will be more adaptive and
heterogeneous. Reconfigurable intelligent surface technology enables the 5G to
work on multistrand waveforms. However, in such a dynamic network, the
identification of specific modulation types is of paramount importance. We
present a RIS-assisted digital classification method based on artificial
intelligence. We train a convolutional neural network to classify digital
modulations. The proposed method operates and learns features directly on the
received signal without feature extraction. The features learned by the
convolutional neural network are presented and analyzed. Furthermore, the
robust features of the received signals at a specific SNR range are studied.
The accuracy of the proposed classification method is found to be remarkable,
particularly for low levels of SNR.
