Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learning
This addresses modulation classification for wireless networks, but it appears incremental as it applies an existing deep learning method to a new context.
The paper tackles the problem of identifying modulation types in dynamic 5G networks using reconfigurable intelligent surfaces, achieving remarkable accuracy, especially at low SNR levels.
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.